Semi-supervised Fine-tuning for Large Language Models (2410.14745v2)

Abstract: Supervised fine-tuning (SFT) is crucial in adapting LLM (LLMs) to a specific domain or task. However, only a limited amount of labeled data is available in practical applications, which poses a severe challenge for SFT in yielding satisfactory results. Therefore, a data-efficient framework that can fully exploit labeled and unlabeled data for LLM fine-tuning is highly anticipated.Towards this end, we introduce a semi-supervised fine-tuning(SemiFT) task and a framework named SemiEvol for LLM alignment from a propagate-and-select manner. For knowledge propagation, SemiEvol adopts a bi-level approach, propagating knowledge from labeled data to unlabeled data through both in-weight and in-context methods. For knowledge selection, SemiEvol incorporates a collaborative learning mechanism, selecting higher-quality pseudo-response samples. We conducted experiments using GPT-4o-mini and Llama-3.1 on seven general or domain-specific datasets, demonstrating significant improvements in model performance on target data. Furthermore, we compared SemiEvol with SFT and self-evolution methods, highlighting its practicality in hybrid data scenarios.

• The paper introduces SemiEvol, a semi-supervised fine-tuning framework that improves LLM adaptability using minimal labeled data and abundant unlabeled data.
• It employs dual strategies—knowledge propagation via in-weight and in-context methods—and collaborative learning to generate high-confidence pseudo-responses.
• Evaluations on models like GPT-4o-mini and Llama-3.1 show significant error reductions across benchmarks such as ARC and ConvFinQA.

SemiEvol: Semi-supervised Fine-tuning for LLM Adaptation

The paper entitled "SemiEvol: Semi-supervised Fine-tuning for LLM Adaptation" presents a novel framework aimed at enhancing the adaptability and performance of LLMs through semi-supervised fine-tuning. This approach is particularly relevant for scenarios where labeled data is scarce, yet abundant unlabeled data is accessible.

Key Contributions

The primary contribution of this research is the introduction of the SemiEvol framework, designed to exploit both labeled and unlabeled data effectively. The framework operates through a bi-level structure consisting of knowledge propagation and knowledge selection. The researchers address a pivotal question: how can LLMs evolve using minimal labeled data alongside plentiful unlabeled data? The complex challenge presented by this hybrid-data scenario is addressed through several complementary strategies.

Methodological Innovations

1. Knowledge Propagation: SemiEvol employs a dual methodology for knowledge dissemination among data types:
   • In-weight Propagation: Adapts model weights utilizing labeled data to improve inference performance.
   • In-context Propagation: Engages k-nearest neighbor retrieval in latent space to enhance predictions on unlabeled data.
2. Collaborative Learning: This mechanism employs multiple versions of LLMs with differing configurations to collaboratively generate high-confidence pseudo-responses for unlabeled data. By justifying and selecting the most appropriate inferences, this method aims to refine the overall output quality.
3. Knowledge Adaptive Selection: By assessing the entropy of predicted responses, this approach dynamically identifies and selects high-confidence pseudo-labeled instances, refining the data subsequently used for model training.

Strong Results and Impacts

The experimental results provided in the paper are compelling. Evaluations conducted on models such as GPT-4o-mini and Llama-3.1 across several datasets, including MMLU and ConvFinQA, show significant performance improvements. Notably, error reduction percentages in tasks like ARC (14.1%) and ConvFinQA (70.1%) illustrate the efficacy of SemiEvol in various scenarios.

Theoretical and Practical Implications

Theoretically, this research enhances our understanding of semi-supervised learning in the context of generative tasks, an area that traditionally focused on classification. It demonstrates how hybrid data strategies can bridge gaps left by conventional supervised and self-evolution methods. Practically, by reducing reliance on labeled data, this framework offers a cost-effective and efficient solution for adapting LLMs to niche or rapidly evolving domain-specific tasks.

Future Directions

Considering the demonstrated success of SemiEvol, future research might explore its applicability to more complex domains requiring specialized knowledge, such as genomics or legal documentation. Additionally, computational evaluations with larger models like GPT-4o or Llama3.1 70B could provide further insights into scalability and effectiveness.

In conclusion, the SemiEvol framework represents a significant step forward in the adaptive application of LLMs, promising enhanced flexibility and performance across a breadth of real-world data scenarios. Its innovative use of semi-supervised methodologies positions it as a valuable tool for future AI development.