title: Backpack Language Models
summary: We present Backpacks: a new neural architecture that marries strong modeling
  performance with an interface for interpretability and control. Backpacks learn
  multiple non-contextual sense vectors for each word in a vocabulary, and
  represent a word in a sequence as a context-dependent, non-negative linear
  combination of sense vectors in this sequence. We find that, after training,
  sense vectors specialize, each encoding a different aspect of a word. We can
  interpret a sense vector by inspecting its (non-contextual, linear) projection
  onto the output space, and intervene on these interpretable hooks to change the
  model's behavior in predictable ways. We train a 170M-parameter Backpack
  language model on OpenWebText, matching the loss of a GPT-2 small
  (124Mparameter) Transformer. On lexical similarity evaluations, we find that
  Backpack sense vectors outperform even a 6B-parameter Transformer LM's word
  embeddings. Finally, we present simple algorithms that intervene on sense
  vectors to perform controllable text generation and debiasing. For example, we
  can edit the sense vocabulary to tend more towards a topic, or localize a
  source of gender bias to a sense vector and globally suppress that sense.
paper_authors._id: 64741ba97be71eb8b1ba55f6
paper_authors.name: John Hewitt
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paper_authors.fullname: John Hewitt
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paper_authors.user: johnhew
paper_authors._id: 64741ba97be71eb8b1ba55f7
paper_authors.name: John Thickstun
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paper_authors.fullname: John Thickstun
paper_authors.type: user
paper_authors.user: jthickstun
paper_authors._id: 64741ba97be71eb8b1ba55f8
paper_authors.name: Christopher D. Manning
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paper_authors.fullname: Christopher Manning
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paper_authors.user: manning
paper_authors._id: 64741ba97be71eb8b1ba55f9
paper_authors.name: Percy Liang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-05-30T08:59:15.269Z
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paper_authors.fullname: Percy Liang
paper_authors.type: user
paper_authors.user: percyliang
paper_title: Backpack Language Models
paper_summary: We present Backpacks: a new neural architecture that marries strong modeling
  performance with an interface for interpretability and control. Backpacks learn
  multiple non-contextual sense vectors for each word in a vocabulary, and
  represent a word in a sequence as a context-dependent, non-negative linear
  combination of sense vectors in this sequence. We find that, after training,
  sense vectors specialize, each encoding a different aspect of a word. We can
  interpret a sense vector by inspecting its (non-contextual, linear) projection
  onto the output space, and intervene on these interpretable hooks to change the
  model's behavior in predictable ways. We train a 170M-parameter Backpack
  language model on OpenWebText, matching the loss of a GPT-2 small
  (124Mparameter) Transformer. On lexical similarity evaluations, we find that
  Backpack sense vectors outperform even a 6B-parameter Transformer LM's word
  embeddings. Finally, we present simple algorithms that intervene on sense
  vectors to perform controllable text generation and debiasing. For example, we
  can edit the sense vocabulary to tend more towards a topic, or localize a
  source of gender bias to a sense vector and globally suppress that sense.
paper_ai_summary: Backpacks, a novel neural architecture, uses multiple sense vectors per word for context-dependent representation, offering interpretability and control with strong modeling performance.
paper_ai_keywords: neural architecture sense vectors context-dependent linear combination interpretability intervention language model OpenWebText GPT-2 Transformer lexical similarity controllable text generation debiasing