Can Small Language Models Enhance Reasoning in Large Language Models?

核心概念

Leveraging a lightweight language model to guide a large language model in reasoning tasks can improve the quality of generated rationales and enhance overall task performance.

摘要

The content introduces a novel framework called LM-Guided Chain-of-Thought (CoT) that utilizes two independent language models (LMs) - a small LM for rationale generation and a large LM for answer prediction.
The key steps are:

Rationale Distillation: The small LM is trained using knowledge distillation to learn reasoning capabilities from the large LM.

Rationale Refinement: The small LM's rationales are further optimized using reinforcement learning based on 8 rationale quality aspects (factuality, relevance, logicality, consistency, coherence, fluency, naturalness, readability).

The authors conduct experiments on multi-hop question answering tasks using HotpotQA and 2WikiMultiHopQA datasets. The results show that the LM-Guided CoT approach outperforms standard prompting and the original CoT prompting, especially in terms of answer prediction accuracy and rationale quality. The reinforcement learning step also contributes to slight improvements in both rationale quality and task performance.
The authors also find that selecting the highest-quality rationales does not always guarantee improved task performance, highlighting the need to balance rationale utilities and overall task objectives.

統計資料

The authors report the following key figures:

The standard prompting approach achieves EM scores of 0.5 and 0.5 on HotpotQA and 2WikiMultiHopQA respectively.
The original CoT prompting approach achieves EM scores of 0.483 and 0.4 on HotpotQA and 2WikiMultiHopQA respectively.
The LM-Guided CoT prompting with knowledge distillation (KD) achieves EM scores of 0.507 and 0.506 on HotpotQA and 2WikiMultiHopQA respectively.
The LM-Guided CoT prompting with KD and self-consistency (SC) decoding achieves the highest EM scores of 0.513 and 0.524 on HotpotQA and 2WikiMultiHopQA respectively.

引述

"LM-guided CoT prompting outperforms both the standard prompting and the original CoT prompting."
"We find that (1) LM-guided CoT with KD and self-consistency (SC) decoding strategy maximizes the performance gain; (2) RL contributes to a slight increase in overall rationale quality and task performance; (3) choosing the highest-quality rationales for the large LM does not always guarantee improved task performance."

從以下內容提煉的關鍵洞見

Can Small Language Models Help Large Language Models Reason Better?

by Jooyoung Lee... 於 arxiv.org 04-05-2024

https://arxiv.org/pdf/2404.03414.pdf

深入探究

How can the LM-Guided CoT framework be extended to other reasoning-intensive tasks beyond multi-hop question answering?

The LM-Guided CoT framework can be extended to other reasoning-intensive tasks by adapting the rationale generation and answer prediction components to suit the specific requirements of different tasks. For instance, in tasks like logical reasoning or decision-making, the rationale generation process can be tailored to focus on generating logical steps or justifications for a particular decision. The answer prediction component can then be modified to consider these generated rationales in the context of the task at hand.
Additionally, the framework can be applied to tasks that involve complex problem-solving, such as puzzle-solving or strategic planning. In these cases, the small LM can be trained to generate reasoning steps that lead to a solution or a strategic move, while the large LM can predict the final outcome based on these generated rationales.
Furthermore, the framework can be utilized in domains like scientific research or legal analysis, where reasoning and justification are crucial. The small LM can generate rationales based on evidence or legal precedents, and the large LM can use these rationales to make informed decisions or predictions.
Overall, by customizing the rationale generation and answer prediction processes to suit the specific requirements of different reasoning-intensive tasks, the LM-Guided CoT framework can be effectively extended beyond multi-hop question answering.

What are the potential limitations or drawbacks of relying on a separate small LM for rationale generation compared to directly optimizing the large LM?

While the LM-Guided CoT framework offers several advantages, there are potential limitations and drawbacks to relying on a separate small LM for rationale generation instead of directly optimizing the large LM:

Complexity and Integration: Using two separate LMs adds complexity to the system and requires integration between the two models. This integration process can be challenging and may introduce additional overhead.

Consistency and Alignment: There may be inconsistencies between the reasoning generated by the small LM and the predictions made by the large LM. Ensuring alignment and coherence between the two models can be difficult and may impact overall performance.

Resource Allocation: Training and maintaining two separate models can be resource-intensive. It requires additional computational resources, storage, and training time compared to optimizing a single large LM.

Transferability: The rationales generated by the small LM may not always transfer effectively to the large LM for answer prediction. This transferability issue can affect the overall performance of the system.

Scalability: Scaling the framework to larger datasets or more complex tasks may pose challenges when relying on a separate small LM for rationale generation. The scalability of the system may be limited by the capabilities of the small LM.

Model Interpretability: Having two separate models for rationale generation and answer prediction can make it harder to interpret the decision-making process. Understanding how the models arrive at their conclusions may be more complex in this setup.

Overall, while using a separate small LM for rationale generation offers certain advantages, it also comes with potential limitations and drawbacks that need to be carefully considered and addressed.

Given the finding that selecting the highest-quality rationales does not always improve task performance, what other strategies could be explored to better align the rationale quality and the overall task objectives?

To better align the rationale quality with the overall task objectives, several strategies can be explored beyond selecting the highest-quality rationales:

Diverse Rationale Sampling: Instead of focusing solely on the highest-quality rationales, a diverse set of rationales can be sampled and used for answer prediction. This approach can provide a broader perspective and reduce the risk of bias from selecting only a subset of rationales.

Ensemble Rationale Aggregation: Combining multiple rationales generated by the small LM through ensemble methods can help capture different perspectives and reasoning paths. Aggregating these diverse rationales can lead to more robust and comprehensive predictions.

Dynamic Rationale Weighting: Assigning different weights to rationales based on their quality and relevance to the task can be explored. Adaptive weighting mechanisms can prioritize rationales that are more likely to lead to accurate predictions.

Feedback Mechanisms: Introducing feedback loops where the model receives feedback on the quality of its generated rationales can help improve the rationale generation process iteratively. This continuous feedback can guide the model towards generating more relevant and accurate rationales.

Multi-Stage Reasoning: Implementing a multi-stage reasoning approach where the small LM generates initial rationales, which are then refined or expanded upon by the large LM, can enhance the overall reasoning process. This multi-stage approach can leverage the strengths of both models effectively.

Domain-Specific Rationale Generation: Tailoring the rationale generation process to specific domains or tasks can improve the relevance and quality of the generated rationales. Domain-specific knowledge and constraints can be incorporated into the rationale generation process to align it more closely with the task objectives.

By exploring these alternative strategies, it is possible to enhance the alignment between rationale quality and task performance, ultimately improving the effectiveness of the LM-Guided CoT framework.