publications | Avishek Anand

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2026

Test-time Corpus Feedback: From Retrieval to RAG

Mandeep Rathee, Venktesh V, Sean MacAvaney , and 1 more author

In Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics, EACL 2026

arXiv PDF
TempRetriever: Fusion-based Temporal Dense Passage Retrieval for Time-Sensitive Questions

Abdelrahman Abdallah, Bhawna Piryani, Jonas Wallat , and 2 more authors

In Proceedings of the Nineteenth ACM International Conference on Web Search and Data Mining, WSDM 2026, Boise, ID, USA, February 22-26, 2026

arXiv PDF
On Listwise Reranking for Corpus Feedback

Soyoung Yoon, Jongho Kim, Daeyong Kwon , and 2 more authors

In Proceedings of the Nineteenth ACM International Conference on Web Search and Data Mining, WSDM 2026, Boise, ID, USA, February 22-26, 2026

arXiv PDF

2025

Think Right, Not More: Test-Time Scaling for Numerical Claim Verification

Primakov Chungkham, Venktesh V, Vinay Setty , and 1 more author

In Findings of the Association for Computational Linguistics: EMNLP 2025, Suzhou, China, November 4-9, 2025

arXiv PDF
A Study into Investigating Temporal Robustness of LLMs

Jonas Wallat, Abdelrahman Abdallah, Adam Jatowt , and 1 more author

In Findings of the Association for Computational Linguistics, ACL 2025, Vienna, Austria, July 27 - August 1, 2025

arXiv PDF
Breaking the Lens of the Telescope: Online Relevance Estimation over Large Retrieval Sets

Mandeep Rathee, Venktesh V, Sean MacAvaney , and 1 more author

In Proceedings of the 48th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2025, Padua, Italy, July 13-18, 2025

arXiv PDF
Sample Efficient Demonstration Selection for In-Context Learning

Kiran Purohit, Venktesh V, Sourangshu Bhattacharya , and 1 more author

In Forty-second International Conference on Machine Learning, ICML 2025, Vancouver, BC, Canada, July 13-19, 2025

arXiv PDF
Correctness is not Faithfulness in Retrieval Augmented Generation Attributions

Jonas Wallat, Maria Heuss, Maarten Rijke , and 1 more author

In Proceedings of the 2025 International ACM SIGIR Conference on Innovative Concepts and Theories in Information Retrieval, ICTIR 2025, Padua, Italy, 18 July 2025

arXiv PDF
Conversational Agents: A Framework for Evaluation (CAFE) (Dagstuhl Perspectives Workshop 24352)

Christine Bauer, Li Chen, Nicola Ferro , and 19 more authors

Dagstuhl Manifestos, 2025

arXiv PDF
Model-free reinforcement learning with noisy actions for automated experimental control in optics

Lea Richtmann, Viktoria-Sophie Schmiesing, Dennis Wilken , and 5 more authors

Trans. Mach. Learn. Res., 2025

arXiv PDF
Recommender Systems for Sustainable Development through Responsible Nudging

Mehrdad Rostami, Alexander Felfernig, Wolfgang Wörndl , and 4 more authors

In Proceedings of the 34th ACM International Conference on Information and Knowledge Management, CIKM 2025, Seoul, Republic of Korea, November 10-14, 2025

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Overview of the CLEF-2025 CheckThat! Lab Task 3 on Fact-Checking Numerical Claims

Venktesh V, Vinay Setty, Avishek Anand , and 6 more authors

In Working Notes of the Conference and Labs of the Evaluation Forum, CLEF 2025, Madrid, Spain, 9-12 September 2025

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Guiding Retrieval Using LLM-Based Listwise Rankers

Mandeep Rathee, Sean MacAvaney, and Avishek Anand

In Advances in Information Retrieval - 47th European Conference on Information Retrieval, ECIR 2025, Lucca, Italy, April 6-10, 2025, Proceedings, Part I

arXiv PDF
Explainable Information Retrieval

Avishek Anand, Sourav Saha, and Venktesh V

In Advances in Information Retrieval - 47th European Conference on Information Retrieval, ECIR 2025, Lucca, Italy, April 6-10, 2025, Proceedings, Part V

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FlashCheck: Exploration of Efficient Evidence Retrieval for Fast Fact-Checking

Kevin Nanhekhan, Venktesh V, Erik Martin , and 3 more authors

In Advances in Information Retrieval - 47th European Conference on Information Retrieval, ECIR 2025, Lucca, Italy, April 6-10, 2025, Proceedings, Part IV

arXiv PDF
Evaluating List Construction and Temporal Understanding capabilities of Large Language Models

Alexandru Dumitru, Venktesh V, Adam Jatowt , and 1 more author

In Proceedings of the 2025 International ACM SIGIR Conference on Innovative Concepts and Theories in Information Retrieval, ICTIR 2025, Padua, Italy, 18 July 2025

arXiv PDF
Enhancing Offline Reinforcement Learning with Curriculum Learning-Based Trajectory Valuation

Amir Abolfazli, Zekun Song, Avishek Anand , and 1 more author

In Proceedings of the 24th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2025, Detroit, MI, USA, May 19-23, 2025

arXiv PDF
SUNAR: Semantic Uncertainty based Neighborhood Aware Retrieval for Complex QA

Venktesh V, Mandeep Rathee, and Avishek Anand

In Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL 2025 - Volume 1: Long Papers, Albuquerque, New Mexico, USA, April 29 - May 4, 2025

arXiv PDF
RankingSHAP - Faithful Listwise Feature Attribution Explanations for Ranking Models

Maria Heuss, Maarten Rijke, and Avishek Anand

In Proceedings of the 48th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2025, Padua, Italy, July 13-18, 2025

