The study ‘Preference-Conditioned Language-Guided Abstraction’ proposes a new way to improve learning from demonstrations in robots. It addresses the problem of spurious feature correlations that often occur in such learning…
Continue readingThe article presents REVEAL, a new dataset for benchmarking automatic verifiers of complex reasoning chains in language models. This is useful in complex reasoning tasks that require step-by-step answers, known…
Continue readingThe study examines two problems in aligning language models to human preferences using reward models. Firstly, it questions if a certain monotone transformation of the reward model can preserve preference…
Continue readingThe paper introduces a new feature attribution (FA) method for generative language models called the Recursive Attribution Generator (ReAGent). Unlike existing FAs, which are mostly developed for encoder-only language models…
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