Computer Science > Computation and Language
[Submitted on 2 Jul 2022 (v1), last revised 2 Dec 2022 (this version, v2)]
Title:FRAME: Evaluating Rationale-Label Consistency Metrics for Free-Text Rationales
View PDFAbstract:Following how humans communicate, free-text rationales aim to use natural language to explain neural language model (LM) behavior. However, free-text rationales' unconstrained nature makes them prone to hallucination, so it is important to have metrics for free-text rationale quality. Existing free-text rationale metrics measure how consistent the rationale is with the LM's predicted label, but there is no protocol for assessing such metrics' reliability. Thus, we propose FRAME, a framework for evaluating rationale-label consistency (RLC) metrics for free-text rationales. FRAME is based on three axioms: (1) good metrics should yield highest scores for reference rationales, which maximize RLC by construction; (2) good metrics should be appropriately sensitive to semantic perturbation of rationales; and (3) good metrics should be robust to variation in the LM's task performance. Across three text classification datasets, we show that existing RLC metrics cannot satisfy all three FRAME axioms, since they are implemented via model pretraining which muddles the metric's signal. Then, we introduce a non-pretraining RLC metric that greatly outperforms baselines on (1) and (3), while performing competitively on (2). Finally, we discuss the limitations of using RLC to evaluate free-text rationales.
Submission history
From: Aaron Chan [view email][v1] Sat, 2 Jul 2022 09:25:29 UTC (1,824 KB)
[v2] Fri, 2 Dec 2022 20:24:20 UTC (1,571 KB)
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