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ir_explain: A Python Library of Explainable IR Methods

Sourav Saha, Harsh Agarwal, Venktesh V , and 4 more authors

In Proceedings of the 48th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2025, Padua, Italy, July 13-18, 2025

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Workshop on Explainability in Information Retrieval

Maria Heuss, Catherine Chen, Avishek Anand , and 2 more authors

In Proceedings of the 48th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2025, Padua, Italy, July 13-18, 2025

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Quam: Adaptive Retrieval through Query Affinity Modelling

Mandeep Rathee, Sean MacAvaney, and Avishek Anand

In Proceedings of the Eighteenth ACM International Conference on Web Search and Data Mining, WSDM 2025, Hannover, Germany, March 10-14, 2025

arXiv PDF
It’s High Time: A Survey of Temporal Information Retrieval and Question Answering

Bhawna Piryani, Abdelrahman Abdallah, Jamshid Mozafari , and 2 more authors

CoRR, 2025

arXiv PDF
Trust but Verify! A Survey on Verification Design for Test-time Scaling

Venktesh V, Mandeep Rathee, and Avishek Anand

CoRR, 2025

arXiv PDF
A Benchmark for Open-Domain Numerical Fact-Checking Enhanced by Claim Decomposition

Venktesh V, Deepali Prabhu, and Avishek Anand

CoRR, 2025

arXiv PDF

2024

Local Feature Selection without Label or Feature Leakage for Interpretable Machine Learning Predictions

Harrie Oosterhuis, Lijun Lyu, and Avishek Anand

In Forty-first International Conference on Machine Learning, ICML 2024, Vienna, Austria, July 21-27, 2024

arXiv PDF
Efficient Neural Ranking Using Forward Indexes and Lightweight Encoders

Jurek Leonhardt, Henrik Müller, Koustav Rudra , and 3 more authors

ACM Transactions of Information Systems (TOIS), 2024

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Dual-encoder-based dense retrieval models have become the standard in IR. They employ large Transformer-based language models, which are notoriously inefficient in terms of resources and latency. We propose Fast-Forward indexes – vector forward indexes which exploit the semantic matching capabilities of dual-encoder models for efficient and effective re-ranking. Our framework enables re-ranking at very high retrieval depths and combines the merits of both lexical and semantic matching via score interpolation. Furthermore, in order to mitigate the limitations of dual-encoders, we tackle two main challenges: Firstly, we improve computational efficiency by either pre-computing representations, avoiding unnecessary computations altogether, or reducing the complexity of encoders. This allows us to considerably improve ranking efficiency and latency. Secondly, we optimize the memory footprint and maintenance cost of indexes; we propose two complementary techniques to reduce the index size and show that, by dynamically dropping irrelevant document tokens, the index maintenance efficiency can be improved substantially. We perform evaluation to show the effectiveness and efficiency of Fast-Forward indexes – our method has low latency and achieves competitive results without the need for hardware acceleration, such as GPUs.
Data Augmentation for Sample Efficient and Robust Document Ranking

Abhijit Anand, Jurek Leonhardt, Jaspreet Singh , and 2 more authors

ACM Transactions of Information Systems (TOIS), 2024

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Contextual ranking models have delivered impressive performance improvements over classical models in the document ranking task. However, these highly over-parameterized models tend to be data-hungry and require large amounts of data even for fine-tuning. In this paper, we propose data-augmentation methods for effective and robust ranking performance. One of the key benefits of using data augmentation is in achieving sample efficiency or learning effectively when we have only a small amount of training data. We propose supervised and unsupervised data augmentation schemes by creating training data using parts of the relevant documents in the query-document pairs. We then adapt a family of contrastive losses for the document ranking task that can exploit the augmented data to learn an effective ranking model. Our extensive experiments on subsets of the MS MARCO and TREC-DL test sets show that data augmentation, along with the ranking-adapted contrastive losses, results in performance improvements under most dataset sizes. Apart from sample efficiency, we conclusively show that data augmentation results in robust models when transferred to out-of-domain benchmarks. Our performance improvements in in-domain and more prominently in out-of-domain benchmarks show that augmentation regularizes the ranking model and improves its robustness and generalization capability.
Is Interpretable Machine Learning Effective at Feature Selection for Neural Learning-to-Rank?

Lijun Lyu, Nirmal Roy, Harrie Oosterhuis , and 1 more author

In Advances in Information Retrieval - 46th European Conference on Information Retrieval, ECIR 2024, Glasgow, UK, March 24-28, 2024, Proceedings, Part IV

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Neural ranking models have become increasingly popular for real-world search and recommendation systems in recent years. Unlike their tree-based counterparts, neural models are much less interpretable. That is, it is very difficult to understand their inner workings and answer questions like how do they make their ranking decisions? or what document features do they find important? This is particularly disadvantageous since interpretability is highly important for real-world systems. In this work, we explore feature selection for neural learning-to-rank (LTR). In particular, we investigate six widely-used methods from the field of interpretable machine learning (ML) and introduce our own modification, to select the input features that are most important to the ranking behavior. To understand whether these methods are useful for practitioners, we further study whether they contribute to efficiency enhancement. Our experimental results reveal a large feature redundancy in several LTR benchmarks: the local selection method TabNet can achieve optimal ranking performance with less than 10 features; the global methods, particularly our G-L2X, require slightly more selected features, but exhibit higher potential in improving efficiency. We hope that our analysis of these feature selection methods will bring the fields of interpretable ML and LTR closer together.
Temporal Blind Spots in Large Language Models

