title: CodeSteer: Symbolic-Augmented Language Models via Code/Text Guidance
summary: 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.
paper_authors._id: 67a97a77d163c9e6ea2bdb85
paper_authors.name: Yongchao Chen
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-02-13T08:26:07.141Z
paper_authors._id: 67266d21b7d88dbcf9e6c4aa
paper_authors.avatarUrl: /avatars/0328058e74c424473ef890d1fbdd3e4d.svg
paper_authors.fullname: Yongchao Chen
paper_authors.type: user
paper_authors.user: yongchao98
paper_authors._id: 67a97a77d163c9e6ea2bdb86
paper_authors.name: Yilun Hao
paper_authors._id: 67a97a77d163c9e6ea2bdb87
paper_authors.name: Yueying Liu
paper_authors._id: 67a97a77d163c9e6ea2bdb88
paper_authors.name: Yang Zhang
paper_authors._id: 67a97a77d163c9e6ea2bdb89
paper_authors.name: Chuchu Fan
paper_title: CodeSteer: Symbolic-Augmented Language Models via Code/Text Guidance
paper_summary: 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.
paper_ai_keywords: Large Language Models (LLMs) CodeSteer SymBench multi-round guidance/generation trajectories guidance comparison pairs multi-round supervised fine-tuning (SFT) direct preference optimization (DPO) CodeSteerLLM symbolic and self-answer checkers GPT-4o OpenAI o1 OpenAI o1-preview DeepSeek R1 Claude Mistral GPT-3.5