SIFT: Grounding LLM Reasoning in Contexts via Stickers (2502.14922v1)

Abstract: This paper identifies the misinterpretation of the context can be a significant issue during the reasoning process of LLMs, 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 Formal Analysis of SIFT: Grounding LLM Reasoning in Contexts via Stickers

The paper "SIFT: Grounding LLM Reasoning in Contexts via Stickers" addresses a notably intricate issue in the reasoning mechanisms of LLMs. Specifically, it identifies the problem of context misinterpretation by these models during reasoning processes as a phenomenon termed "factual drift." This misinterpretation can lead to erroneous reasoning outputs, an issue prevalent across various models, from less complex structures like Llama3.2-3B-Instruct to advanced models like DeepSeek-R1.

To tackle this challenge, the authors propose a novel post-training methodology named Stick to the Facts (SIFT). The core innovation in SIFT lies in its introduction of a "Sticker," a model-generated artifact that emphasizes essential contextual information during reasoning. The methodology leverages inference-time computation to ground the model's reasoning in its contextual basis.

SIFT operates through a sophisticated sequence of processes: Initially, it generates a Sticker that encapsulates the gist of the query. The mechanism involves dual predictions—one relying solely on the query and the other on the query augmented with the Sticker. In cases of divergence between these predictions, the Sticker undergoes sequential refinement through forward optimization (tuning alignment with the query) and inverse generation (aligning with the model's inherent tendencies). The goal is to yield more accurate reasoning outcomes.

The empirical evaluation of SIFT demonstrates consistent performance improvements across a spectrum of models and benchmarks. Notably, it enhances the pass@1 accuracy of the DeepSeek-R1 model on the AIME2024 benchmark from 78.33% to 85.67%, establishing a new state-of-the-art performance in the open-source community. These results are particularly significant for the DeepSeek-R1 model, where even a relatively small percentage increase in accuracy represents a substantial leap given its advanced baseline performance.

The implications of this research extend both practically and theoretically within the AI domain. Practically, SIFT offers a method to significantly enhance reasoning accuracy without additional model training, making it a cost-effective solution for LLM applications. Theoretically, the concept of using self-generated contextual markers (Stickers) highlights a potent strategy for LLM reasoning augmentation, potentially inspiring further refinement in AI models to better capture and utilize context.

Future developments evolving from this work can explore internalizing the SIFT framework deeper into smaller LLM architectures, potentially enabling efficient on-device reasoning capabilities. Additionally, by minimizing output token lengths, SIFT could optimize computational efficiency, a critical factor in real-world deployment scenarios. Furthermore, its inverse Sticker generation method may hold promise in advancing data generation tasks, thus broadening applications in AI systems where reverse synthesis is required.

In conclusion, the SIFT methodology presents a compelling advancement in addressing context misinterpretations in LLM reasoning processes. Its implementation could markedly shift how LLMs are employed in tasks requiring precise contextual understanding and offer a strategic pathway for future research into efficient, context-aware AI systems.