Improving the Reasoning of Large Language Models Through Self-Training with Consistency-Driven Rationale Evaluation

Core Concepts

Evaluating the reasoning process of large language models (LLMs) through follow-up questions, and incorporating this evaluation into self-training, enhances the models' reasoning abilities and robustness.

Abstract

• Bibliographic Information: Lee, J., Sakaguchi, K., & Bak, J. (2024). Self-Training Meets Consistency: Improving LLMs’ Reasoning With Consistency-Driven Rationale Evaluation. arXiv preprint arXiv:2411.06387.
• Research Objective: This paper introduces CREST (Consistency-driven Rationale Evaluation for Self-Training), a novel framework designed to enhance the reasoning capabilities of LLMs by evaluating their self-generated rationales through follow-up questions and leveraging this evaluation during self-training.
• Methodology: CREST involves four key stages: 1) Rationale Generation: The LLM generates multiple rationales and answer predictions for a given question. 2) Rationale Evaluation: Each rationale is evaluated based on the correctness of its answer prediction and its performance on answering follow-up questions derived from the original question. 3) Supervised Fine-Tuning: The LLM is fine-tuned on a filtered set of rationales, prioritizing those that demonstrate consistency in answering both the original and follow-up questions. 4) Preference Learning: The fine-tuned model is further trained using a preference learning algorithm, learning to favor rationales that consistently lead to correct answers across both original and follow-up questions.
• Key Findings: Experiments on three reasoning datasets (ReClor, ARC, and CSQA) demonstrate that CREST effectively improves the reasoning abilities of LLMs, outperforming other self-training approaches. The authors find that evaluating rationales through follow-up questions provides a more robust measure of their quality compared to solely relying on the correctness of the final answer.
• Main Conclusions: CREST offers a promising approach to enhance LLM reasoning by incorporating consistency-driven rationale evaluation into the self-training process. The framework encourages the model to learn more robust and generalizable reasoning patterns, leading to improved performance on challenging reasoning tasks.
• Significance: This research contributes to the field of natural language processing by addressing the crucial challenge of improving the reasoning capabilities of LLMs. The proposed CREST framework offers a practical and effective method to enhance the reliability and trustworthiness of LLM-generated rationales.
• Limitations and Future Research: The current study primarily focuses on multiple-choice question-answering tasks. Future research could explore the applicability of CREST to other reasoning tasks, such as mathematical reasoning or open-ended question answering. Additionally, further investigation into the weighting and interpretation of different follow-up questions could lead to even more refined rationale evaluation and training procedures.

Stats

Accuracy of MCREST surpasses other self-training baselines across ReClor, ARC, and CSQA datasets.
In ARC-Challenge and CSQA, peak performance is achieved at a tolerance value (t) of 2, indicating the benefit of excluding less robust rationales from training.
In ReClor, optimal performance is observed at t = 3, suggesting a dataset-specific influence of tolerance on reasoning complexity.
Preference learning with a lambda (λ) value of 0.6, balancing the trade-off between easy and hard sets in ReClor, yields the best overall performance.
CREST leads to improvements across all three FLASK metrics (robustness, correctness, and efficiency) for rationale generation, as evaluated by GPT-4o.

Quotes

Key Insights Distilled From

Self-Training Meets Consistency: Improving LLMs' Reasoning With Consistency-Driven Rationale Evaluation

by Jaehyeok Lee... at arxiv.org 11-12-2024

https://arxiv.org/pdf/2411.06387.pdf

Deeper Inquiries

How might the CREST framework be adapted for tasks involving different data modalities, such as image-based reasoning or reasoning with knowledge graphs?

