SocREval: Large Language Models with the Socratic Method for Reference-Free Reasoning Evaluation

arXiv:2310.00074v2 Announce Type: replace
Abstract: To comprehensively gauge the capacity of current models for complex reasoning, it is crucial to assess their step-by-step reasoning in a scalable manner. Established reference-based evaluation metrics rely on human-annotated reasoning chains as references to assess the model-derived chains. However, such "gold-standard" human-written reasoning chains may not be unique and their acquisition is often labor-intensive. Existing reference-free reasoning evaluation metrics, while eliminating the need for human-crafted reasoning chains as references, often require fine-tuning with human-derived …

abstract arxiv capacity cs.ai cs.cl current evaluation evaluation metrics free however human language language models large language large language models metrics reasoning reference scalable standard step-by-step type