Papers by Sambit Sahu
Lessons from the Field: An Adaptable Lifecycle Approach to Applied Dialogue Summarization (2026.eacl-industry)
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Kushal Chawla, Chenyang Zhu, Pengshan Cai, Sangwoo Cho, Scott Novotney, Ayushman Singh, Jonah Lewis, Keasha Safewright, Alfy Samuel, Erin Babinsky, Shi-Xiong Zhang, Sambit Sahu
| Challenge: | Summarization of multi-party dialogues is a critical capability in industry . but generating high-quality summaries in practice is challenging . prior work has focused on static datasets and benchmarks, a condition rare in practical scenarios . |
| Approach: | They present an agentic system to summarize multi-party interactions using static datasets. |
| Outcome: | The proposed system can summarize multi-party interactions using a set of complex requirements. |
Routing with Generated Data: Annotation-Free LLM Skill Estimation and Expert Selection (2026.acl-long)
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Tianyi Niu, Justin Chen, Genta Indra Winata, Shi-Xiong Zhang, Supriyo Chakraborty, Sambit Sahu, Yue Zhang, Elias Stengel-Eskin, Mohit Bansal
| Challenge: | Existing approaches typically assume access to ground-truth labeled data . Existing methods require a classifier to select models given an input . |
| Approach: | They propose a routing setting where routers are trained exclusively on generated queries and answers from LLMs. |
| Outcome: | The proposed router outperforms the best query-answer router by 4.6% absolute accuracy when trained on weak generator data. |