Enhancing Generalization in Chain of Thought Reasoning for Smaller Models (2501.09804v1)

Abstract: Chain-of-Thought (CoT) reasoning in smaller LLMs is a challenging natural language process problem yet highly desirable in many real-life applications. Existing CoT knowledge distillation methods often suffer from overly conservative memorization in smaller LLMs, leading to low generalization confidence. As fully preserving the CoT ability of teacher model is impossible, we hypothesize that adversarial CoT fine-tuning is crucial for developing smaller LLM with robust CoT generalization. To this end, we propose \textit{PRompt-Assisted Domain-Adversarial fine-tuning} (PRADA), a principled fine-tuning framework that integrates diverse CoT domains. Specifically, PRADA pioneers two CoT improvements in smaller LLM: (1) Recovering the domain-invariant feature insight which typically lost during distillation with domain adversarial fine-tuning; (2) Enhancing the domain adaptability of CoT prompt engineering by employing domain-adversarial approaches. We theoretically demonstrate the effectiveness of our approach and empirically show that it significantly outperforms the state of the arts in a wide range of tasks. Moreover, our empirical findings reveal that the smaller LLM, when leveraging PRADA, aligns closely with domain knowledge, thereby improving the explainability of our approach.

• The paper introduces PRADA to overcome memorization, significantly enhancing generalization in chain-of-thought reasoning for small models.
• It employs diverse reasoning generation and P-tuning to enable domain-agnostic knowledge acquisition for improved cross-domain performance.
• Empirical tests across 12 datasets validate PRADA’s efficiency, reducing computational costs while maintaining robust reasoning capabilities.

Enhancing Generalization in Chain of Thought Reasoning for Smaller Models

This paper addresses the crucial challenge of improving the generalization capabilities of smaller LLMs in Chain-of-Thought (CoT) reasoning processes, an area of substantial interest due to its implications for computational efficiency and practical deployment in real-world applications. The authors propose a novel framework—PRompt-Assisted Domain-Adversarial fine-tuning (PRADA)—to tackle the issues faced by smaller models that have been distilled from extensive LLMs such as GPT-3.

The central limitation observed with current CoT knowledge distillation methods is a significant shift towards memorization rather than generalization when reducing the size of the models. This transition often results in poor performance when models are applied to new, unseen domains. The paper suggests that achieving robust generalization in smaller models necessitates adversarial fine-tuning strategies to recover domain-invariant features typically lost during standard distillation processes. PRADA precisely addresses this by improving CoT reasoning through adversarial learning techniques.

The PRADA methodology incorporates three primary innovations:

1. Diverse CoT Reasoning Generation: Utilizing a large teacher model, diverse CoT reasoning responses are generated via task-agnostic Zero-Shot-CoT prompting. This step is essential to ensure the student model can be exposed to a variety of reasoning paths and not limit to specific memorized sequences.
2. Deployment of Prompt Learning Techniques: The student model employs P-Tuning, where continuous prompt embeddings are fine-tuned on the source domain. This phase aims to facilitate the acquisition of domain-agnostic knowledge, increasing the model's adaptability across diverse domains.
3. Domain-Adversarial Fine-Tuning: Incorporating both source and target domain data, this phase aims to fine-tune the student model’s parameters while maintaining stable domain invariance and avoiding substantial performance loss across multiple domains.

The paper provides a theoretical justification for the PRADA framework and supports these claims with empirical data from experiments across 12 diverse datasets. The numerical results indicate that PRADA surpasses existing CoT distillation methods in task performance, particularly when cross-domain adaptability is required.

The implications of this research extend both practically and theoretically. Practically, the PRADA framework could lower computational costs associated with deploying LLMs by enabling smaller models to retain robust reasoning capacities originally exclusive to their larger counterparts. Theoretically, this work enriches the existing literature on model distillation, domain adaptation, and adversarial training, showing a clear pathway to enhance generalization skills in smaller architectures.

Future research directions may explore the scalability of PRADA to different architectures and additional domains, possibly uncovering new insights into hierarchical task generalization and transfer learning mechanisms. The integration of such frameworks could lead to rapid advancements in efficiently training smaller, yet equally proficient, models with broad application potential. As AI research continues to blend adversarial strategies with model training, further emphasis on preserving generalization abilities without imposing parameter burden will be critical.