DS@GT at Touché: Large Language Models for Retrieval-Augmented Debate
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866908447570657280 |
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| author | Miyaguchi, Anthony Johnston, Conor Potdar, Aaryan |
| author_facet | Miyaguchi, Anthony Johnston, Conor Potdar, Aaryan |
| contents | Large Language Models (LLMs) demonstrate strong conversational abilities. In this Working Paper, we study them in the context of debating in two ways: their ability to perform in a structured debate along with a dataset of arguments to use and their ability to evaluate utterances throughout the debate. We deploy six leading publicly available models from three providers for the Retrieval-Augmented Debate and Evaluation. The evaluation is performed by measuring four key metrics: Quality, Quantity, Manner, and Relation. Throughout this task, we found that although LLMs perform well in debates when given related arguments, they tend to be verbose in responses yet consistent in evaluation. The accompanying source code for this paper is located at https://github.com/dsgt-arc/touche-2025-rad. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_09090 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | DS@GT at Touché: Large Language Models for Retrieval-Augmented Debate Miyaguchi, Anthony Johnston, Conor Potdar, Aaryan Information Retrieval Computation and Language Large Language Models (LLMs) demonstrate strong conversational abilities. In this Working Paper, we study them in the context of debating in two ways: their ability to perform in a structured debate along with a dataset of arguments to use and their ability to evaluate utterances throughout the debate. We deploy six leading publicly available models from three providers for the Retrieval-Augmented Debate and Evaluation. The evaluation is performed by measuring four key metrics: Quality, Quantity, Manner, and Relation. Throughout this task, we found that although LLMs perform well in debates when given related arguments, they tend to be verbose in responses yet consistent in evaluation. The accompanying source code for this paper is located at https://github.com/dsgt-arc/touche-2025-rad. |
| title | DS@GT at Touché: Large Language Models for Retrieval-Augmented Debate |
| topic | Information Retrieval Computation and Language |
| url | https://arxiv.org/abs/2507.09090 |