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

arXiv: https://arxiv.org/abs/2608.08143 · HTML: https://mlsanity.com/paper/2608.08143/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in RAG robustness and adaptive retrieval, one of 15 directions in Retrieval-augmented generation.
- Builds on 8 of its 12 references that are in the map.
- First posted on arXiv 8 Aug 2026.

## Placed in

- Retrieval-augmented generation / [RAG robustness and adaptive retrieval](https://mlsanity.com/rag/rag-robustness-and-adaptive-retrieval/)

## Builds on (most cited first, 8 of 8)

- [Debating with More Persuasive LLMs Leads to More Truthful Answers](https://mlsanity.com/paper/2402.06782/) (2024)
- [Jasper and Stella: distillation of SOTA embedding models](https://mlsanity.com/paper/2412.19048/) (2024)
- [Fast and Three-rious: Speeding Up Weak Supervision with Triplet Methods](https://mlsanity.com/paper/2002.11955/) (2020)
- [Shrinking the Generation-Verification Gap with Weak Verifiers](https://mlsanity.com/paper/2506.18203/) (2025)
- [An Empirical Analysis on Large Language Models in Debate Evaluation](https://mlsanity.com/paper/2406.00050/) (2024)
- [Learning From Revisions: Quality Assessment of Claims in Argumentation at Scale](https://mlsanity.com/paper/2101.10250/) (2021)
- [The Effect of Scaling, Retrieval Augmentation and Form on the Factual Consistency of Language Models](https://mlsanity.com/paper/2311.01307/) (2023)
- [DS@GT at Touch'e: Large Language Models for Retrieval-Augmented Debate](https://mlsanity.com/paper/2507.09090/) (2025)

## Abstract (arXiv)

We extend the DS@GT ARC working-note submission to the Touch'e 2025 Retrieval-Augmented Debate task. The task has two subtasks: generating the next utterance in a simulated debate, and evaluating debate responses according to the Gricean maxims of Quantity, Quality, Relation, and Manner. The DS@GT ARC submission consisted of six leading LLMs from three providers through a retrieval-augmented prompting pipeline. We summarize the results from the working paper and explore whether multi-LLM evaluator agreement is a reliable proxy for official evaluation performance. The analysis shows that frontier LLM systems are strong response generators, and as evaluators they agree strongly within model families. However this consensus does not reliably track the official evaluation target, with the largest gap on the Quality maxim. The accompanying source code for this paper is located at https://github.com/dsgt-arc/touche-2025-rad and https://github.com/dsgt-arc/touche-2025-rad-analysis.