Papers by Sarthak Ahuja
Scalable and Robust Self-Learning for Skill Routing in Large-Scale Conversational AI Systems (2022.naacl-industry)
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| Challenge: | Existing methods to enable skill routing do not scale in terms of the number of skills and skill on-boarding. |
| Approach: | They propose a model-based approach to enable natural conversation by allowing frequent policy updates . they propose an annotation-based system, rule-based model, and bandit-based learning . |
| Outcome: | The proposed method is scalable and cost-effective, the authors show . they show that it can improve the user experience without abrupt policy changes . |
Improving Tool Retrieval by Leveraging Large Language Models for Query Generation (2025.coling-industry)
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| Challenge: | Large Language Models (LLMs) have shown great promise in common sense language understanding, conversational fluency, and reasoning. |
| Approach: | They propose to use Large Language Models to generate a retrieval query and embed it into the prompt to find relevant tools via a nearest-neighbor search. |
| Outcome: | The proposed method improves retrieval for in-domain (seen tools) and out-of-domain settings. |
DQA: Diagnostic Question Answering for IT Support (2026.acl-industry)
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Vishaal Kapoor, Mariam Dundua, Evren Yortucboylu, Sarthak Ahuja, Neda Kordjazi, Yiming Li, Vaibhavi padala, Derek Ho, Jennifer Whitted, Rebecca Steinert
| Challenge: | a diagnostic question-answering framework maintains persistent diagnostic state and aggregates retrieved cases at the level of root causes rather than individual documents. |
| Approach: | They propose a diagnostic question-answering framework that maintains persistent diagnostic state . it aggregates retrieved cases at the level of root causes rather than individual documents . |
| Outcome: | The framework achieves a 78.7% success rate under trajectory-level success criterion compared to a multi-turn RAG baseline . the framework reduces average turns from 8.4 to 3.9, compared with a single-turn baseline crim. |
Scalable and Safe Remediation of Defective Actions in Self-Learning Conversational Systems (2023.acl-industry)
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| Challenge: | Off-Policy reinforcement learning has been used to improve conversational AIs, but in large-scale commercial environments it is challenging to balance between policy improvements and experience continuity. |
| Approach: | They propose to curate and leverage regression incident reports to validate, safe-guard, and improve policies prior to the online deployment. |
| Outcome: | The proposed method validates, safe-guards, and improves policies prior to the online deployment. |