@inproceedings{khandelwal-bhardwaj-2026-prompteam,
title = "Prompteam at {UZH} Shared Task 2026: {RAG}-Augmented Classification and Cosine-Filtered Relation Prediction for {UN} Resolutions",
author = "Khandelwal, Siddhartha and
Bhardwaj, Jyotsana",
editor = "Elaraby, Mohamed and
Hautli-Janisz, Annette and
Romberg, Julia and
Musi, Elena and
Ruggeri, Federico and
Lawrence, John",
booktitle = "Proceedings of the 13th Workshop on Argument Mining and Reasoning",
month = jul,
year = "2026",
address = "San Diego, California, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.argmining-1.14/",
pages = "116--119",
ISBN = "979-8-89176-399-9",
abstract = "We describe our system for the UZH ArgMining 2026 Shared Task on reconstructing argumentative structure in UN/UNESCO resolutions. The task requires (1) classifying paragraph types and assigning thematic tags from a 141-label taxonomy, and (2) predicting directed argumentative relations between paragraphs. Our pipeline combines a quantised Qwen2.5-7B-Instruct model with retrieval-augmented generation (RAG) backed by FAISS-indexed dense embeddings for few-shot prompting and tag candidate pre-filtering. For relation prediction, we apply a sliding-window cosine pre-filter that reduces the quadratic pair space to near-linear cost. A parallelisable, fault-tolerant pipeline with atomic checkpointing enabled complete processing of 2,959 paragraphs across three concurrent Kaggle T4 sessions despite 12-hour GPU limits. Our system achieved 2nd place overall on the shared task leaderboard."
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%0 Conference Proceedings
%T Prompteam at UZH Shared Task 2026: RAG-Augmented Classification and Cosine-Filtered Relation Prediction for UN Resolutions
%A Khandelwal, Siddhartha
%A Bhardwaj, Jyotsana
%Y Elaraby, Mohamed
%Y Hautli-Janisz, Annette
%Y Romberg, Julia
%Y Musi, Elena
%Y Ruggeri, Federico
%Y Lawrence, John
%S Proceedings of the 13th Workshop on Argument Mining and Reasoning
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, USA
%@ 979-8-89176-399-9
%F khandelwal-bhardwaj-2026-prompteam
%X We describe our system for the UZH ArgMining 2026 Shared Task on reconstructing argumentative structure in UN/UNESCO resolutions. The task requires (1) classifying paragraph types and assigning thematic tags from a 141-label taxonomy, and (2) predicting directed argumentative relations between paragraphs. Our pipeline combines a quantised Qwen2.5-7B-Instruct model with retrieval-augmented generation (RAG) backed by FAISS-indexed dense embeddings for few-shot prompting and tag candidate pre-filtering. For relation prediction, we apply a sliding-window cosine pre-filter that reduces the quadratic pair space to near-linear cost. A parallelisable, fault-tolerant pipeline with atomic checkpointing enabled complete processing of 2,959 paragraphs across three concurrent Kaggle T4 sessions despite 12-hour GPU limits. Our system achieved 2nd place overall on the shared task leaderboard.
%U https://aclanthology.org/2026.argmining-1.14/
%P 116-119
Markdown (Informal)
[Prompteam at UZH Shared Task 2026: RAG-Augmented Classification and Cosine-Filtered Relation Prediction for UN Resolutions](https://aclanthology.org/2026.argmining-1.14/) (Khandelwal & Bhardwaj, ArgMining 2026)
ACL