Think Before You Speak: Using Self-talk to Generate Implicit Commonsense Knowledge for Response Generation

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Language Models that Seek for Knowledge: Modular Search & Generation for Dialogue and Prompt Completion (2022, background): “Language models (LMs) have recently been shown to generate more factual responses by employing modularity (Zhou et al., 2021) in combination with retrieval (Adolphs et al.”
Survey of Hallucination in Natural Language Generation (2022, background): “Recently, there have been some attempts to generate informative responses without explicit knowledge inputs, but with the help of the implicit knowledge inside large pre-trained language models instead [217, 239] during the inference time.”
Learning by Distilling Context (2022, background): “Many recent works show that language models perform better when it is required to generate a chain of reasoning steps before outputting the ﬁnal answer (Zhou et al., 2021; Nye et al., 2021b; Cobbe et al., 2021; Wei et al., 2022b; Zhou et al., 2022; Lewkowycz et al., 2022).”