Inference to the Best Explanation in Large Language Models
Dhairya Dalal Marco Valentino André Freitas Paul Buitelaar
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.