Papers by Reshma Sekhar
HalluDetect: Detecting, Mitigating, and Benchmarking Hallucinations in Conversational Systems in the Legal Domain (2025.emnlp-industry)
Copied to clipboard
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. |