Distilling Large Language Models for Reasoning: Focusing on Decomposition for Efficiency and Generalization
Core Concepts
Distilling the decomposition capability of large language models (LLMs) offers a cost-effective and generalizable approach to improve reasoning in smaller models, while distilling the solving capability proves less effective and less generalizable.
Abstract
- Bibliographic Information: Wu, Z., Bai, H., Zhang, A., Gu, J., Vydiswaran, V. G. V., Jaitly, N., & Zhang, Y. (2024). Divide-or-Conquer? Which Part Should You Distill Your LLM? arXiv preprint arXiv:2402.15000v2.
- Research Objective: This paper investigates whether it is more effective to distill the decomposition capability or the solving capability of large language models (LLMs) for reasoning tasks. The authors hypothesize that decomposition, being less knowledge-intensive, is easier to distill and more generalizable than solving.
- Methodology: The researchers propose a two-stage framework for reasoning, separating decomposition and solving. They distill the decomposition capability of a teacher LLM (GPT-3.5-turbo) into smaller student models (Vicuna-13B and Mistral-7B) using demonstrations. They evaluate the performance and inference cost of using the distilled decomposers with different solvers on various reasoning datasets (GSM8K, DROP, Bamboogle).
- Key Findings: The study finds that distilling the decomposition capability is indeed easier and more effective than distilling the solving capability. Distilled decomposers achieve comparable or even better performance than the teacher model while significantly reducing inference costs. Moreover, they exhibit strong generalization across different tasks, datasets, and solvers. In contrast, distilling the solving capability leads to a significant performance drop and poor generalization.
- Main Conclusions: The authors conclude that focusing on distilling the decomposition capability of LLMs is a promising direction for achieving cost-efficient and generalizable reasoning in smaller models. They suggest that this approach can be particularly beneficial for domains where labeled data is scarce and local adaptation is crucial.
- Significance: This research contributes to the field of LLM distillation by providing empirical evidence for the feasibility and benefits of distilling specific reasoning capabilities. It highlights the importance of separating decomposition and solving in reasoning tasks and offers a practical approach for leveraging the power of LLMs in resource-constrained settings.
- Limitations and Future Research: The study primarily focuses on math and QA tasks. Future research could explore the effectiveness of this approach in other reasoning tasks, such as tool use, LLM agents, and multi-turn decision making. Additionally, investigating the use of reinforcement learning to further enhance the decomposer based on solver feedback is a promising avenue.
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Divide-or-Conquer? Which Part Should You Distill Your LLM?
Stats
The cost for Vicuna-13B is approximately $7.42∗10−5 for 1000 input tokens and $1.48∗10−4 for 1000 output tokens.
Dynamic planning is ×3.96 and ×2.32 more expensive than static planning for the number of input tokens on Bamboogle and GSM8K, respectively.
Quotes
"We hypothesize that the decomposition should be easier to distill into a smaller model compared to the problem solving because the latter requires large amounts of domain knowledge while the former only requires learning general problem solving strategies."
"We find that we can distill the problem decomposition phase and at the same time achieve good generalization across tasks, datasets, and models. However, it is harder to distill the problem solving capability without losing performance and the resulting distilled model struggles with generalization."
Deeper Inquiries
How can the proposed decomposition-focused distillation approach be extended to more complex reasoning tasks involving tool use or multi-modal inputs?
Extending the decomposition-focused distillation approach to more complex reasoning tasks like those involving tool use or multi-modal inputs presents exciting challenges and opportunities. Here's a breakdown of potential strategies:
1. Tool Use:
Augmenting Sub-question Generation: The instruction for decomposition (Idecomp) can be modified to incorporate tool awareness. Instead of just generating sub-questions, the student model can be trained to produce sub-tasks that explicitly include tool calls. For example:
Original: "What is the capital of France?"
Tool-aware: "Use a knowledge base lookup tool to find the capital of France."
Intermediate Representation: Introduce an intermediate representation layer that maps sub-questions to specific tools. This layer can be trained using demonstrations from the teacher model, where tool usage is explicitly labeled.
Reinforcement Learning: Fine-tune the student model using reinforcement learning, where rewards are provided for selecting the correct tool and successfully completing the sub-task.
