DEBATE: Devil's Advocate-Based Assessment and Text Evaluation
This addresses the need for more reliable and unbiased evaluation of machine-generated texts, particularly for researchers and practitioners in NLG, though it is an incremental improvement over existing multi-agent approaches.
The paper tackles the problem of bias in single-agent LLM-based evaluators for natural language generation by proposing DEBATE, a multi-agent scoring system with a Devil's Advocate agent, which substantially outperforms previous state-of-the-art methods on SummEval and TopicalChat benchmarks.
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.