In this paper published on 12/20/2023 titled ALMANACS: A Simulatability Benchmark for Language Model Explainability, Edmund Mills, Shiye Su, Stuart Russell, and Scott Emmons present ALMANACS, a language model explainability benchmark that scores explainability methods on simulatability.
How do we measure the efficacy of language model explainability methods? While many explainability methods have been developed, they are typically evaluated on bespoke tasks, preventing an apples-to-apples comparison. To help fill this gap, the authors of this paper present ALMANACS, a language model explainability benchmark. ALMANACS scores explainability methods on simulatability, i.e., how well the explanations improve behavior prediction on new inputs. The ALMANACS scenarios span twelve safety-relevant topics such as ethical reasoning and advanced AI behaviors; they have idiosyncratic premises to invoke model-specific behavior; and they have a train-test distributional shift to encourage faithful explanations. By using another language model to predict behavior based on the explanations, ALMANACS is a fully automated benchmark. The authors use ALMANACS to evaluate counterfactuals, rationalizations, attention, and Integrated Gradients explanations. Their results are sobering: when averaged across all topics, no explanation method outperforms the explanation-free control. The authors conclude that despite modest successes in prior work, developing an explanation method that aids simulatability in ALMANACS remains an open challenge.