Rationale-Inspired Natural Language Explanations with Commonsense

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Chain of Thought Prompting Elicits Reasoning in Large Language Models (2022, neutral): “That line of work typically focuses on natural language inference (Camburu et al., 2018; Yordanov et al., 2021; Bostrom et al., 2021), and produces explanations either simultaneously to or after the ﬁnal prediction (Narang et al., 2020; Majumder et al., 2021; Wiegreffe et al., 2021, 2022).”
Can language models learn from explanations in context? (2022, methodology): “For example, explanations have been used for training or tuning models for computer vision (Hendricks et al., 2016; Schramowski et al., 2020; Majumder et al., 2021; Mu et al., 2020), for mathematical problem solving by program induction (Ling et al., 2017), or relational reasoning and reinforcement…”
Reframing Human-AI Collaboration for Generating Free-Text Explanations (2021, neutral): “We hope our results can guide future work on free-text explanations via neural or neuro-symbolic systems (Brahman et al., 2021; Majumder et al., 2021; Saha et al., 2021).”
Few-Shot Out-of-Domain Transfer Learning of Natural Language Explanations (2021, methodology): “For ComVE, we also add a heuristic baseline (as in [Majumder et al., 2021]), given by selecting as an NLE the correct statement of the pair of statements.”