Fine-tuning large language models for domain adaptation: Exploration of training strategies, scaling, model merging and synergistic capabilities (2409.03444v1)

Abstract: The advancement of LLMs for domain applications in fields such as materials science and engineering depends on the development of fine-tuning strategies that adapt models for specialized, technical capabilities. In this work, we explore the effects of Continued Pretraining (CPT), Supervised Fine-Tuning (SFT), and various preference-based optimization approaches, including Direct Preference Optimization (DPO) and Odds Ratio Preference Optimization (ORPO), on fine-tuned LLM performance. Our analysis shows how these strategies influence model outcomes and reveals that the merging of multiple fine-tuned models can lead to the emergence of capabilities that surpass the individual contributions of the parent models. We find that model merging leads to new functionalities that neither parent model could achieve alone, leading to improved performance in domain-specific assessments. Experiments with different model architectures are presented, including Llama 3.1 8B and Mistral 7B models, where similar behaviors are observed. Exploring whether the results hold also for much smaller models, we use a tiny LLM with 1.7 billion parameters and show that very small LLMs do not necessarily feature emergent capabilities under model merging, suggesting that model scaling may be a key component. In open-ended yet consistent chat conversations between a human and AI models, our assessment reveals detailed insights into how different model variants perform and show that the smallest model achieves a high intelligence score across key criteria including reasoning depth, creativity, clarity, and quantitative precision. Other experiments include the development of image generation prompts based on disparate biological material design concepts, to create new microstructures, architectural concepts, and urban design based on biological materials-inspired construction principles.

• The paper demonstrates that continued pretraining (CPT) significantly enhances domain-specific LLM performance using specialized corpora.
• It reveals that supervised fine-tuning (SFT) and preference-based optimization like DPO and ORPO effectively tailor models for specific applications.
• The paper uncovers that model merging via SLERP produces synergistic emergent behaviors, unlocking new capabilities beyond individual model performance.

Fine-tuning LLMs for Domain Adaptation: Evaluations and Implications

The paper explores the intricacies of fine-tuning LLMs for domain-specific applications, particularly within the context of materials science and engineering. The authors investigate numerous training strategies, including Continued Pretraining (CPT), Supervised Fine-Tuning (SFT), and preference-based optimization techniques such as Direct Preference Optimization (DPO) and Odds Ratio Preference Optimization (ORPO). They also examine the effects of model merging, using techniques like Spherical Linear Interpolation (SLERP), to enhance the capabilities of these models beyond their initial individual performances.

Key Findings and Strategies

1. Continued Pretraining (CPT): Continued Pretraining ensures that the models are finely tuned with domain-specific corpus from materials science, thereby refining their capabilities and enhancing their technical accuracy. This method forms the backbone of the fine-tuning strategy, and when used with high-quality text, provides a stronger foundation for further improvements in performance.
2. Supervised Fine-Tuning (SFT): The authors utilized SFT to adapt models to specialized tasks. By employing curated sets of question-answer pairs, they effectively train the models to excel in specific applications within their domain.
3. Preference-Based Optimization: Both DPO and ORPO emerge as valuable tools in aligning models closer to human preferences. This bypasses the more computationally intensive reinforcement learning methods traditionally used for this purpose.
4. Model Merging via SLERP: One of the paper's most notable explorations is the potential of model merging. Using SLERP, a geometrically-aware method, the authors demonstrate that merged models can exhibit emergent capabilities not present in either parent model. The nonlinear character of SLERP blending respects the complex layer interactions, unlocking improved performances and new functionalities.

Implications for Theory and Applications

The theoretical implications of this research can have far-reaching effects on how we conceptualize the emergent capabilities of AI systems. Particularly, this paper provides substantial insights into the non-linear interactions within neural networks brought forth by model merging strategies like SLERP. The observed emergent behaviors suggest that there may be untapped potential in leveraging the intrinsic geometrical properties of the model parameter space.

From a practical perspective, the findings underscore the viability of utilizing model merging as a transformative tool. This method does not merely amalgamate knowledge but synergistically enhances the model's capabilities, offering significant efficiencies in AI system design. Applications in specialized fields, such as material science-related AI applications, stand to benefit greatly from these advancements.

Future Developments in AI

The exploration of model merging presents a promising avenue for future developments in AI. As the demand for precise and efficient models grows, particularly for domain-specific applications, the methodologies presented in the paper could serve as a keystone for future research. Moreover, understanding and harnessing the emergent capabilities could lead to breakthroughs in how we deploy AI across diverse and complex disciplines.

In summary, this paper represents a valuable addition to the growing body of work on LLMs, providing a comprehensive evaluation of fine-tuning strategies and the potential of model merging to enhance AI capabilities. As AI systems become increasingly integral to specialized fields, the techniques and insights gleaned from this research will likely play a pivotal role in shaping future innovations.