Revolutionizing Model Interpretability: Introducing CC-SHAP for LLM Self-Consistency
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In a riveting episode of AI Coffee Break with Letitia, a groundbreaking self-consistency measure for assessing natural language explanations by LLMs is unveiled. The team, driven by a quest for faithful explanations that truly reflect a model's reasoning process, introduces the ingenious CC-SHAP score. This score, ranging from -1 to 1, meticulously evaluates the alignment between input contributions for answer prediction and explanation, setting a new standard in the field. Unlike traditional tests, CC-SHAP transcends binary verdicts, offering a continuous measure of self-consistency that exposes discrepancies in how answers and explanations utilize inputs.
With a bold approach that sidesteps the need for semantic evaluations or input edits, CC-SHAP emerges as a beacon of interpretability applicable even to models like GPT2 that resist input alterations. By providing insights into where answers and explanations diverge in input usage, this measure promises a deeper understanding of model behavior. The team's plan to subject 11 LLMs to the rigorous scrutiny of CC-SHAP and other tests from the literature sets the stage for a showdown of self-consistency evaluations, revealing stark differences in results among existing tests.
As the dust settles on the comparative consistency bank evaluation, it becomes evident that the landscape of self-consistency testing is rife with contradictions. Notably, GPT2's self-consistency swings between extremes, while base LLMs lag behind chat models in consistency. Surprisingly, no clear correlation emerges between model size and self-consistency, challenging conventional wisdom in the field. By redefining faithfulness tests as self-consistency tests, the team propels the conversation forward, offering a compelling new perspective on measuring model performance. With CC-SHAP leading the charge towards a more unified understanding of model behavior, the future of interpretability testing looks brighter than ever.
Image copyright Youtube
Image copyright Youtube
Image copyright Youtube
Image copyright Youtube
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