ProgCo: Program Helps Self-Correction of Large Language Models (2501.01264v1)

Abstract: Self-Correction aims to enable LLMs to self-verify and self-refine their initial responses without external feedback. However, LLMs often fail to effectively self-verify and generate correct feedback, further misleading refinement and leading to the failure of self-correction, especially in complex reasoning tasks. In this paper, we propose Program-driven Self-Correction (ProgCo). First, program-driven verification (ProgVe) achieves complex verification logic and extensive validation through self-generated, self-executing verification pseudo-programs. Then, program-driven refinement (ProgRe) receives feedback from ProgVe, conducts dual reflection and refinement on both responses and verification programs to mitigate misleading of incorrect feedback in complex reasoning tasks. Experiments on three instruction-following and mathematical benchmarks indicate that ProgCo achieves effective self-correction, and can be further enhance performance when combined with real program tools.

Analysis of "ProgCo: Program Helps Self-Correction of LLMs"

The paper "ProgCo: Program-driven Self-Correction in LLMs" introduces ProgCo, a novel framework aimed at enhancing the self-correction capability of LLMs. ProgCo offers a unique approach by integrating self-generated and self-executed pseudo-verification programs for both self-verification and self-refinement phases. This paper provides a thorough evaluation and presents promising results using this methodology, illustrating its potential impact on the efficacy of LLMs in handling complex reasoning tasks.

Overview and Methodology

LLMs typically struggle with intrinsic self-correction, particularly in complex reasoning and multi-step tasks, where hallucinations and erroneous outputs are prevalent. Traditional methods relying on prompting and checklist verification techniques often fall short due to overconfidence and inability to parse complex logic. ProgCo addresses these gaps through its two main components:

1. Program-driven Verification (ProgVe): ProgVe shifts the paradigm from natural language checks to generating pseudo-verification programs, which are executed by the model itself. This approach handles complex verification logic and benefits from the precision of programming constructs over natural language, thereby reducing ambiguity and improving recall rates of incorrect responses.
2. Program-driven Refinement (ProgRe): Building on feedback from ProgVe, ProgRe incorporates a dual reflection mechanism. It allows both the response and the verification program to be corrected iteratively, achieving greater robustness in handling incorrect feedback. By employing this dual refinement mechanism, ProgCo mitigates the risk of misleading corrections, a common pitfall in conventional self-correction processes.

Experimental Results and Implications

The paper provides a comprehensive evaluation of ProgCo across several tasks, including instruction-following and mathematical reasoning datasets (IFEval, GSM8K, and MATH). The results reveal significant performance improvements over baseline methods. For instance, ProgCo demonstrated considerable enhancements in accuracy across different LLM architectures like GPT-3.5 and GPT-4o, showing improvements up to 8% in complex mathematical tasks.

ProgCo's ability to incorporate real symbolic tools, such as Python executors, further extends its applicability. The combination of LLM execution with symbolic computation tools facilitates more reliable handling of numeric and symbolic reasoning tasks, which are usually challenging for purely LLM-driven approaches.

Future Research Directions

This research opens several avenues for future work in LLM self-correction. Firstly, integration of more sophisticated symbolic tools and interpreters during the ProgVe process could be explored to tackle larger-scale numerical computation tasks. Secondly, enhancements in human-like reasoning within the LLM's execution phase may benefit from programmatic structures tailored to specific domains. Additionally, future studies might investigate automating the ProgCo process to reduce inference overheads, potentially through specialized finetuning or reinforcement-learning strategies.

In conclusion, ProgCo presents a compelling methodology for enhancing LLMs through program integration, significantly advancing self-correction capabilities. This work sets a foundational approach for leveraging program-driven processes in AI, with the potential to transform complex reasoning and multi-step problem-solving in LLMs. As LLM technologies continue to evolve, methodologies like ProgCo will likely become integral for developing more robust, reliable, and intelligent AI systems.