Jonas Wallat, Adam Jatowt, and Avishek Anand

In Proceedings of the 17th ACM International Conference on Web Search and Data Mining, WSDM 2024, Merida, Mexico, March 4-8, 2024

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Large language models (LLMs) have recently gained significant attention due to their unparalleled ability to perform various natural language processing tasks. These models, benefiting from their advanced natural language understanding capabilities, have demonstrated impressive zero-shot performance. However, the pre-training data utilized in LLMs is often confined to a specific corpus, resulting in inherent freshness and temporal scope limitations. Consequently, this raises concerns regarding the effectiveness of LLMs for tasks involving temporal intents. In this study, we aim to investigate the underlying limitations of general-purpose LLMs when deployed for tasks that require a temporal understanding. We pay particular attention to handling factual temporal knowledge through three popular temporal QA datasets. Specifically, we observe low performance on detailed questions about the past and, surprisingly, for rather new information. In manual and automatic testing, we find multiple temporal errors and characterize the conditions under which QA performance deteriorates. Our analysis contributes to understanding LLM limitations and offers valuable insights into developing future models that can better cater to the demands of temporally-oriented tasks.
RankingSHAP - Listwise Feature Attribution Explanations for Ranking Models

Maria Heuss, Maarten Rijke, and Avishek Anand

arXiV, 2024

arXiv PDF
DINE: Dimensional Interpretability of Node Embeddings

Simone Piaggesi, Megha Khosla, André Panisson , and 1 more author

IEEE Trans. Knowl. Data Eng., 2024

arXiv PDF
Causal Probing for Dual Encoders

Jonas Wallat, Hauke Hinrichs, and Avishek Anand

In Proceedings of the 33rd ACM International Conference on Information and Knowledge Management, CIKM 2024, Boise, ID, USA, October 21-25, 2024

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Understanding the User: An Intent-Based Ranking Dataset

Abhijit Anand, Jurek Leonhardt, Venktesh V , and 1 more author

In Proceedings of the 33rd ACM International Conference on Information and Knowledge Management, CIKM 2024, Boise, ID, USA, October 21-25, 2024

arXiv PDF
EXPLORA: Efficient Exemplar Subset Selection for Complex Reasoning

Kiran Purohit, Venktesh V, Raghuram Devalla , and 3 more authors

In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024, Miami, FL, USA, November 12-16, 2024

arXiv PDF
DISCO: DISCovering Overfittings as Causal Rules for Text Classification Models

Zijian Zhang, Vinay Setty, Yumeng Wang , and 1 more author

In MAI-XAI@ECAI

arXiv PDF
QuanTemp: A real-world open-domain benchmark for fact-checking numerical claims

Venktesh V, Abhijit Anand, Avishek Anand , and 1 more author

In Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2024, Washington DC, USA, July 14-18, 2024

arXiv PDF
The Surprising Effectiveness of Rankers trained on Expanded Queries

Abhijit Anand, Venktesh V, Vinay Setty , and 1 more author

In Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2024, Washington DC, USA, July 14-18, 2024

arXiv PDF
DEXTER: A Benchmark for open-domain Complex Question Answering using LLMs

Venktesh V, Deepali Prabhu, and Avishek Anand

CoRR, 2024

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2023

An in-depth analysis of passage-level label transfer for contextual document ranking

Koustav Rudra, Zeon Trevor Fernando, and Avishek Anand

Information Retrieval Journmal, 2023

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Pre-trained contextual language models such as BERT, GPT, and XLnet work quite well for document retrieval tasks. Such models are fine-tuned based on the query-document/query-passage level relevance labels to capture the ranking signals. However, the documents are longer than the passages and such document ranking models suffer from the token limitation (512) of BERT. Researchers proposed ranking strategies that either truncate the documents beyond the token limit or chunk the documents into units that can fit into the BERT. In the later case, the relevance labels are either directly transferred from the original query-document pair or learned through some external model. In this paper, we conduct a detailed study of the design decisions about splitting and label transfer on retrieval effectiveness and efficiency. We find that direct transfer of relevance labels from documents to passages introduces label noise that strongly affects retrieval effectiveness for large training datasets. We also find that query processing times are adversely affected by fine-grained splitting schemes. As a remedy, we propose a careful passage level labelling scheme using weak supervision that delivers improved performance (3–14% in terms of nDCG score) over most of the recently proposed models for ad-hoc retrieval while maintaining manageable computational complexity on four diverse document retrieval datasets.
Report on the 21st Dutch-Belgian Information Retrieval Workshop (DIR 2023)

Avishek Anand, Maria Soledad Pera, Maria Heuss , and 2 more authors

SIGIR Forum, 2023
Zorro: Valid, Sparse, and Stable Explanations in Graph Neural Networks

Thorben Funke, Megha Khosla, Mandeep Rathee , and 1 more author

IEEE Trans. Knowl. Data Eng., 2023

arXiv PDF
Extractive Explanations for Interpretable Text Ranking

Jurek Leonhardt, Koustav Rudra, and Avishek Anand

ACM Transactions of Information Systems (TOIS), 2023

Abs arXiv PDF

Neural document ranking models perform impressively well due to superior language understanding gained from pre-training tasks. However, due to their complexity and large number of parameters, these (typically transformer-based) models are often non-interpretable in that ranking decisions can not be clearly attributed to specific parts of the input documents. In this paper we propose ranking models that are inherently interpretable by generating explanations as a by-product of the prediction decision. We introduce the Select-and-Rank paradigm for document ranking, where we first output an explanation as a selected subset of sentences in a document. Thereafter, we solely use the explanation or selection to make the prediction, making explanations first-class citizens in the ranking process. Technically, we treat sentence selection as a latent variable trained jointly with the ranker from the final output. To that end, we propose an end-to-end training technique for Select-and-Rank models utilizing reparameterizable subset sampling using the Gumbel-max trick. We conduct extensive experiments to demonstrate that our approach is competitive to state-of-the-art methods. Our approach is broadly applicable to numerous ranking tasks and furthers the goal of building models that are interpretable by design. Finally, we present real-world applications that benefit from our sentence selection method.
Probing BERT for Ranking Abilities

