[ArgAssist: LLM-based Argument Synthesis for Insurance Disputes](https://aclanthology.org/2026.sigdial-1.9.pdf)
[Anubhav Sinha](/people/anubhav-sinha/unverified/),
[Nitin Ramrakhiyani](/people/nitin-ramrakhiyani/unverified/),
[Sachin Pawar](/people/sachin-pawar/),
[Isha Narang](/people/isha-narang/unverified/),
[Manoj Apte](/people/manoj-apte/)
Abstract
Access to timely and affordable dispute resolution is a major challenge for industries such as insurance, finance, and consumer goods, where legal disputes between suppliers and consumers are frequent and often complex. We present "ArgAssist", an LLM-based system for structured argumentative discourse generation that forms a core component of an alternative dispute resolution (ADR) platform that we are building. ArgAssist assists parties involved in a dispute (such as insurer and insured in an insurance dispute) by synthesizing discourse-structured legal arguments, represented as claims supported by typed premises grounded in case information. ArgAssist first generates "base" arguments using an LLM, which are then strengthened by grounding them in relevant prior cases and statutes to ensure legal soundness and contextual coherence. We introduce a novel evaluation metric for assessing the quality of generated arguments and demonstrate ArgAssist’s effectiveness on two real-world datasets in the insurance domain. Our results show that explicitly modeling argumentative discourse structure and grounding significantly improves alignment with expert-authored legal arguments.- Anthology ID:
- 2026.sigdial-1.9
- Volume:
[Proceedings of the 27th Annual Meeting of the Special Interest Group on Discourse and Dialogue](/volumes/2026.sigdial-1/)- Month:
- August
- Year:
- 2026
- Address:
- Atlanta, Georgia, USA
- Editors:
[Jinho D. Choi](/people/jinho-d-choi/),[Yun-Nung Chen](/people/yun-nung-chen/),[Kotaro Funakoshi](/people/kotaro-funakoshi/),[Ali Emami](/people/ali-emami/)- Venue:
[SIGDIAL](/venues/sigdial/)- SIG:
[SIGDIAL](/sigs/sigdial/)- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 124–138
- Language:
- URL:
[https://aclanthology.org/2026.sigdial-1.9/](https://aclanthology.org/2026.sigdial-1.9/)- DOI:
- Cite (ACL):
- Anubhav Sinha, Nitin Ramrakhiyani, Sachin Pawar, Isha Narang, and Manoj Apte. 2026. ArgAssist: LLM-based Argument Synthesis for Insurance Disputes. InProceedings of the 27th Annual Meeting of the Special Interest Group on Discourse and Dialogue, pages 124–138, Atlanta, Georgia, USA. Association for Computational Linguistics. - Cite (Informal):
[ArgAssist: LLM-based Argument Synthesis for Insurance Disputes](https://aclanthology.org/2026.sigdial-1.9/)(Sinha et al., SIGDIAL 2026)- PDF:
[https://aclanthology.org/2026.sigdial-1.9.pdf](https://aclanthology.org/2026.sigdial-1.9.pdf)