Promote, Suppress, Iterate: How Language Models Answer One-to-Many
  Factual Queries
To answer one-to-many factual queries (e.g., listing cities of a country), a
language model (LM) must simultaneously recall knowledge and avoid repeating
previous answers. How are these two subtasks implemented and integrated
internally? Across multiple datasets and models, we identify a
promote-then-suppress mechanism: the model first recalls all answers, and then
suppresses previously generated ones. Specifically, LMs use both the subject
and previous answer tokens to perform knowledge recall, with attention
propagating subject information and MLPs promoting the answers. Then, attention
attends to and suppresses previous answer tokens, while MLPs amplify the
suppression signal. Our mechanism is corroborated by extensive experimental
evidence: in addition to using early decoding and causal tracing, we analyze
how components use different tokens by introducing both Token Lens, which
decodes aggregated attention updates from specified tokens, and a knockout
method that analyzes changes in MLP outputs after removing attention to
specified tokens. Overall, we provide new insights into how LMs' internal
components interact with different input tokens to support complex factual
recall. Code is available at
https://github.com/Lorenayannnnn/how-lms-answer-one-to-many-factual-queries.
67cbd2a9101af374ebe1ee20
Tianyi Lorena Yan
claimed_verified
2025-03-10T08:02:06.336Z
63102e82236215d0b7120b0e
/avatars/232bc42247990c0c4efe5e57176cba85.svg
Tianyi Lorena Yan
user
LorenaYannnnn
67cbd2a9101af374ebe1ee21
Robin Jia
Promote, Suppress, Iterate: How Language Models Answer One-to-Many
  Factual Queries
To answer one-to-many factual queries (e.g., listing cities of a country), a
language model (LM) must simultaneously recall knowledge and avoid repeating
previous answers. How are these two subtasks implemented and integrated
internally? Across multiple datasets and models, we identify a
promote-then-suppress mechanism: the model first recalls all answers, and then
suppresses previously generated ones. Specifically, LMs use both the subject
and previous answer tokens to perform knowledge recall, with attention
propagating subject information and MLPs promoting the answers. Then, attention
attends to and suppresses previous answer tokens, while MLPs amplify the
suppression signal. Our mechanism is corroborated by extensive experimental
evidence: in addition to using early decoding and causal tracing, we analyze
how components use different tokens by introducing both Token Lens, which
decodes aggregated attention updates from specified tokens, and a knockout
method that analyzes changes in MLP outputs after removing attention to
specified tokens. Overall, we provide new insights into how LMs' internal
components interact with different input tokens to support complex factual
recall. Code is available at
https://github.com/Lorenayannnnn/how-lms-answer-one-to-many-factual-queries.
Language models use a promote-then-suppress mechanism to recall and prevent repetition of answers in one-to-many factual queries, utilizing attention and MLPs for these processes.
attention MLPs promote-then-suppress mechanism Token Lens knockout method