Code4Struct: Code Generation for Few-Shot Structured Prediction from Natural Language

By Xingyao Wang, Sha Li, Heng Ji. 2022

58 citations recorded by the source corpus.

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In Natural Language Processing

Citations in the corpus

PAL: Program-aided Language Models (2022, methodology): “2022), the results for LaMDA and UL2 are from Wang et al. (2022b), and the results for Minerva are from Lewkowycz et al. (2022).”
ViperGPT: Visual Inference via Python Execution for Reasoning (2023, methodology): “In the natural language processing domain, they have been aimed at using exter-nal tools [46, 40], or for structured reasoning using Codex [34, 54, 14, 10].”
Chameleon: Plug-and-Play Compositional Reasoning with Large Language Models (2023, methodology): “Most existing approaches are either limited to a small number of tools [33, 6, 47, 15, 37, 43] or rely on domain-specific tools [34, 52, 11, 51, 45], and thus are not easy to generalize to diverse queries.”
Logic-LM: Empowering Large Language Models with Symbolic Solvers for Faithful Logical Reasoning (2023, background): “…with access to external tools and resources, such as the information retriever (Nakano et al., 2021; Shi et al., 2023; Lazaridou et al., 2022), calculator (Cobbe et al., 2021), code interpreter (Wang et al., 2022), planner (Liu et al., 2023a), and other pre-trained models (Shen et al., 2023).”
Reasoning with Language Model Prompting: A Survey (2022, background): “, 2022), collaborating LMs and codes to tackle specific tasks has recently sprung up (Wang et al., 2022e; Cheng et al., 2022; Wu et al., 2022b).”
ToolQA: A Dataset for LLM Question Answering with External Tools (2023, methodology): “On the other hand, Program-of-Thought [6], PAL [13], MathPrompt [15], and Code4Struct [63] apply code-based tools to enhance LLMs’ abilities in question answering with a focus on tabular and math-related tasks.”