SIFT: Grounding LLM Reasoning in Contexts via Stickers
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.
67bbe4ba79e0a705cf573985
Zihao Zeng
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2025-02-24T13:05:08.989Z
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Zeng Zihao
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Xuyao Huang
admin_assigned
2025-02-24T13:19:22.415Z
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Huang Xuyao
user
ElysiaTrue
67bbe4ba79e0a705cf573987
Boxiu Li
67bbe4ba79e0a705cf573988
Zhijie Deng
admin_assigned
2025-02-24T13:19:36.725Z
673d5f411b0fe168ad4896b2
https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/fYgQx6XNi_P5GxlUKbH5G.png
Zhijie Deng
user
thudzj
SIFT: Grounding LLM Reasoning in Contexts via Stickers
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.
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.
Large language models Stick to the Facts (SIFT) Sticker forward optimization inverse generation GSM8K MATH-500 AIME2024