title: SIFT: Grounding LLM Reasoning in Contexts via Stickers
summary: This paper identifies the misinterpretation of the context can be a
  significant issue during the reasoning process of large language models,
  spanning from smaller models like Llama3.2-3B-Instruct to cutting-edge ones
  like DeepSeek-R1. For example, in the phrase "10 dollars per kilo," LLMs might
  not recognize that "per" means "for each," leading to calculation errors. We
  introduce a novel, post-training approach called **Stick to the Facts (SIFT)**
  to tackle this. SIFT leverages increasing inference-time compute to ground LLM
  reasoning in contexts. At the core of SIFT lies the *Sticker*, which is
  generated by the model itself to explicitly emphasize the key information
  within the context. Given the curated Sticker, SIFT generates two predictions
  -- one from the original query and one from the query augmented with the
  Sticker. If they differ, the Sticker is sequentially refined via *forward*
  optimization (to better align the extracted facts with the query) and *inverse*
  generation (to conform with the model's inherent tendencies) for more faithful
  reasoning outcomes. Studies across diverse models (from 3B to 100B+) and
  benchmarks (e.g., GSM8K, MATH-500) reveal consistent performance improvements.
  Notably, SIFT improves the pass@1 accuracy of DeepSeek-R1 on AIME2024 from
  78.33% to **85.67**%, establishing a new state-of-the-art in the open-source
  community. The code is available at https://github.com/zhijie-group/SIFT.
paper_authors._id: 67bbe4ba79e0a705cf573985
paper_authors.name: Zihao Zeng
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-02-24T13:05:08.989Z
paper_authors._id: 6544c0585a13979f82038a1c
paper_authors.avatarUrl: /avatars/01f3e862d49020e9eaf1728e4ba97bea.svg
paper_authors.fullname: Zeng Zihao
paper_authors.type: user
paper_authors.user: zzh6666
paper_authors._id: 67bbe4ba79e0a705cf573986
paper_authors.name: Xuyao Huang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-02-24T13:19:22.415Z
paper_authors._id: 6721dacfc5309c08451d21d5
paper_authors.avatarUrl: /avatars/ac8be5ac8b8ee5b5533214e526b72dad.svg
paper_authors.fullname: Huang Xuyao
paper_authors.type: user
paper_authors.user: ElysiaTrue
paper_authors._id: 67bbe4ba79e0a705cf573987
paper_authors.name: Boxiu Li
paper_authors._id: 67bbe4ba79e0a705cf573988
paper_authors.name: Zhijie Deng
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-02-24T13:19:36.725Z
paper_authors._id: 673d5f411b0fe168ad4896b2
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/fYgQx6XNi_P5GxlUKbH5G.png
paper_authors.fullname: Zhijie Deng
paper_authors.type: user
paper_authors.user: thudzj
paper_title: SIFT: Grounding LLM Reasoning in Contexts via Stickers
paper_summary: This paper identifies the misinterpretation of the context can be a
  significant issue during the reasoning process of large language models,
  spanning from smaller models like Llama3.2-3B-Instruct to cutting-edge ones
  like DeepSeek-R1. For example, in the phrase "10 dollars per kilo," LLMs might
  not recognize that "per" means "for each," leading to calculation errors. We
  introduce a novel, post-training approach called **Stick to the Facts (SIFT)**
  to tackle this. SIFT leverages increasing inference-time compute to ground LLM
  reasoning in contexts. At the core of SIFT lies the *Sticker*, which is
  generated by the model itself to explicitly emphasize the key information
  within the context. Given the curated Sticker, SIFT generates two predictions
  -- one from the original query and one from the query augmented with the
  Sticker. If they differ, the Sticker is sequentially refined via *forward*
  optimization (to better align the extracted facts with the query) and *inverse*
  generation (to conform with the model's inherent tendencies) for more faithful
  reasoning outcomes. Studies across diverse models (from 3B to 100B+) and
  benchmarks (e.g., GSM8K, MATH-500) reveal consistent performance improvements.
  Notably, SIFT improves the pass@1 accuracy of DeepSeek-R1 on AIME2024 from
  78.33% to **85.67**%, establishing a new state-of-the-art in the open-source
  community. The code is available at https://github.com/zhijie-group/SIFT.
paper_ai_summary: A novel post-training approach, Stick to the Facts (SIFT), improves the reasoning accuracy of large language models by generating and refining stickers, leading to enhanced performance on diverse benchmarks.
paper_ai_keywords: Large language models Stick to the Facts (SIFT) Sticker forward optimization inverse generation GSM8K MATH-500 AIME2024