Papers by Avishek Anand
EXPLORA: Efficient Exemplar Subset Selection for Complex Reasoning (2024.emnlp-main)
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Kiran Purohit, Venktesh V, Raghuram Devalla, Krishna Yerragorla, Sourangshu Bhattacharya, Avishek Anand
| Challenge: | Recent advances in large language models (LLMs) have enabled in-context learning (ICL) a critical challenge in ICL is the selection of optimal exemplars . |
| Approach: | They propose an algorithm for static exemplar subset selection for reasoning tasks . they propose a method that estimates parameters without incorporating confidence information . |
| Outcome: | The proposed method significantly reduces the number of LLM calls to 11% of those required by state-of-the-art methods and achieves a substantial performance improvement of 12.24%. |
It’s High Time: A Survey of Temporal Question Answering (2026.acl-long)
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| Challenge: | Temporal Question Answering (TQA) is a research area that focuses on answering questions involving temporal constraints or context. |
| Approach: | They present a comprehensive overview of Temporal Question Answering (TQA) this research area focuses on answering questions involving temporal constraints or context . |
| Outcome: | The proposed frameworks are compared against a range of datasets, tasks, and approaches. |
Answer Quality Aware Aggregation for Extractive QA Crowdsourcing (2022.findings-emnlp)
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| Challenge: | Existing methods for creating extractive question answering datasets are crowdsourcing, but results are often inconsistent. |
| Approach: | They propose a method for aggregating answers from different crowd workers that takes into account the relations between the answer, question, and context passage. |
| Outcome: | The proposed method outperforms baselines by 16% on precision and effectively conduct answer aggregation for extractive question answering task. |
Test-time Corpus Feedback: From Retrieval to RAG (2026.findings-eacl)
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| Challenge: | Retrieval-augmented generation (RAG) pipelines treat retrieval and reasoning as isolated components, limiting performance on complex tasks. |
| Approach: | They propose to integrate large language models with retrieval to improve query quality . they also propose to use feedback to improve the query, retrieved context, or document pool . |
| Outcome: | The proposed methods bridge IR and NLP perspectives and highlight retrieval as a dynamic, learnable component of end-to-end RAG systems. |
SUNAR: Semantic Uncertainty based Neighborhood Aware Retrieval for Complex QA (2025.naacl-long)
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| Challenge: | Open-domain complex question-answering systems face challenges in retrieving and reasoning over information that addresses multifaceted queries. |
| Approach: | They propose a method that leverages large language models to guide a Neighborhood Aware Retrieval process. |
| Outcome: | The proposed approach outperforms retrieve-and-reason baselines on two complex QA datasets. |
A Study into Investigating Temporal Robustness of LLMs (2025.findings-acl)
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| Challenge: | Large Language Models (LLMs) are limited in their ability to process temporal information and perform tasks requiring temporal reasoning and factual knowledge. |
| Approach: | They propose to use eight time-sensitiverobustness tests to test the model's temporal robustness for user questions in the zero-shot setting. |
| Outcome: | The proposed tests improve the temporal QA performance by up to 55%. |
Think Right, Not More: Test-Time Scaling for Numerical Claim Verification (2025.findings-emnlp)
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| Challenge: | Fact-checking real-world claims requires multistep reasoning and numerical reasoning . large language models are unable to understand nuance of numerical aspects . |
| Approach: | They propose scaling test-time compute (TTS) for large language models to solve this problem . they train a verifier model to navigate the space of possible reasoning paths . |
| Outcome: | The proposed approach achieves 1.8x higher efficiency than standard TTS while delivering 18.8% performance improvement over single-shot verification methods. |