Inference to the Best Explanation in Large Language Models
<|reference_start|>Inference to the Best Explanation in Large Language Models: While Large Language Models (LLMs) have found success in real-world applications, their underlying explanatory process is still poorly understood. This paper proposes IBE-Eval, a framework inspired by philosophical accounts on Inference to the Best Explanation (IBE) to advance the interpretation and evaluation of LLMs' explanations. IBE-Eval estimates the plausibility of natural language explanations through a combination of explicit logical and linguistic features including: consistency, parsimony, coherence, and uncertainty. Extensive experiments are conducted on Causal Question Answering (CQA), where \textit{IBE-Eval} is tasked to select the most plausible causal explanation amongst competing ones generated by LLMs (i.e., GPT 3.5 and Llama 2). The experiments reveal that IBE-Eval can successfully identify the best explanation with up to 77\% accuracy ($\approx 27\%$ above random), improving upon a GPT 3.5-as-a-Judge baseline ($\approx+17\%$) while being intrinsically more efficient and interpretable. Additional analyses suggest that, despite model-specific variances, LLM-generated explanations tend to conform to IBE criteria and that IBE-Eval is significantly correlated with human judgment, opening up opportunities for future development of automated explanation verification tools.<|reference_end|>
@article{dalal2024inference,
    title={Inference to the Best Explanation in Large Language Models},
    author={Dhairya Dalal, Marco Valentino, Andr'e Freitas, and Paul Buitelaar},
    journal={arXiv preprint arXiv:2402.10767},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.10767},
    primaryClass={cs.CL cs.AI}
}