Papers by Avinash Baidya
The Behavior Gap: Evaluating Zero-shot LLM Agents in Complex Task-Oriented Dialogs (2025.findings-acl)
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| Challenge: | Recent studies show that LLM-based agents struggle to perform in zero-shot scenarios. |
| Approach: | They propose a framework to quantify the behavior gap between AI agents and human experts . they propose to examine discrepancies in dialog acts, tool usage, and knowledge utilization . |
| Outcome: | The proposed framework measures the behavior gap between AI agents and human experts on task-oriented dialogs. |
RIMRULE: Improving Tool-Using Language Agents via MDL-Guided Rule Learning (2026.acl-long)
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Xiang Gao, Yuguang Yao, Qi Zhang, Kaiwen Dong, Avinash Baidya, Ruocheng Guo, Hilaf Hasson, Kamalika Das
| Challenge: | Large language models (LLMs) struggle to use tools reliably in domain-specific settings. |
| Approach: | They propose a neuro-symbolic approach to adapt large language models to task-specific tools . they propose reusable rules that are distilled from failure traces and injected into the prompt . |
| Outcome: | Experiments show that the proposed approach outperforms prompting-based adaptation methods and complements finetuning. |