Papers by Harshvivek Kashid
HalluDetect: Detecting, Mitigating, and Benchmarking Hallucinations in Conversational Systems in the Legal Domain (2025.emnlp-industry)
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Spandan Anaokar, Shrey Ganatra, Swapnil Bhattacharyya, Harshvivek Kashid, Shruthi N Nair, Reshma Sekhar, Siddharth Manohar, Rahul Hemrajani, Pushpak Bhattacharyya
| Challenge: | Large Language Models (LLMs) are widely used in industry but still produce hallucinations, limiting their reliability in critical applications. |
| Approach: | They propose to reduce hallucinations in consumer grievance chatbots by reducing their token accuracy by 0.4159 per turn. |
| Outcome: | The proposed system achieves an F1 score of 68.92% outperforming baseline detectors by 22.47% while maintaining the highest token accuracy. |
From Recall to Creation: Generating Follow-Up Questions Using Bloom’s Taxonomy and Grice’s Maxims (2025.acl-industry)
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Archana Yadav, Harshvivek Kashid, Medchalimi Sruthi, B JayaPrakash, Chintalapalli Raja Kullayappa, Mandala Jagadeesh Reddy, Pushpak Bhattacharyya
| Challenge: | In-car AI assistants struggle with multi-turn conversations and fail to handle cognitively complex follow-up questions. |
| Approach: | They propose a framework that leverages Bloom's Taxonomy to generate follow-up questions with increasing cognitive complexity and a Gricean-inspired evaluation framework to assess their Logical Consistency, Informativeness, Relevance, and Clarity. |
| Outcome: | The proposed framework validates both LLM-based and human evaluations and identifies the specific cognitive complexity level at which in-car AI assistants begin to falter information. |