LLM-as-a-qualitative-judge: automating error analysis in natural language generation

TL;DR

This paper introduces a novel LLM-based evaluation method that generates structured qualitative reports on NLG system errors, aiding developers in system improvement with high agreement to human annotations.

Contribution

The work presents a new approach for qualitative error analysis in NLG using LLMs, including a clustering algorithm and evaluation strategy, with validation on multiple datasets.

Findings

LLM-as-a-qualitative-judge matches human annotations in 2/3 cases

It produces error reports similar to human annotators

Use of the method improves NLG system performance

Abstract

Prompting large language models (LLMs) to evaluate generated text, known as LLM-as-a-judge, has become a standard evaluation approach in natural language generation (NLG), but is primarily used as a quantitative tool, i.e. with numerical scores as main outputs. In this work, we propose LLM-as-a-qualitative-judge, an LLM-based evaluation approach with the main output being a structured report of common issue types in the NLG system outputs. Our approach is targeted at providing developers with meaningful insights on what improvements can be done to a given NLG system and consists of two main steps, namely open-ended per-instance issue analysis and clustering of the discovered issues using an intuitive cumulative algorithm. We also introduce a strategy for evaluating the proposed approach, coupled with ~300 annotations of issues in instances from 12 NLG datasets. Our results show that instance-specific issues output by LLM-as-a-qualitative-judge match those annotated by humans in 2/3 cases, and that LLM-as-a-qualitative-judge is capable of producing error type reports resembling the reports composed by human annotators. We also demonstrate in a case study how the use of LLM-as-a-qualitative-judge can substantially improve NLG systems performance. Our code and data are publicly available at https://github.com/tunde-ajayi/llm-as-a-qualitative-judge.