Jonas Wallat, Fabian Beringer, Abhijit Anand , and 1 more author

In Advances in Information Retrieval - 45th European Conference on Information Retrieval, ECIR 2023, Dublin, Ireland, April 2-6, 2023, Proceedings, Part II

Abs PDF Code

Contextual models like BERT are highly effective in numerous text-ranking tasks. However, it is still unclear as to whether contextual models understand well-established notions of relevance that are central to IR. In this paper, we use probing, a recent approach used to analyze language models, to investigate the ranking abilities of BERT-based rankers. Most of the probing literature has focussed on linguistic and knowledge-aware capabilities of models or axiomatic analysis of ranking models. In this paper, we fill an important gap in the information retrieval literature by conducting a layer-wise probing analysis using four probes based on lexical matching, semantic similarity as well as linguistic properties like coreference resolution and named entity recognition. Our experiments show an interesting trend that BERT-rankers better encode ranking abilities at intermediate layers. Based on our observations, we train a ranking model by augmenting the ranking data with the probe data to show initial yet consistent performance improvements.
Listwise Explanations for Ranking Models Using Multiple Explainers

Lijun Lyu, and Avishek Anand

In Advances in Information Retrieval - 45th European Conference on Information Retrieval, ECIR 2023, Dublin, Ireland, April 2-6, 2023, Proceedings, Part I

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This paper proposes a novel approach towards better interpretability of a trained text-based ranking model in a post-hoc manner. A popular approach for post-hoc interpretability text ranking models are based on locally approximating the model behavior using a simple ranker. Since rankings have multiple relevance factors and are aggregations of predictions, existing approaches that use a single ranker might not be sufficient to approximate a complex model, resulting in low fidelity. In this paper, we overcome this problem by considering multiple simple rankers to better approximate the entire ranking list from a black-box ranking model. We pose the problem of local approximation as a GENERALIZED PREFERENCE COVERAGE (GPC) problem that incorporates multiple simple rankers towards the listwise explanation of ranking models. Our method MULTIPLEX uses a linear programming approach to judiciously extract the explanation terms, so that to explain the entire ranking list. We conduct extensive experiments on a variety of ranking models and report fidelity improvements of 37%–54% over existing competitors. We finally compare explanations in terms of multiple relevance factors and topic aspects to better understand the logic of ranking decisions, showcasing our explainers’ practical utility.
Explainability of Text Processing and Retrieval Methods

Avishek Anand, Sourav Saha, Procheta Sen , and 1 more author

In Proceedings of the 15th Annual Meeting of the Forum for Information Retrieval Evaluation, FIRE 2023, Panjim, India, December 15-18, 2023
A Deep Reinforcement Learning Approach to Configuration Sampling Problem

Amir Abolfazli, Jakob Spiegelberg, Gregory Palmer , and 1 more author

In IEEE International Conference on Data Mining, ICDM 2023, Shanghai, China, December 1-4, 2023

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Configurable software systems have become increasingly popular as they enable customized software variants. The main challenge in dealing with configuration problems is that the number of possible configurations grows exponentially as the number of features increases. Therefore, algorithms for testing customized software have to deal with the challenge of tractably finding potentially faulty configurations given exponentially large configurations. To overcome this problem, prior works focused on sampling strategies to significantly reduce the number of generated configurations, guaranteeing a high t-wise coverage. In this work, we address the configuration sampling problem by proposing a deep reinforcement learning (DRL) based sampler that efficiently finds the trade-off between exploration and exploitation, allowing for the efficient identification of a minimal subset of configurations that covers all t-wise feature interactions while minimizing redundancy. We also present the CS-Gym, an environment for the configuration sampling. We benchmark our results against heuristic-based sampling methods on eight different feature models of software product lines and show that our method outperforms all sampling methods in terms of sample size. Our findings indicate that the achieved improvement has major implications for cost reduction, as the reduction in sample size results in fewer configurations that need to be tested.
Explainable Information Retrieval

Avishek Anand, Procheta Sen, Sourav Saha , and 2 more authors

In

arXiv PDF
The Effect of Masking Strategies on Knowledge Retention by Language Models

Jonas Wallat, Tianyi Zhang, and Avishek Anand

arxiv, 2023

arXiv PDF
Query Understanding in the Age of Large Language Models

Avishek Anand, Venktesh V, Abhijit Anand , and 1 more author

arxiv, 2023

arXiv PDF
Context Aware Query Rewriting for Text Rankers using LLM

Abhijit Anand, Venktesh V, Vinay Setty , and 1 more author

CoRR, 2023

Abs PDF

Query rewriting refers to an established family of approaches that are applied to underspecified and ambiguous queries to overcome the vocabulary mismatch problem in document ranking. Queries are typically rewritten during query processing time for better query modelling for the downstream ranker. With the advent of large- language models (LLMs), there have been initial investigations into using generative approaches to generate pseudo documents to tackle this inherent vocabulary gap. In this work, we analyze the utility of LLMs for improved query rewriting for text ranking tasks. We find that there are two inherent limitations of using LLMs as query re-writers – concept drift when using only queries as prompts and large inference costs during query processing. We adopt a simple, yet surprisingly effective, approach called context aware query rewriting (CAR) to leverage the benefits of LLMs for query understanding. Firstly, we rewrite ambiguous training queries by context-aware prompting of LLMs, where we use only relevant documents as context. Unlike existing approaches, we use LLM- based query rewriting only during the training phase. Eventually, a ranker is fine-tuned on the rewritten queries instead of the original queries during training. In our extensive experiments, we find that fine-tuning a ranker using re-written queries offers a significant improvement of up to 33% on the passage ranking task and up to 28% on the document ranking task when compared to the baseline performance of using original queries.
In-Context Ability Transfer for Question Decomposition in Complex QA

