title: Large Language Model Programs
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.
  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.
paper_authors._id: 645af7fd7d8ec84b7b5e68b7
paper_authors.name: Imanol Schlag
paper_authors._id: 645af7fd7d8ec84b7b5e68b8
paper_authors.name: Sainbayar Sukhbaatar
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-07-30T16:01:39.688Z
paper_authors._id: 66a8611eb51510d82ed54231
paper_authors.avatarUrl: /avatars/ad559e774fee4914091b82c9831ae2a2.svg
paper_authors.fullname: Sainbayar Sukhbaatar
paper_authors.type: user
paper_authors.user: sainbar
paper_authors._id: 645af7fd7d8ec84b7b5e68b9
paper_authors.name: Asli Celikyilmaz
paper_authors._id: 645af7fd7d8ec84b7b5e68ba
paper_authors.name: Wen-tau Yih
paper_authors._id: 645af7fd7d8ec84b7b5e68bb
paper_authors.name: Jason Weston
paper_authors._id: 645af7fd7d8ec84b7b5e68bc
paper_authors.name: Jürgen Schmidhuber
paper_authors._id: 645af7fd7d8ec84b7b5e68bd
paper_authors.name: Xian Li
paper_title: Large Language Model Programs
paper_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.
  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.
paper_ai_keywords: large pre-trained language models (LLMs) pre-trained language models (LLMs) multimodal in-context examples parameterise fine-tuning evidence-supported question-answering chain of thought encoder