CodeSteer: Symbolic-Augmented Language Models via Code/Text Guidance
Existing methods fail to effectively steer Large Language Models (LLMs)
between textual reasoning and code generation, leaving symbolic computing
capabilities underutilized. We introduce CodeSteer, an effective method for
guiding LLM code/text generation. We construct a comprehensive benchmark
SymBench comprising 37 symbolic tasks with adjustable complexity and also
synthesize datasets of 12k multi-round guidance/generation trajectories and
5.5k guidance comparison pairs. We fine-tune the Llama-3-8B model with a newly
designed multi-round supervised fine-tuning (SFT) and direct preference
optimization (DPO). The resulting model, CodeSteerLLM, augmented with the
proposed symbolic and self-answer checkers, effectively guides the code/text
generation of larger models. Augmenting GPT-4o with CodeSteer raises its
average performance score from 53.3 to 86.4, even outperforming the existing
best LLM OpenAI o1 (82.7), o1-preview (74.8), and DeepSeek R1 (76.8) across all
37 tasks (28 seen, 9 unseen). Trained for GPT-4o, CodeSteer demonstrates
superior generalizability, providing an average 41.8 performance boost on
Claude, Mistral, and GPT-3.5. CodeSteer-guided LLMs fully harness symbolic
computing to maintain strong performance on highly complex tasks. Models,
Datasets, and Codes are available at
https://github.com/yongchao98/CodeSteer-v1.0.
67a97a77d163c9e6ea2bdb85
Yongchao Chen
claimed_verified
2025-02-13T08:26:07.141Z
67266d21b7d88dbcf9e6c4aa
/avatars/0328058e74c424473ef890d1fbdd3e4d.svg
Yongchao Chen
user
yongchao98
67a97a77d163c9e6ea2bdb86
Yilun Hao
67a97a77d163c9e6ea2bdb87
Yueying Liu
67a97a77d163c9e6ea2bdb88
Yang Zhang
67a97a77d163c9e6ea2bdb89
Chuchu Fan
CodeSteer: Symbolic-Augmented Language Models via Code/Text Guidance
Existing methods fail to effectively steer Large Language Models (LLMs)
between textual reasoning and code generation, leaving symbolic computing
capabilities underutilized. We introduce CodeSteer, an effective method for
guiding LLM code/text generation. We construct a comprehensive benchmark
SymBench comprising 37 symbolic tasks with adjustable complexity and also
synthesize datasets of 12k multi-round guidance/generation trajectories and
5.5k guidance comparison pairs. We fine-tune the Llama-3-8B model with a newly
designed multi-round supervised fine-tuning (SFT) and direct preference
optimization (DPO). The resulting model, CodeSteerLLM, augmented with the
proposed symbolic and self-answer checkers, effectively guides the code/text
generation of larger models. Augmenting GPT-4o with CodeSteer raises its
average performance score from 53.3 to 86.4, even outperforming the existing
best LLM OpenAI o1 (82.7), o1-preview (74.8), and DeepSeek R1 (76.8) across all
37 tasks (28 seen, 9 unseen). Trained for GPT-4o, CodeSteer demonstrates
superior generalizability, providing an average 41.8 performance boost on
Claude, Mistral, and GPT-3.5. CodeSteer-guided LLMs fully harness symbolic
computing to maintain strong performance on highly complex tasks. Models,
Datasets, and Codes are available at
https://github.com/yongchao98/CodeSteer-v1.0.
CodeSteer, a method for guiding LLMs in code and text generation, enhances symbolic computing capabilities, significantly boosting performance across various tasks and models.
Large Language Models (LLMs) symbolic computing CodeSteer SymBench multi-round supervised fine-tuning (SFT) direct preference optimization (DPO) CodeSteerLLM GPT-4o OpenAI o1 o1-preview DeepSeek R1 Claude Mistral GPT-3.5