Venktesh V, Sourangshu Bhattacharya, and Avishek Anand

arxiv, 2023

arXiv

2022

Explainable Information Retrieval: A Survey

Avishek Anand, Lijun Lyu, Maximilian Idahl , and 3 more authors

arxiv, 2022

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For What It’s Worth: Humans Overwrite Their Economic Self-interest to Avoid Bargaining With AI Systems

Alexander Erlei, Richeek Das, Lukas Meub , and 2 more authors

In CHI ’22: CHI Conference on Human Factors in Computing Systems, New Orleans, LA, USA, 29 April 2022 - 5 May 2022

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As algorithms are increasingly augmenting and substituting human decision-making, understanding how the introduction of computational agents changes the fundamentals of human behavior becomes vital. This pertains to not only users, but also those parties who face the consequences of an algorithmic decision. In a controlled experiment with 480 participants, we exploit an extended version of two-player ultimatum bargaining where responders choose to bargain with either another human, another human with an AI decision aid or an autonomous AI-system acting on behalf of a passive human proposer. Our results show strong responder preferences against the algorithm, as most responders opt for a human opponent and demand higher compensation to reach a contract with autonomous agents. To map these preferences to economic expectations, we elicit incentivized subject beliefs about their opponent’s behavior. The majority of responders maximize their expected value when this is line with approaching the human proposer. In contrast, responders predicting income maximization for the autonomous AI-system overwhelmingly override economic self-interest to avoid the algorithm.
Answer Quality Aware Aggregation for Extractive QA Crowdsourcing

Peide Zhu, Zhen Wang, Claudia Hauff , and 2 more authors

In Findings of the Association for Computational Linguistics: EMNLP 2022, Abu Dhabi, United Arab Emirates, December 7-11, 2022

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Quality control is essential for creating extractive question answering (EQA) datasets via crowdsourcing. Aggregation across answers, i.e. word spans within passages annotated, by different crowd workers is one major focus for ensuring its quality. However, crowd workers cannot reach a consensus on a considerable portion of questions. We introduce a simple yet effective answer aggregation method that takes into account the relations among the answer, question, and context passage. We evaluate answer quality from both the view of question answering model to determine how confident the QA model is about each answer and the view of the answer verification model to determine whether the answer is correct. Then we compute aggregation scores with each answer’s quality and its contextual embedding produced by pre-trained language models. The experiments on a large real crowdsourced EQA dataset show that our framework outperforms baselines by around 16% on precision and effectively conduct answer aggregation for extractive QA task.
Supervised Contrastive Learning Approach for Contextual Ranking

Abhijit Anand, Jurek Leonhardt, Koustav Rudra , and 1 more author

In ICTIR ’22: The 2022 ACM SIGIR International Conference on the Theory of Information Retrieval (ICTIR), Madrid, Spain, July 11 - 12, 2022

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BERT Rankers are Brittle: A Study using Adversarial Document Perturbations

Yumeng Wang, Lijun Lyu, and Avishek Anand

In ICTIR ’22: The 2022 ACM SIGIR International Conference on the Theory of Information Retrieval, Madrid, Spain, July 11 - 12, 2022

arXiv PDF
SparCAssist: A Model Risk Assessment Assistant Based on Sparse Generated Counterfactuals

Zijian Zhang, Vinay Setty, and Avishek Anand

In

arXiv PDF
Efficient Neural Ranking using Forward Indexes

Jurek Leonhardt, Koustav Rudra, Megha Khosla , and 2 more authors

In

arXiv
BAGEL: A Benchmark for Assessing Graph Neural Network Explanations

Mandeep Rathee, Thorben Funke, Avishek Anand , and 1 more author

arxiv, 2022

arXiv
Distribution-Aligned Fine-Tuning for Efficient Neural Retrieval

Jurek Leonhardt, Marcel Jahnke, and Avishek Anand

CoRR, 2022

arXiv

2021

Question Answering for the Curated Web: Tasks and Methods in QA over Knowledge Bases and Text Collections

Rishiraj Saha Roy, and Avishek Anand

2021
A Comparative Study for Unsupervised Network Representation Learning

Megha Khosla, Vinay Setty, and Avishek Anand

IEEE Trans. Knowl. Data Eng., 2021
FaxPlainAC: A Fact-Checking Tool Based on EXPLAINable Models with HumAn Correction in the Loop

Zijian Zhang, Koustav Rudra, and Avishek Anand

In CIKM ’21: The 30th ACM International Conference on Information and Knowledge Management, Virtual Event, Queensland, Australia, November 1 - 5, 2021

arXiv PDF
Towards Axiomatic Explanations for Neural Ranking Models

Michael Völske, Alexander Bondarenko, Maik Fröbe , and 4 more authors

In ICTIR ’21: The 2021 ACM SIGIR International Conference on the Theory of Information Retrieval, Virtual Event, Canada, July 11, 2021

arXiv PDF
Extracting per Query Valid Explanations for Blackbox Learning-to-Rank Models

