Instruction-tuning Aligns LLMs to the Human Brain
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.
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Khai Loong Aw
admin_assigned
2023-12-04T10:45:25.821Z
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Khai Loong Aw
user
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656d33b60bbc114fe639bcc9
Syrielle Montariol
admin_assigned
2023-12-04T10:45:35.553Z
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Montariol
user
Syrielle
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Badr AlKhamissi
admin_assigned
2023-12-04T10:45:44.223Z
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/avatars/2e41b2651d30b855f0b46cddaa02aa43.svg
Badr AlKhamissi
user
bkhmsi
656d33b60bbc114fe639bccb
Martin Schrimpf
admin_assigned
2023-12-04T10:45:53.586Z
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https://cdn-avatars.huggingface.co/v1/production/uploads/1656896923660-62c23d7b99c6c7738a66989b.jpeg
Martin Schrimpf
user
mschrimpf
656d33b60bbc114fe639bccc
Antoine Bosselut
admin_assigned
2023-12-04T10:45:59.665Z
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Antoine Bosselut
user
atcbosselut
Instruction-tuning Aligns LLMs to the Human Brain
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.
Instruction-tuning improves brain alignment in large language models but not behavioral alignment, with correlations found between brain alignment and model size and world knowledge performance.
instruction-tuning large language models brain alignment behavioral alignment neural activity world knowledge model size