2. Multi-modal Inputs:
Joint Encoding: Utilize multi-modal encoders that can process both text and other modalities (e.g., images, videos) to represent the input context. The student model can then learn to decompose the query based on this joint representation.
Modality-Specific Decomposition: Train separate decomposers for different modalities. For example, an image decomposer could identify relevant regions or objects, while a text decomposer focuses on textual information. These decompositions can then be fused to form a comprehensive set of sub-tasks.
Attention Mechanisms: Employ attention mechanisms to allow the model to focus on relevant parts of the multi-modal input while generating sub-questions. This can help the model to better understand the relationships between different modalities and generate more meaningful decompositions.
Challenges:
Data Requirements: Training effective decomposers for tool use and multi-modal inputs would require large-scale datasets with rich annotations of tool usage and multi-modal alignments.
Evaluation: Evaluating the performance of these models would necessitate new benchmarks and metrics that capture the complexity of tool use and multi-modal reasoning.
Could the performance gap observed when distilling the solving capability be bridged by employing more advanced distillation techniques or larger student models?
While the paper demonstrates the effectiveness of distilling the decomposition capability, distilling the solving capability proves more challenging. It's plausible that more advanced techniques and larger student models could help bridge this performance gap:
1. Advanced Distillation Techniques:
Intermediate Task Distillation: Instead of directly distilling the final answer, break down the solving process into intermediate tasks and distill knowledge at each step. This can provide a more structured learning process for the student model.
Multi-teacher Distillation: Leverage multiple teacher models with diverse strengths and expertise to provide a richer learning signal for the student.
Reinforcement Learning for Distillation: Employ reinforcement learning algorithms to fine-tune the student model, using rewards based on the similarity of its outputs to the teacher's outputs.
2. Larger Student Models:
Scaling Up: Increasing the size of the student model can enhance its capacity to retain and utilize the distilled knowledge.
Specialized Architectures: Exploring student models with architectures specifically designed for knowledge-intensive tasks, such as models with larger memory capacities or enhanced retrieval mechanisms.
Trade-offs:
Complexity and Cost: More advanced distillation techniques and larger student models often come with increased computational complexity and training costs.
Diminishing Returns: There might be diminishing returns in performance improvement as the student model size approaches that of the teacher model.
Beyond Distillation:
Hybrid Approaches: Combining distillation with other techniques like fine-tuning on task-specific datasets or incorporating external knowledge sources could lead to more robust solving capabilities.
What are the ethical implications of developing highly capable yet compact language models, particularly in terms of accessibility and potential misuse?
Developing highly capable yet compact language models raises significant ethical considerations, particularly regarding accessibility and potential misuse:
Accessibility:
Democratization of AI: Smaller models can be deployed on less powerful hardware, potentially democratizing access to advanced AI capabilities for individuals and organizations with limited resources.
Exacerbating Inequalities: If access to these models or the resources to train them remains concentrated in the hands of a few, it could exacerbate existing inequalities in AI development and deployment.
Potential Misuse:
Lower Barrier to Entry for Malicious Actors: The reduced computational requirements of smaller models could lower the barrier to entry for malicious actors seeking to exploit them for harmful purposes, such as generating misinformation or crafting more convincing phishing attacks.
Amplification of Biases: If not carefully mitigated, biases present in the training data can be amplified in smaller models, leading to unfair or discriminatory outcomes.
Over-reliance and Automation Bias: The ease of deployment and apparent competence of these models could lead to over-reliance and automation bias, potentially resulting in poor decision-making or unintended consequences.
Mitigations:
Responsible Development Guidelines: Establishing clear ethical guidelines and best practices for the development and deployment of compact language models.
Bias Detection and Mitigation: Investing in robust bias detection and mitigation techniques to ensure fairness and equity in model outputs.
Transparency and Explainability: Promoting transparency in model development and striving for explainable AI to foster trust and accountability.
Regulation and Policy: Exploring appropriate regulatory frameworks and policies to govern the development and use of these powerful technologies.
Addressing these ethical implications proactively is crucial to ensure that the development of highly capable yet compact language models benefits society while minimizing potential harms.