Jaspreet Singh, Megha Khosla, Zhenye Wang , and 1 more author

In ICTIR ’21: The 2021 ACM SIGIR International Conference on the Theory of Information Retrieval, Virtual Event, Canada, July 11, 2021
Exploiting Sentence-Level Representations for Passage Ranking

Jurek Leonhardt, Fabian Beringer, and Avishek Anand

In Proceedings of the LWDA 2021 Workshops: FGWM, KDML, FGWI-BIA, and FGIR, Online, September 1-3, 2021

arXiv PDF
L3S at the TREC 2021 Deep Learning Track

Jurek Leonhardt, Avishek Anand, and Koustav Rudra

In Proceedings of the Thirtieth Text REtrieval Conference, TREC 2021, online, November 15-19, 2021
Explain and Predict, and then Predict Again

Zijian Zhang, Koustav Rudra, and Avishek Anand

In WSDM ’21, The Fourteenth ACM International Conference on Web Search and Data Mining, Virtual Event, Israel, March 8-12, 2021

arXiv PDF
Revisiting the Auction Algorithm for Weighted Bipartite Perfect Matchings

Megha Khosla, and Avishek Anand

CoRR, 2021
Towards Benchmarking the Utility of Explanations for Model Debugging

Maximilian Idahl, Lijun Lyu, Ujwal Gadiraju , and 1 more author

CoRR, 2021
Learnt Sparsification for Interpretable Graph Neural Networks

Mandeep Rathee, Zijian Zhang, Thorben Funke , and 2 more authors

CoRR, 2021
Fast Forward Indexes for Efficient Document Ranking

Jurek Leonhardt, Koustav Rudra, Megha Khosla , and 2 more authors

CoRR, 2021

arXiv

2020

Characterization and classification of semantic image-text relations

Christian Otto, Matthias Springstein, Avishek Anand , and 1 more author

International Journal Multimedia Information Retrieval, 2020
Dagstuhl seminar 19461 on conversational search: seminar goals and working group outcomes

Avishek Anand, Lawrence Cavedon, Matthias Hagen , and 3 more authors

SIGIR Forum, 2020
BERTnesia: Investigating the capture and forgetting of knowledge in BERT

Jaspreet Singh, Jonas Wallat, and Avishek Anand

In Proceedings of the Third BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP, BlackboxNLP@EMNLP 2020, Online, November 2020

arXiv PDF
Distant Supervision in BERT-based Adhoc Document Retrieval

Koustav Rudra, and Avishek Anand

In CIKM ’20: The 29th ACM International Conference on Information and Knowledge Management, Virtual Event, Ireland, October 19-23, 2020
Model agnostic interpretability of rankers via intent modelling

Jaspreet Singh, and Avishek Anand

In FAT* ’20: Conference on Fairness, Accountability, and Transparency, Barcelona, Spain, January 27-30, 2020
Impact of Algorithmic Decision Making on Human Behavior: Evidence from Ultimatum Bargaining

Alexander Erlei, Franck Awounang Nekdem, Lukas Meub , and 2 more authors

In Proceedings of the Eighth AAAI Conference on Human Computation and Crowdsourcing, HCOMP 2020, Hilversum, The Netherlands (virtual), October 25-29, 2020
Question Answering over Curated and Open Web Sources

Rishiraj Saha Roy, and Avishek Anand

In ICTIR ’20: The 2020 ACM SIGIR International Conference on the Theory of Information Retrieval, Virtual Event, Norway, September 14-17, 2020
Question Answering over Curated and Open Web Sources

Rishiraj Saha Roy, and Avishek Anand

In Proceedings of the 43rd International ACM SIGIR conference on research and development in Information Retrieval, SIGIR 2020, Virtual Event, China, July 25-30, 2020

arXiv PDF
Boilerplate Removal using a Neural Sequence Labeling Model

Jurek Leonhardt, Avishek Anand, and Megha Khosla

In Companion of The 2020 Web Conference 2020, Taipei, Taiwan, April 20-24, 2020

arXiv PDF
Valid Explanations for Learning to Rank Models

Jaspreet Singh, Megha Khosla, and Avishek Anand

CoRR, 2020
Conversational Search - A Report from Dagstuhl Seminar 19461

Avishek Anand, Lawrence Cavedon, Matthias Hagen , and 3 more authors

CoRR, 2020

2019

A Faster Algorithm for Cuckoo Insertion and Bipartite Matching in Large Graphs

Megha Khosla, and Avishek Anand

Algorithmica, 2019

arXiv PDF
Estimating PageRank deviations in crawled graphs

Helge Holzmann, Avishek Anand, and Megha Khosla

Appl. Netw. Sci., 2019
Dissonance Between Human and Machine Understanding

Zijian Zhang, Jaspreet Singh, Ujwal Gadiraju , and 1 more author

Proc. ACM Hum. Comput. Interact., 2019

arXiv PDF
Clarifying False Memories in Voice-based Search

Johannes Kiesel, Arefeh Bahrami, Benno Stein , and 2 more authors

In Proceedings of the 2019 Conference on Human Information Interaction and Retrieval, CHIIR 2019, Glasgow, Scotland, UK, March 10-14, 2019
Understanding, Categorizing and Predicting Semantic Image-Text Relations

Christian Otto, Matthias Springstein, Avishek Anand , and 1 more author

In Proceedings of the 2019 on International Conference on Multimedia Retrieval, ICMR 2019, Ottawa, ON, Canada, June 10-13, 2019

arXiv PDF
Finding Interpretable Concept Spaces in Node Embeddings Using Knowledge Bases

