LLM-INSTRUCT at UZH Shared Task 2026: Constraint-Aware Retrieval and Selective Debate for Paragraph-Level Argument Mining
LLM-INSTRUCT, the winning system for the UZH Shared Task at ArgMining 2026, focuses on paragraph-level argument mining in UN and UNESCO resolutions. It uses metadata-aware dense retrieval to narrow candidate tags, constrained decoding with per-dimension caps, and a three-agent debate branch for uncertain cases. It achieved 1st overall, 1st in F1, and 5th in LLM-as-a-Judge. Development improved Task 1b Micro-F1 from 35.83% to 40.08%. The key lesson: reducing decision space before generation improves accuracy and submission robustness.
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[Submitted on 10 May 2026]
Title:LLM-INSTRUCT at UZH Shared Task 2026: Constraint-Aware Retrieval and Selective Debate for Paragraph-Level Argument Mining
View a PDF of the paper titled LLM-INSTRUCT at UZH Shared Task 2026: Constraint-Aware Retrieval and Selective Debate for Paragraph-Level Argument Mining, by Phuong Huu Vu Tran and 3 other authors
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Abstract:We present LLM-INSTRUCT, the winning system for the UZH Shared Task at ArgMining 2026 on paragraph-level argument mining in UN and UNESCO resolutions. The task requires paragraph-type classification, prediction of a subset of 141 official tags, and directed relation prediction under a strict JSON schema setting using only open-weight models up to 8B parameters. We frame the task as constrained structured prediction. The system first narrows the candidate tag space with metadata-aware dense retrieval, then applies constrained decoding with per-dimension caps, escalates only uncertain cases to a three-agent debate branch, and finally validates the output schema. On the official leaderboard, LLM-INSTRUCT ranked 1st overall, with 1st in F1 and 5th in LLM-as-a-Judge. During development, our configuration search further improved Task 1b Micro-F1 from 35.83% to 40.08% while keeping the internal Task 2 score at 4.421. The main lesson is simple: reducing the decision space before generation improves both accuracy and submission robustness. Our code and supporting scripts are publicly available at: this https URL
Comments: Accepted to the 13th Workshop on Argument Mining (ArgMining 2026) at ACL 2026
Subjects:
Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
Cite as: arXiv:2607.20430 [cs.CL]
(or arXiv:2607.20430v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2607.20430
arXiv-issued DOI via DataCite
Submission history
From: Long Vo Minh [view email] [v1] Sun, 10 May 2026 05:08:59 UTC (129 KB)
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