Papers by Siddarth Madala
Tackling Distractor Documents in Multi-Hop QA with Reinforcement and Curriculum Learning (2026.findings-eacl)
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| Challenge: | Existing work on retrieval-augmented generation systems has shown that retrievers exhibit imperfect recall and precision, limiting downstream performance. |
| Approach: | They propose a retrieval-augmented generation model that generates answers from larger sets of retrieved contexts. |
| Outcome: | The proposed model generates answers and cites relevant information from larger sets of retrieved contexts. |
Contextual Relevance and Adaptive Sampling for LLM-Based Document Reranking (2026.acl-long)
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| Challenge: | identifying relevant documents for Reasoning-intensive queries remains a challenge . large language models have shown strong performance in zero-shot document reranking . |
| Approach: | They propose a reranking algorithm that estimates contextual relevance by aggregating LLMs' relevance judgments across batches. |
| Outcome: | The proposed algorithm improves nDCG@10 over retrieval and reranking baselines by 15% and 6–21% respectively. |