title: Writing in the Margins: Better Inference Pattern for Long Context
    Retrieval
summary: In this paper, we introduce Writing in the Margins (WiM), a new inference
  pattern for Large Language Models designed to optimize the handling of long
  input sequences in retrieval-oriented tasks. This approach leverages the
  chunked prefill of the key-value cache to perform segment-wise inference, which
  enables efficient processing of extensive contexts along with the generation
  and classification of intermediate information ("margins") that guide the model
  towards specific tasks. This method increases computational overhead marginally
  while significantly enhancing the performance of off-the-shelf models without
  the need for fine-tuning. Specifically, we observe that WiM provides an average
  enhancement of 7.5% in accuracy for reasoning skills (HotpotQA, MultiHop-RAG)
  and more than a 30.0% increase in the F1-score for aggregation tasks (CWE).
  Additionally, we show how the proposed pattern fits into an interactive
  retrieval design that provides end-users with ongoing updates about the
  progress of context processing, and pinpoints the integration of relevant
  information into the final response. We release our implementation of WiM using
  Hugging Face Transformers library at
  https://github.com/writer/writing-in-the-margins.
paper_authors._id: 66ced55c5e180b9b9ca195ee
paper_authors.name: Melisa Russak
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-08-28T09:33:44.129Z
paper_authors._id: 60e61b3969bd0df25c9375da
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/1625692968400-noauth.jpeg
paper_authors.fullname: Melisa Russak
paper_authors.type: user
paper_authors.user: melisa
paper_authors._id: 66ced55c5e180b9b9ca195ef
paper_authors.name: Umar Jamil
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-08-28T09:36:57.524Z
paper_authors._id: 63805c4a9f1f158b014d63d8
paper_authors.avatarUrl: /avatars/3221a1ed4d3f809993ba5c79c341f88d.svg
paper_authors.fullname: Umar Jamil
paper_authors.type: user
paper_authors.user: hkproj
paper_authors._id: 66ced55c5e180b9b9ca195f0
paper_authors.name: Christopher Bryant
paper_authors._id: 66ced55c5e180b9b9ca195f1
paper_authors.name: Kiran Kamble
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-08-28T08:26:37.049Z
paper_authors._id: 621d6f532165dc431641e438
paper_authors.avatarUrl: /avatars/56ccef10a8426d7160ef3586a771bd63.svg
paper_authors.fullname: Kiran Kamble
paper_authors.type: user
paper_authors.user: kiranr
paper_authors._id: 66ced55c5e180b9b9ca195f2
paper_authors.name: Axel Magnuson
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-08-28T09:37:33.499Z
paper_authors._id: 65f0d67922af97ebe97d9cd8
paper_authors.avatarUrl: /avatars/2075546f2dfdd060e07b82c945180e97.svg
paper_authors.fullname: Axel Magnuson
paper_authors.type: user
paper_authors.user: axel-writer
paper_authors._id: 66ced55c5e180b9b9ca195f3
paper_authors.name: Mateusz Russak
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-08-28T09:42:18.925Z
paper_authors._id: 6357a6f4a1f8ad1c31bdcc81
paper_authors.avatarUrl: /avatars/5996bbfdef627bf43f5d74866f2ef3bd.svg
paper_authors.fullname: Mateusz Russak
paper_authors.type: user
paper_authors.user: mateuszrussak
paper_authors._id: 66ced55c5e180b9b9ca195f4
paper_authors.name: Waseem AlShikh
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-08-28T09:33:42.700Z
paper_authors._id: 60cd486d723acf5eb46fe8d3
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/60cd486d723acf5eb46fe8d3/Z1bD1kjvZ0QAOjZna41Xr.jpeg
paper_authors.fullname: Waseem AlShikh
paper_authors.type: user
paper_authors.user: wassemgtk
paper_title: Writing in the Margins: Better Inference Pattern for Long Context
    Retrieval
paper_summary: In this paper, we introduce Writing in the Margins (WiM), a new inference
  pattern for Large Language Models designed to optimize the handling of long
  input sequences in retrieval-oriented tasks. This approach leverages the
  chunked prefill of the key-value cache to perform segment-wise inference, which
  enables efficient processing of extensive contexts along with the generation
  and classification of intermediate information ("margins") that guide the model
  towards specific tasks. This method increases computational overhead marginally
  while significantly enhancing the performance of off-the-shelf models without
  the need for fine-tuning. Specifically, we observe that WiM provides an average
  enhancement of 7.5% in accuracy for reasoning skills (HotpotQA, MultiHop-RAG)
  and more than a 30.0% increase in the F1-score for aggregation tasks (CWE).
  Additionally, we show how the proposed pattern fits into an interactive
  retrieval design that provides end-users with ongoing updates about the
  progress of context processing, and pinpoints the integration of relevant
  information into the final response. We release our implementation of WiM using
  Hugging Face Transformers library at
  https://github.com/writer/writing-in-the-margins.
paper_ai_keywords: key-value cache segment-wise inference retrieval-oriented tasks intermediate information margins reasoning skills HotpotQA MultiHop-RAG aggregation tasks CWE interactive retrieval design ongoing updates final response Hugging Face Transformers library