title: Instruction-tuning Aligns LLMs to the Human Brain
summary: Instruction-tuning is a widely adopted method of finetuning that enables
  large language models (LLMs) to generate output that more closely resembles
  human responses to natural language queries, in many cases leading to
  human-level performance on diverse testbeds. However, it remains unclear
  whether instruction-tuning truly makes LLMs more similar to how humans process
  language. We investigate the effect of instruction-tuning on LLM-human
  similarity in two ways: (1) brain alignment, the similarity of LLM internal
  representations to neural activity in the human language system, and (2)
  behavioral alignment, the similarity of LLM and human behavior on a reading
  task. We assess 25 vanilla and instruction-tuned LLMs across three datasets
  involving humans reading naturalistic stories and sentences. We discover that
  instruction-tuning generally enhances brain alignment by an average of 6%, but
  does not have a similar effect on behavioral alignment. To identify the factors
  underlying LLM-brain alignment, we compute correlations between the brain
  alignment of LLMs and various model properties, such as model size, various
  problem-solving abilities, and performance on tasks requiring world knowledge
  spanning various domains. Notably, we find a strong positive correlation
  between brain alignment and model size (r = 0.95), as well as performance on
  tasks requiring world knowledge (r = 0.81). Our results demonstrate that
  instruction-tuning LLMs improves both world knowledge representations and brain
  alignment, suggesting that mechanisms that encode world knowledge in LLMs also
  improve representational alignment to the human brain.
paper_authors._id: 656d33b60bbc114fe639bcc8
paper_authors.name: Khai Loong Aw
paper_authors.status: admin_assigned
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paper_authors.fullname: Khai Loong Aw
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paper_authors.name: Syrielle Montariol
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paper_authors.fullname: Montariol
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paper_authors.user: Syrielle
paper_authors._id: 656d33b60bbc114fe639bcca
paper_authors.name: Badr AlKhamissi
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paper_authors._id: 63ca94752ffd350b486354e1
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paper_authors.fullname: Badr AlKhamissi
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paper_authors.user: bkhmsi
paper_authors._id: 656d33b60bbc114fe639bccb
paper_authors.name: Martin Schrimpf
paper_authors.status: admin_assigned
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paper_authors._id: 62c23d7b99c6c7738a66989b
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paper_authors.fullname: Martin Schrimpf
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paper_authors.user: mschrimpf
paper_authors._id: 656d33b60bbc114fe639bccc
paper_authors.name: Antoine Bosselut
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-12-04T10:45:59.665Z
paper_authors._id: 654fb1c67490049d621516fb
paper_authors.avatarUrl: /avatars/0f88177da236b1bce4a4a58fd0246884.svg
paper_authors.fullname: Antoine Bosselut
paper_authors.type: user
paper_authors.user: atcbosselut
paper_title: Instruction-tuning Aligns LLMs to the Human Brain
paper_summary: Instruction-tuning is a widely adopted method of finetuning that enables
  large language models (LLMs) to generate output that more closely resembles
  human responses to natural language queries, in many cases leading to
  human-level performance on diverse testbeds. However, it remains unclear
  whether instruction-tuning truly makes LLMs more similar to how humans process
  language. We investigate the effect of instruction-tuning on LLM-human
  similarity in two ways: (1) brain alignment, the similarity of LLM internal
  representations to neural activity in the human language system, and (2)
  behavioral alignment, the similarity of LLM and human behavior on a reading
  task. We assess 25 vanilla and instruction-tuned LLMs across three datasets
  involving humans reading naturalistic stories and sentences. We discover that
  instruction-tuning generally enhances brain alignment by an average of 6%, but
  does not have a similar effect on behavioral alignment. To identify the factors
  underlying LLM-brain alignment, we compute correlations between the brain
  alignment of LLMs and various model properties, such as model size, various
  problem-solving abilities, and performance on tasks requiring world knowledge
  spanning various domains. Notably, we find a strong positive correlation
  between brain alignment and model size (r = 0.95), as well as performance on
  tasks requiring world knowledge (r = 0.81). Our results demonstrate that
  instruction-tuning LLMs improves both world knowledge representations and brain
  alignment, suggesting that mechanisms that encode world knowledge in LLMs also
  improve representational alignment to the human brain.
paper_ai_keywords: instruction-tuning large language models (LLMs) brain alignment behavioral alignment neural activity human language system reading task world knowledge model size problem-solving abilities