Generating Sequences by Learning to Self-Correct (2211.00053v1)

Abstract: Sequence generation applications require satisfying semantic constraints, such as ensuring that programs are correct, using certain keywords, or avoiding undesirable content. LLMs, whether fine-tuned or prompted with few-shot demonstrations, frequently violate these constraints, and lack a mechanism to iteratively revise their outputs. Moreover, some powerful LLMs are of extreme scale or inaccessible, making it inefficient, if not infeasible, to update their parameters for task-specific adaptation. We present Self-Correction, an approach that decouples an imperfect base generator (an off-the-shelf LLM or supervised sequence-to-sequence model) from a separate corrector that learns to iteratively correct imperfect generations. To train the corrector, we propose an online training procedure that can use either scalar or natural language feedback on intermediate imperfect generations. We show that Self-Correction improves upon the base generator in three diverse generation tasks - mathematical program synthesis, lexically-constrained generation, and toxicity control - even when the corrector is much smaller than the base generator.

Generating Sequences by Learning to [Self-]Correct: An Overview

The paper "Generating Sequences by Learning to [Self-]Correct" presents an advanced methodology for sequence generation tasks, emphasizing the importance of semantic constraint satisfaction. Typical natural LLMs, whether fine-tuned or prompted in few-shot frameworks, often face challenges in meeting these constraints. They may produce sequences that lack accuracy, omit required keywords, or include undesirable content. Furthermore, these models are predominantly single-pass systems, generating output without iterative refinement capabilities. This approach neglects partially correct and useful structures embedded within suboptimal sequences, requiring a complete restart in case of errors. The proposed 'self-correction' mechanism introduces a paradigm shift, decoupling the base generator from a corrector module explicitly trained to rectify the sequence iteratively.

Self-Correction Framework

Self-correction leverages a base generator—a pre-existing LLM or supervised sequence-to-sequence model—and a corrector that iteratively improves the sequence's quality. This approach allows for task-specific adaptation without altering the base model parameters, which can be particularly advantageous given the constraints of large-scale, sometimes inaccessible LLMs. The corrector employs an online training procedure that integrates feedback, either scalar or in natural language, to refine intermediate outputs.

Empirical Evaluation

The self-correction framework was evaluated across diverse tasks: mathematical program synthesis, lexically-constrained generation, and toxicity control. Each task demonstrates unique properties:

1. Mathematical Program Synthesis: The corrector significantly elevated program accuracy over the base GPT-Neo model. Using problem-solving datasets demanding semantic precision, the self-corrector nearly doubled the accuracy metrics compared to the generator alone, proving effective even for complex sequence structures.
2. Lexically Constrained Generation: Applying self-correction to sentence generation tasks showed improvements in constraint satisfaction without deteriorating fluency. Compared to sophisticated decoding algorithms, self-corrector harnessed both efficiency and effectiveness, maintaining competitive rates of constraint fulfiLLMent while being computationally less demanding.
3. Toxicity Control: Addressing safety in LLM outputs, self-correction successfully reduced the generation of toxic content compared to the base models and traditional approaches such as PPLM and GeDi. The corrector maintained text fluency and diversity, indicating its utility in generating safe, varied content without compromising style or readability.

Modularity and Feedback

A notable finding was the modularity of the self-correction approach, which permits correcting larger generators, such as GPT-3, regardless of a smaller trained corrector. This flexibility suggests applications in improving outputs of various models without retraining the generator. Moreover, incorporating explicit natural language feedback further enhanced the corrector's efficacy, allowing nuanced adjustments informed by real-time assessments rather than mere scalar feedback.

Implications and Future Directions

The self-correction methodology introduces a novel dimension in sequence generation, pushing the boundaries of iterative refinement and efficiency. By decoupling generation from correction, it achieves expressive and adaptable sequence rectification, suitable for both small-scale models and larger, resource-intensive frameworks. Practical implications span enhancing the fidelity of generated content, automatic error detection and correction, and achieving safe model deployments in naturally constrained environments. The theoretical implications suggest potential in hierarchical task decomposition, leading to robust multitask models capable of nuanced discernment between generation fidelity and correction strategies.

Future research could delve into optimizing the feedback mechanisms further, exploring more dynamic, real-time corrective guidance systems, and extending self-correction frameworks into broader, more complex generation paradigms. The adaptability in sequence improvement marks a progressive step in natural language processing, potentially redefining model training, deployment, and evaluation metrics across various domains.