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
claimed_verified
2025-02-24T13:05:08.989Z
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Zeng Zihao
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67bbe4ba79e0a705cf573986
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.
large language models Llama3.2-3B-Instruct DeepSeek-R1 reasoning process context calculation errors Stick to the Facts (SIFT) increasing inference-time compute Sticker forward optimization inverse generation performance improvements GSM8K MATH-500 pass@1 accuracy AIME2024