Inferring LISP Programs From Examples

By D. E. Shaw, William R. Swartout, C. Green. 1975

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Program Synthesis with Large Language Models (2021, background): “Program synthesis is a longstanding goal of artificial intelligence research [Manna and Waldinger, 1971, Waldinger et al., 1969, Summers, 1977, Shaw et al., 1975, Pnueli and Rosner, 1989, Manna and Waldinger, 1975], dating as far back as the 1940s and 50s [Copeland, 2012, Backus et al., 1957].”
The Inference of Regular LISP Programs from Examples (1978, background): “[12], Siklossy and Sykes [13], and Summers [14], [15].”
A large-scale benchmark for few-shot program induction and synthesis (2021, background): “Few-shot program induction and synthesis Program synthesis (Shaw et al., 1975; Solar-Lezama et al., 2006; Gulwani et al., 2017) aims at generating code that satisfies a set of input-output examples.”
Is Your Code Generated by ChatGPT Really Correct? Rigorous Evaluation of Large Language Models for Code Generation (2023, methodology): “program synthesis techniques have been developed, including deductive synthesis [18, 31, 53], programming by examples [19, 48] and neural-guided synthesis [25].”
Program Synthesis (2017, background): “[125] developed a framework for learning restricted Lisp programs from a single input-output example.”