Papers by Shweta Garg
CodeScout: Contextual Problem Statement Enhancement for Software Agents (2026.findings-acl)
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Manan Suri, Xiangci Li, Mehdi Shojaie, Songyang Han, Chao-Chun Hsu, Shweta Garg, Aniket Anand Deshmukh, Varun Kumar
| Challenge: | Current AI-powered code assistance tools struggle with ambiguous problem statements . failures on such ambiguously requests are highly correlated with longer trajectories . |
| Approach: | They propose a contextual query refinement approach that transforms ambiguous user requests into comprehensive, actionable problem statements through lightweight pre-exploration of the target codebase. |
| Outcome: | Empirical results show that CodeScout improves resolution rates with 27 additional issues resolved compared to baseline method. |
CODESTRUCT: Code Agents over Structured Action Spaces (2026.acl-long)
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| Challenge: | LLM-based code agents treat repositories as unstructured text, fail to produce valid patches . authors propose a structure-aware interface that exposes a codebase as a programmable action space . |
| Approach: | They propose to reframe the codebase as a structured action space where agents operate on named AST entities rather than text spans. |
| Outcome: | Evaluated on six LLMs, the proposed framework improves Pass@1 accuracy by 1.2-5.0% and reduces token consumption by 12-38%. |
ChemNER: Fine-Grained Chemistry Named Entity Recognition with Ontology-Guided Distant Supervision (2021.emnlp-main)
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| Challenge: | Named entity recognition (NER) is a fundamental step in scientific literature analysis to build AI-driven systems for molecular discovery, synthetic strategy designing, and manufacturing. |
| Approach: | They propose an ontology-guided method for fine-grained named entity recognition (NER) it leverages the chemistry type ontologies to generate distant labels with flexible KB-matching . |
| Outcome: | The proposed method significantly outperforms the state-of-the-art methods with a .25 absolute F1 improvement. |
Improving Answer Selection and Answer Triggering using Hard Negatives (D19-1)
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| Challenge: | Existing approaches to answer selection and answer triggering have been proposed. |
| Approach: | They propose to use hard negatives with a siamese network and a suitable loss function for answer selection and answer triggering. |
| Outcome: | The proposed model improves on InsuranceQA, SelQA, and an internal QA dataset by 2.3 points over previous baselines. |
IPO: Your Language Model is Secretly a Preference Classifier (2025.acl-long)
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| Challenge: | Reinforcement learning from human feedback (RLHF) is the primary method for aligning large language models with human preferences, but it often incurs significant computational and financial costs due to its reliance on training external reward models or human-labeled preferences. |
| Approach: | They propose an alternative approach that leverages generative LLMs as preference classifiers to reduce the dependence on external reward models or human-labeled preferences. |
| Outcome: | The proposed approach reduces the dependence on external reward models or human-labeled preferences by using generative LLMs as preference classifiers. |