LLM self-explanations for entity resolution are unstable and weakly faithful to causal evidence, but a hybrid framework using them as priors matches post-hoc quality at up to 10x lower cost.
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Proposes SCSuff metric for evaluating LLM explanation sufficiency via model-generated alternative inputs, showing explanations are typically insufficient and predictable from hidden states.
Data-similarity and data-influence produce significantly overlapping rankings of training documents for LLM outputs, with asymmetry allowing a favorable cost-accuracy trade-off.
A claimed mathematical proof establishes a quadrilemma showing that complex environments, high AI performance, interpretable explanations, and complete faithfulness cannot coexist.
BoolXLLM augments an existing Boolean rule learner with LLMs for feature selection, discretization thresholds, and natural-language rule translation to improve interpretability while preserving accuracy.
MLLMs achieve competitive but subhuman performance on the new VSI-Bench for visual-spatial intelligence from videos, with spatial reasoning as the main bottleneck and explicit cognitive map generation improving distance estimation.
Self-explanations from LLMs produce faithful token subsets for correct predictions but align with human rationales only conditionally on text length and task complexity, unlike post-hoc attribution methods that highlight structural tokens.
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