We make the training library JaxFormer including checkpoints available as open source contribution. In addition, our model CodeGen (with up to 16B parameters trained on TPU-v4) outperforms OpenAI’s Codex on the HumanEval benchmark. Our findings show the emergence of conversational capabilities and the effectiveness of the proposed conversational program synthesis paradigm. To study the model behavior on conversational program synthesis, we develop a multi-turn programming benchmark (MTPB), where solving each problem requires multi-step synthesis via multi-turn conversation between the user and the model. With weak supervision in the data and the scaling up of data size and model size, conversational capacities emerge from the simple autoregressive language modeling. We train a family of large language models, called CodeGen, on natural language and programming language data. It treats program synthesis as a sequence prediction problem, in which the specification is expressed in natural language and the desired program is conditionally sampled. Our new approach casts the process of writing a specification and program as a multi-turn conversation between a user and a system. ![]() We propose a conversational program synthesis approach via large language models, which addresses the challenges of searching over a vast program space and user intent specification faced in prior approaches. Program synthesis strives to generate a computer program as a solution to a given problem specification. The information of existing papers or adding new work.Ī Conversational Paradigm for Program SynthesisĮrik Nijkamp, Bo Pang, Hiroaki Hayashi, Lifu Tu, Huan Wang, Yingbo Zhou, Silvio Savarese, Caiming Xiong Search across all paper titles, abstracts, authors by using the search field.
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