Papers by Nitesh V. Chawla
Context Attribution with Multi-Armed Bandit Optimization (2026.findings-acl)
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| Challenge: | Existing approaches to augmenting attribution with retrieval-augmented generation (RAG) focus on training models to explicitly cite context segments during generation, but their reliability remains unverifiable. |
| Approach: | They propose a framework that formulates context attribution as a combinatorial multi-armed bandit problem by using Linear Thompson Sampling to efficiently identify the most influential context segments while minimizing the number of model queries. |
| Outcome: | The proposed method reduces model queries by 30% while matching or exceeding the attribution quality of existing approaches. |
Continuous Context Sampling Allows Extending Diversity Boundaries of Large Language Models (2026.acl-srw)
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| Challenge: | Large language models exhibit a persistent limitation: repeated generations from the same prompt tend to be semantically similar. |
| Approach: | They propose to construct a conditioning distribution from a small set of diverse anchor generations and use it to condition an LLM's generation distribution. |
| Outcome: | The proposed framework significantly expands the model's reachable semantic range by constructing a conditioning distribution from a small set of diverse anchor generations. |