Papers by Dhruv Gupta
SACL: Understanding and Combating Textual Bias in Code Retrieval with Semantic-Augmented Reranking and Localization (2025.findings-emnlp)
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| Challenge: | a recent study shows that code retrievers exhibit a strong bias towards well-documented code . |
| Approach: | They propose a framework that augments textual information with semantic information to mask specific features while preserving code functionality. |
| Outcome: | The proposed framework enhances textual information and reduces bias by augmenting code or structural knowledge with semantic information. |
Decomposed Prompting Does Not Fix Knowledge Gaps, But Helps Models Say “I Don’t Know” (2026.findings-acl)
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| Challenge: | Large language models struggle to recognize their knowledge limits in closed-book question answering . disagreements between prompting regimes are indicative of potential errors, study finds . |
| Approach: | They evaluate three task-equivalent prompting regimes: Direct, Assistive, Incremental . disagreements between prompting régimes provide a precise signal of internal uncertainty . |
| Outcome: | The proposed decomposed prompting outperforms standard uncertainty baselines as an error detector. |