Maximilian Idahl, Megha Khosla, and Avishek Anand

In Machine Learning and Knowledge Discovery in Databases - International Workshops of ECML PKDD 2019, Würzburg, Germany, September 16-20, 2019, Proceedings, Part I

arXiv PDF
Node Representation Learning for Directed Graphs

Megha Khosla, Jurek Leonhardt, Wolfgang Nejdl , and 1 more author

In Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2019, Würzburg, Germany, September 16-20, 2019, Proceedings, Part I

arXiv PDF
A study on the Interpretability of Neural Retrieval Models using DeepSHAP

Zeon Trevor Fernando, Jaspreet Singh, and Avishek Anand

In Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2019, Paris, France, July 21-25, 2019

arXiv PDF
Asynchronous Training of Word Embeddings for Large Text Corpora

Avishek Anand, Megha Khosla, Jaspreet Singh , and 2 more authors

In Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining, WSDM 2019, Melbourne, VIC, Australia, February 11-15, 2019

arXiv PDF
EXS: Explainable Search Using Local Model Agnostic Interpretability

Jaspreet Singh, and Avishek Anand

In Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining, WSDM 2019, Melbourne, VIC, Australia, February 11-15, 2019

arXiv PDF
TableNet: An Approach for Determining Fine-grained Relations for Wikipedia Tables

Besnik Fetahu, Avishek Anand, and Maria Koutraki

In The World Wide Web Conference, WWW 2019, San Francisco, CA, USA, May 13-17, 2019

arXiv PDF
A Comprehensive Comparison of Unsupervised Network Representation Learning Methods

Megha Khosla, Avishek Anand, and Vinay Setty

CoRR, 2019
Conversational Search (Dagstuhl Seminar 19461)

Avishek Anand, Lawrence Cavedon, Hideo Joho , and 2 more authors

Dagstuhl Reports, 2019

2018

Delusive PageRank in Incomplete Graphs

Helge Holzmann, Avishek Anand, and Megha Khosla

In Complex Networks and Their Applications VII - Volume 1 Proceedings The 7th International Conference on Complex Networks and Their Applications COMPLEX NETWORKS 2018, Cambridge, UK, December 11-13, 2018
Effects of Algorithmic Decision-Making and Interpretability on Human Behavior: Experiments using Crowdsourcing

Avishek Anand, Kilian Bizer, Alexander Erlei , and 5 more authors

In Proceedings of the HCOMP 2018 Works in Progress and Demonstration Papers Track of the sixth AAAI Conference on Human Computation and Crowdsourcing (HCOMP 2018), Zurich, Switzerland, July 5-8, 2018
Toward Voice Query Clarification

Johannes Kiesel, Arefeh Bahrami, Benno Stein , and 2 more authors

In The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval, SIGIR 2018, Ann Arbor, MI, USA, July 08-12, 2018
User Fairness in Recommender Systems

Jurek Leonhardt, Avishek Anand, and Megha Khosla

In Companion of the The Web Conference 2018 on The Web Conference 2018, WWW 2018, Lyon , France, April 23-27, 2018

arXiv PDF
Posthoc Interpretability of Learning to Rank Models using Secondary Training Data

Jaspreet Singh, and Avishek Anand

CoRR, 2018
Interpreting search result rankings through intent modeling

Jaspreet Singh, and Avishek Anand

CoRR, 2018

2017

Designing Search Tasks for Archive Search

Jaspreet Singh, and Avishek Anand

In Proceedings of the 2017 Conference on Conference Human Information Interaction and Retrieval, CHIIR 2017, Oslo, Norway, March 7-11, 2017

arXiv PDF
Fine Grained Citation Span for References in Wikipedia

Besnik Fetahu, Katja Markert, and Avishek Anand

In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, EMNLP 2017, Copenhagen, Denmark, September 9-11, 2017

arXiv PDF
BioNex: A System For Biomedical News Event Exploration

Patrick Ernst, Arunav Mishra, Avishek Anand , and 1 more author

In Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval, Shinjuku, Tokyo, Japan, August 7-11, 2017
Exploring Web Archives Through Temporal Anchor Texts

Helge Holzmann, Wolfgang Nejdl, and Avishek Anand

In Proceedings of the 2017 ACM on Web Science Conference, WebSci 2017, Troy, NY, USA, June 25 - 28, 2017
Modeling Event Importance for Ranking Daily News Events

Vinay Setty, Abhijit Anand, Arunav Mishra , and 1 more author

In Proceedings of the Tenth ACM International Conference on Web Search and Data Mining, WSDM 2017, Cambridge, United Kingdom, February 6-10, 2017

2016

History by Diversity: Helping Historians search News Archives

Jaspreet Singh, Wolfgang Nejdl, and Avishek Anand

In Proceedings of the 2016 ACM Conference on Human Information Interaction and Retrieval, CHIIR 2016, Carrboro, North Carolina, USA, March 13-17, 2016

arXiv PDF
Finding News Citations for Wikipedia

Besnik Fetahu, Katja Markert, Wolfgang Nejdl , and 1 more author

In Proceedings of the 25th ACM International Conference on Information and Knowledge Management, CIKM 2016, Indianapolis, IN, USA, October 24-28, 2016

arXiv PDF
Discovering Entities with Just a Little Help from You

Jaspreet Singh, Johannes Hoffart, and Avishek Anand

In Proceedings of the 25th ACM International Conference on Information and Knowledge Management, CIKM 2016, Indianapolis, IN, USA, October 24-28, 2016

arXiv PDF
The Dawn of Today’s Popular Domains: A Study of the Archived German Web over 18 Years

