Related papers: Large Language Model Programs

Large Language Model Programs

URL: http://arxiv.org/abs/2305.05364v1
Date: Tue, 9 May 2023 11:55:36 GMT
Title: Large Language Model Programs
Authors: Imanol Schlag, Sainbayar Sukhbaatar, Asli Celikyilmaz, Wen-tau Yih, Jason Weston, J\"urgen Schmidhuber, Xian Li
Abstract summary: In recent years, large pre-trained language models (LLMs) have demonstrated the ability to follow instructions and perform novel tasks from a few examples. We extend this line of reasoning and present a method which further expands the capabilities of an LLM by embedding it within an algorithm or program. We obtain a 6.4% improvement over the chain of thought baseline through a more algorithmic approach without any finetuning.
Score: 74.31873455763275
License: http://creativecommons.org/licenses/by/4.0/
Abstract: In recent years, large pre-trained language models (LLMs) have demonstrated the ability to follow instructions and perform novel tasks from a few examples. The possibility to parameterise an LLM through such in-context examples widens their capability at a much lower cost than finetuning. We extend this line of reasoning and present a method which further expands the capabilities of an LLM by embedding it within an algorithm or program. To demonstrate the benefits of this approach, we present an illustrative example of evidence-supported question-answering. We obtain a 6.4\% improvement over the chain of thought baseline through a more algorithmic approach without any finetuning. Furthermore, we highlight recent work from this perspective and discuss the advantages and disadvantages in comparison to the standard approaches.

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