DEBATE : Devil’s Advocate-Based Assessment and Text Evaluation (2024)

Abstract: As natural language generation (NLG) models have become prevalent, systematically assessing the quality of machine-generated texts has become increasingly important. Recent studies introduce LLM-based evaluators that operate as reference-free metrics, demonstrating their capability to adeptly handle novel tasks. However, these models generally rely on a single-agent approach, which, we argue, introduces an inherent limit to their performance. This is because there exist biases in LLM agent’s responses, including preferences for certain text structure or content. In this work, we propose DEBATE, an NLG evaluation framework based on multi-agent scoring system augmented with a concept of Devil’s Advocate. Within the framework, one agent is instructed to criticize other agents’ arguments, potentially resolving the bias in LLM agent’s answers. DEBATE substantially outperforms the previous state-of-the-art methods in two meta-evaluation benchmarks in NLG evaluation, SummEval and TopicalChat. We also show that the extensiveness of debates among agents and the persona of an agent can influence the performance of evaluators.

## introduction as the quantity of machine-generated texts have increased dramatically due to recent advances in generative large language models (llms) @xcite @xcite , robust nlg evaluation has become important @xcite . improving the performance of nlg evaluation largely hinges on reliable and consistent automatic evaluators. traditional evaluation techniques like rouge @xcite and bleu @xcite perform assessments at the token level, having limitations in reflecting contextualized semantics of sentences. recent methods equipped * indicates equal contribution. with pre-trained language models incorporate contextual information in evaluation @xcite @xcite . however, most existing methods exhibit a relatively low correlation with human ratings, and rely heavily on costly human-annotated data. recent research has introduced llm-based methods for nlg evaluation @xcite @xcite . they usually require task-specific prompts and generate scores based on the probabilities of output tokens. on the other hand, these llm-based methods, by predominantly utilizing single-agent systems without a mechanism for critiquing or post-processing llm-generated responses, are particularly susceptible to implicit or induced biases @xcite . this limitation undermines both the stability and the effectiveness of their performance in evaluation tasks. in this paper, we introduce debate (devil's advocate-based assessment and textual evaluation), a simple yet effective multi-agent-based nlg evaluation framework that uses scores generated by llms. based on the structure of @xcite , we design a multi-agent scoring system where three agents, commander, scorer, and critic, debate with each other. the commander acts as the leader in debate while the scorer calculates scores in response to given tasks. the critic is given a role of devil's advocate and provides constructive criticism on the scorer's output, ensuring a more balanced evaluation process. we expect the framework to correct potential biases in nlg scoring and yield answers that align well with human-generated ones. we show that debating strategies commonly used in social sciences, such as devil's advocate, have significant potential in llm-based scoring systems. despite its straightforward concept, experimental results indicate that debate sets new state-of-the-art results on two nlg meta-evaluation benchmarks: summarization and dialogue generation. 2 method overview debate, an llm-based multi-agent nlg evaluator, adopts the experimental design from @xcite and includes three agents: commander (leader), scorer, and critic. as in figure 1 , debate has three primary components: (1) a predefined template that outlines the nlg task (e.g., summarization) and the evaluation aspect (e.g., coherence), (2) a multi-agent scoring framework that embeds a debate among agents, and (3) a prompt of devil's advocate to criticize the scorer. see appendix e for details. ## conclusion in this paper, we propose debate, the first stateof-the-art, multi-agent nlg evaluator with devil's advocate. we demonstrate the potential of incorporating debating strategies from social science into multi-agent scoring systems. we expect further exploration of the effect of diverse debating strategies in nlg evaluation.