Helge Holzmann, Wolfgang Nejdl, and Avishek Anand

In Proceedings of the 16th ACM/IEEE-CS on Joint Conference on Digital Libraries, JCDL 2016, Newark, NJ, USA, June 19 - 23, 2016

arXiv PDF
ArchiveSpark: Efficient Web Archive Access, Extraction and Derivation

Helge Holzmann, Vinay Goel, and Avishek Anand

In Proceedings of the 16th ACM/IEEE-CS on Joint Conference on Digital Libraries, JCDL 2016, Newark, NJ, USA, June 19 - 23, 2016

arXiv PDF
L3S at the NTCIR-12 Temporal Information Access (Temporalia-2) Task

Zeon Trevor Fernando, Jaspreet Singh, and Avishek Anand

In Proceedings of the 12th NTCIR Conference on Evaluation of Information Access Technologies, National Center of Sciences, Tokyo, Japan, June 7-10, 2016
On the Applicability of Delicious for Temporal Search on Web Archives

Helge Holzmann, Wolfgang Nejdl, and Avishek Anand

In Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval, SIGIR 2016, Pisa, Italy, July 17-21, 2016

arXiv PDF
Expedition: A Time-Aware Exploratory Search System Designed for Scholars

Jaspreet Singh, Wolfgang Nejdl, and Avishek Anand

In Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval, SIGIR 2016, Pisa, Italy, July 17-21, 2016

arXiv PDF
Temporal Information Retrieval

Nattiya Kanhabua, and Avishek Anand

In Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval, SIGIR 2016, Pisa, Italy, July 17-21, 2016
Exploring the past of the web: alexandria & archive-it hackathon

Avishek Anand, and Jefferson Bailey

In Proceedings of the 8th ACM Conference on Web Science, WebSci 2016, Hannover, Germany, May 22-25, 2016
The Knowledge Awakens: Keeping Knowledge Bases Fresh with Emerging Entities

Johannes Hoffart, Dragan Milchevski, Gerhard Weikum , and 2 more authors

In Proceedings of the 25th International Conference on World Wide Web, WWW 2016, Montreal, Canada, April 11-15, 2016, Companion Volume
Tempas: Temporal Archive Search Based on Tags

Helge Holzmann, and Avishek Anand

In Proceedings of the 25th International Conference on World Wide Web, WWW 2016, Montreal, Canada, April 11-15, 2016, Companion Volume

arXiv PDF

2015

Automated News Suggestions for Populating Wikipedia Entity Pages

Besnik Fetahu, Katja Markert, and Avishek Anand

In Proceedings of the 24th ACM International Conference on Information and Knowledge Management, CIKM 2015, Melbourne, VIC, Australia, October 19 - 23, 2015

arXiv PDF
Balancing Novelty and Salience: Adaptive Learning to Rank Entities for Timeline Summarization of High-impact Events

Tuan Tran, Claudia Niederée, Nattiya Kanhabua , and 2 more authors

In Proceedings of the 24th ACM International Conference on Information and Knowledge Management, CIKM 2015, Melbourne, VIC, Australia, October 19 - 23, 2015

arXiv PDF
The Sweet Spot between Inverted Indices and Metric-Space Indexing for Top-K-List Similarity Search

Evica Milchevski, Avishek Anand, and Sebastian Michel

In Proceedings of the 18th International Conference on Extending Database Technology, EDBT 2015, Brussels, Belgium, March 23-27, 2015
How much is Wikipedia Lagging Behind News?

Besnik Fetahu, Abhijit Anand, and Avishek Anand

In Proceedings of the ACM Web Science Conference, WebSci 2015, Oxford, United Kingdom, June 28 - July 1, 2015

arXiv PDF
Exploring Long Running News Stories using Wikipedia

Jaspreet Singh, Abhijit Anand, Vinay Setty , and 1 more author

In Proceedings of the ACM Web Science Conference, WebSci 2015, Oxford, United Kingdom, June 28 - July 1, 2015

2014

Phrase Query Optimization on Inverted Indexes

Avishek Anand, Ida Mele, Srikanta J. Bedathur , and 1 more author

In Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management, CIKM 2014, Shanghai, China, November 3-7, 2014

2013

Indexing methods for web archives

Avishek Anand

Saarland University , 2013
FERRARI: Flexible and efficient reachability range assignment for graph indexing

Stephan Seufert, Avishek Anand, Srikanta J. Bedathur , and 1 more author

In 29th IEEE International Conference on Data Engineering, ICDE 2013, Brisbane, Australia, April 8-12, 2013

2012

Index maintenance for time-travel text search

Avishek Anand, Srikanta J. Bedathur, Klaus Berberich , and 1 more author

In The 35th International ACM SIGIR conference on research and development in Information Retrieval, SIGIR ’12, Portland, OR, USA, August 12-16, 2012
High-Performance Reachability Query Processing under Index Size Restrictions

Stephan Seufert, Avishek Anand, Srikanta J. Bedathur , and 1 more author

CoRR, 2012

2011

Temporal index sharding for space-time efficiency in archive search

Avishek Anand, Srikanta J. Bedathur, Klaus Berberich , and 1 more author

In Proceeding of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2011, Beijing, China, July 25-29, 2011

2010

Efficient temporal keyword search over versioned text

Avishek Anand, Srikanta J. Bedathur, Klaus Berberich , and 1 more author

In Proceedings of the 19th ACM Conference on Information and Knowledge Management, CIKM 2010, Toronto, Ontario, Canada, October 26-30, 2010

2009

EverLast: a distributed architecture for preserving the web

Avishek Anand, Srikanta J. Bedathur, Klaus Berberich , and 2 more authors

In Proceedings of the 2009 Joint International Conference on Digital Libraries, JCDL 2009, Austin, TX, USA, June 15-19, 2009