System 2 Attention (is something you might need too)
Soft attention in Transformer-based Large Language Models (LLMs) is
susceptible to incorporating irrelevant information from the context into its
latent representations, which adversely affects next token generations. To help
rectify these issues, we introduce System 2 Attention (S2A), which leverages
the ability of LLMs to reason in natural language and follow instructions in
order to decide what to attend to. S2A regenerates the input context to only
include the relevant portions, before attending to the regenerated context to
elicit the final response. In experiments, S2A outperforms standard
attention-based LLMs on three tasks containing opinion or irrelevant
information, QA, math word problems and longform generation, where S2A
increases factuality and objectivity, and decreases sycophancy.
655c2e1bb27f103d7b8eb5c9
Jason Weston
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2023-11-21T13:15:56.450Z
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Jason Weston
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spermwhale
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Sainbayar Sukhbaatar
claimed_verified
2024-07-30T16:01:32.514Z
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Sainbayar Sukhbaatar
user
sainbar
System 2 Attention (is something you might need too)
Soft attention in Transformer-based Large Language Models (LLMs) is
susceptible to incorporating irrelevant information from the context into its
latent representations, which adversely affects next token generations. To help
rectify these issues, we introduce System 2 Attention (S2A), which leverages
the ability of LLMs to reason in natural language and follow instructions in
order to decide what to attend to. S2A regenerates the input context to only
include the relevant portions, before attending to the regenerated context to
elicit the final response. In experiments, S2A outperforms standard
attention-based LLMs on three tasks containing opinion or irrelevant
information, QA, math word problems and longform generation, where S2A
increases factuality and objectivity, and decreases sycophancy.
System 2 Attention in Transformer-based Large Language Models improves factual accuracy and reduces bias by refining input context.
soft attention Transformer-based Large Language Models (LLMs) System 2 Attention (S2A) natural language reasoning context regeneration factuality objectivity sycophancy QA math word problems longform generation