mlsanityData to 13 Oct 2026

DS@GT at Touch'e: Large Language Models for Retrieval-Augmented Debate

arXiv 2507.09090 ↗First posted 12 Jul 2025cs.IR cs.CLcited by 1

On the map: Retrieval-augmented generation → RAG robustness and adaptive retrieval

Abstract (arXiv)

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.

Key facts. Cited by 1 paper in the map (data through 13 Oct 2026). Placed in RAG robustness and adaptive retrieval, one of 15 directions in Retrieval-augmented generation. Builds on 4 of its 13 references that are in the map. First posted on arXiv 12 Jul 2025.

Builds on

4 of 13 references.

References · 4

  1. 2024Debating with More Persuasive LLMs Leads to More Truthful Answerscited by 261
  2. 2024Jasper and Stella: distillation of SOTA embedding modelscited by 111
  3. 2024An Empirical Analysis on Large Language Models in Debate Evaluationcited by 20
  4. 2023The Effect of Scaling, Retrieval Augmentation and Form on the Factual Consistency of Language Modelscited by 11

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Cited by year (2026 to 13 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026DS@GT ARC at Touch'e: Large Language Models for Retrieval-Augmented Debatecited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025DebateBench: A Challenging Long Context Reasoning Benchmark For Large Language Models
  2. 2025R-Debater: Retrieval-Augmented Debate Generation through Argumentative Memory
  3. 2024DebateQA: Evaluating Question Answering on Debatable Knowledge
  4. 2025Can LLMs Judge Debates? Evaluating Non-Linear Reasoning via Argumentation Theory Semantics
  5. 2024RAD-Bench: Evaluating Large Language Models Capabilities in Retrieval Augmented Dialogues
  6. 2026Evaluating the Capabilities of LLMs for Persuasive Dialogue
  7. 2026Argument Quality Assessment with Large Language Models: A Pairwise Bradley-Terry Approach
  8. 2026ArgBench: Benchmarking LLMs on Computational Argumentation Tasks