Summary of Debate: Devil’s Advocate-based Assessment and Text Evaluation, by Alex Kim et al.
DEBATE: Devil’s Advocate-Based Assessment and Text Evaluation
by Alex Kim, Keonwoo Kim, Sangwon Yoon
First submitted to arxiv on: 16 May 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary In this research paper, the authors propose DEBATE, a novel framework for assessing the quality of machine-generated texts. The existing LLM-based evaluators have limitations due to biases in their responses, which can be resolved by introducing a multi-agent scoring system. The authors demonstrate that DEBATE outperforms previous state-of-the-art methods on two benchmarks, SummEval and TopicalChat. Additionally, the framework’s performance is influenced by the extent of debates among agents and the persona of an agent. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper introduces a new way to measure how good machine-generated texts are. The current methods have some problems because they are based on single “thinkers” that can be biased. The authors suggest using multiple thinkers that argue with each other, which helps to reduce these biases. This approach is shown to be better than previous methods for evaluating the quality of generated texts. |
