Papers by Anirudh Goyal
Reasoning Robustness of LLMs to Adversarial Typographical Errors (2024.emnlp-main)
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Esther Gan, Yiran Zhao, Liying Cheng, Mao Yancan, Anirudh Goyal, Kenji Kawaguchi, Min-Yen Kan, Michael Shieh
| Challenge: | Large Language Models (LLMs) have demonstrated impressive capabilities in reasoning using Chain-of-Thought (CoT) prompting. |
| Approach: | They develop an algorithm that iteratively samples typos for words that are important to the query and selects the edit that is most likely to succeed in attacking. |
| Outcome: | The proposed algorithm detects typographical errors in large and closed-source LLMs and shows that they are robust to them. |
A Systematic Examination of Preference Learning through the Lens of Instruction-Following (2025.naacl-long)
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Joongwon Kim, Anirudh Goyal, Aston Zhang, Bo Xiong, Rui Hou, Melanie Kambadur, Dhruv Mahajan, Hannaneh Hajishirzi, Liang Tan
| Challenge: | a recent study has found that preference learning is a key tool for enhancing LLM training and alignment. |
| Approach: | They use a synthetic data generation pipeline to generate 48,000 unique instruction-following prompts with 23 verifiable constraints to obtain preference pairs. |
| Outcome: | The proposed pipeline generates 48,000 unique instruction-following prompts with 23 verifiable constraints that enable fine-grained and automated quality assessments of model responses. |
LitSearch: A Retrieval Benchmark for Scientific Literature Search (2024.emnlp-main)
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| Challenge: | Literature search questions pose significant challenges for modern retrieval systems . a lack of domain expertise and reasoning through lengthy papers is a challenge . |
| Approach: | They propose a retrieval benchmark for literature search queries using inline citations from papers and questions about recently published papers. |
| Outcome: | The proposed retrieval benchmarks outperform state-of-the-art retrieval models and reranking pipelines. |
Masking or Mitigating? Deconstructing the Impact of Query Rewriting on Retriever Biases in RAG (2026.findings-acl)
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| Challenge: | Query enhancement techniques are now standard in retrieval-augmented generation systems, yet their impact on these biases remains unexplored. |
| Approach: | They evaluate query enhancement techniques that improve retrieval quality . they find that simple rewriting reduces bias through increased score variance . no technique uniformly addresses all biases, and effects vary substantially across retrievers . |
| Outcome: | The proposed method achieves strongest aggregate reduction, but fails under adversarial conditions where multiple biases combine. |