title: Promote, Suppress, Iterate: How Language Models Answer One-to-Many
    Factual Queries
summary: 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.
paper_authors._id: 67cbd2a9101af374ebe1ee20
paper_authors.name: Tianyi Lorena Yan
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-03-10T08:02:06.336Z
paper_authors._id: 63102e82236215d0b7120b0e
paper_authors.avatarUrl: /avatars/232bc42247990c0c4efe5e57176cba85.svg
paper_authors.fullname: Tianyi Lorena Yan
paper_authors.type: user
paper_authors.user: LorenaYannnnn
paper_authors._id: 67cbd2a9101af374ebe1ee21
paper_authors.name: Robin Jia
paper_title: Promote, Suppress, Iterate: How Language Models Answer One-to-Many
    Factual Queries
paper_summary: 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.
paper_ai_keywords: attention MLPs (Multi-Layer Perceptrons) Token Lens causal tracing knockout method