Adapting CREST for different data modalities presents exciting challenges and opportunities. Here's how it could be approached:
Image-based Reasoning:

Rationale Generation: Instead of text, the LLM would need to generate a sequence of image-focused operations or attention maps as a "rationale". This could involve highlighting regions of interest, drawing bounding boxes, or generating captions that explain the reasoning process.
Follow-up Questions: These would need to be image-based as well. For example, asking the model to identify objects that support or contradict its initial prediction, or to select from a set of images that represent a logical consequence of the scene.
Evaluation: Metrics would need to assess the alignment between the generated rationale (image operations or captions) and the correct reasoning process. This could involve comparing the model's attention to human-annotated regions or evaluating the semantic similarity between generated captions and ground truth explanations.
Knowledge Graph Reasoning:

Rationale Generation: The LLM could generate a sequence of logical inferences or graph traversals as its rationale. This would involve identifying relevant entities and relations in the knowledge graph and explaining how they lead to the final answer.
Follow-up Questions: These could involve querying the knowledge graph for facts that support or contradict the model's reasoning, or asking the model to predict the truth value of related triples.
Evaluation: Metrics would need to assess the validity and soundness of the generated logical inferences or graph traversals. This could involve checking the consistency of the model's reasoning with the knowledge graph or comparing its performance to symbolic reasoning engines.
Key Challenges:

Multimodal Representation Learning: Effectively combining and aligning information from different modalities (e.g., text and images) is crucial for rationale generation and evaluation.
Task-Specific Rationale Structures: The structure and content of rationales will vary significantly across tasks and modalities. Designing appropriate generation and evaluation mechanisms for these diverse structures is essential.

Could the reliance on a pre-trained LLM and the computational cost associated with generating and evaluating multiple rationales pose limitations to the scalability of CREST?

Yes, the reliance on pre-trained LLMs and the computational cost of CREST present scalability challenges:

LLM Size and Inference Time: Larger LLMs, while generally more capable, require significant computational resources and have longer inference times. Generating and evaluating multiple rationales per question amplifies this cost, potentially making CREST impractical for large datasets or real-time applications.
Rationale Generation and Evaluation Overhead: Generating diverse, high-quality rationales can be computationally expensive, especially if exploration techniques like sampling are used. Evaluating these rationales with follow-up questions further adds to the computational burden.
Potential Mitigation Strategies:

Efficient LLM Architectures and Inference: Utilizing efficient LLM architectures (e.g., distilled models, MoE models) or leveraging techniques like quantization and pruning can reduce inference time and memory requirements.
Rationale Sampling and Filtering: Instead of generating a fixed number of rationales, employing adaptive sampling strategies based on initial rationale quality or uncertainty estimates could improve efficiency.
Parallelism and Distributed Computing: Distributing the rationale generation, evaluation, and training processes across multiple GPUs or TPUs can significantly accelerate CREST.
Knowledge Distillation: Training smaller, specialized models to mimic the reasoning capabilities of the larger LLM used in CREST could improve scalability for deployment.

If human reasoning often involves intuition and leaps of logic, how can we develop evaluation metrics that capture these nuances and encourage LLMs to reason more creatively?

Capturing the nuances of human-like reasoning, including intuition and leaps of logic, is a significant challenge in evaluating and encouraging creative reasoning in LLMs. Here are some potential directions:

Beyond Logical Validity: Current metrics primarily focus on logical consistency and correctness. We need to explore metrics that go beyond these aspects to capture:

Novelty and Originality: Rewarding rationales that demonstrate novel connections or insights, even if they involve unconventional reasoning paths.
Intuitive Plausibility: Assessing how well a rationale aligns with human intuition, even if it doesn't follow strict logical rules. This could involve human evaluation or using LLMs trained on human judgments of "intuitive" explanations.
Analogical Reasoning: Evaluating the ability to draw analogies and transfer knowledge from different domains, which often involves leaps of logic.

Process-Oriented Evaluation: Instead of just evaluating the final answer or rationale, we need to analyze the reasoning process itself. This could involve:

Step-wise Evaluation: Assessing the coherence and relevance of each step in the reasoning chain, even if individual steps might not be logically sound in isolation.
Exploration of Alternatives: Encouraging models to generate and compare multiple reasoning paths, rewarding those that explore diverse possibilities.

Incorporating Cognitive Biases: Human reasoning is often influenced by cognitive biases. Incorporating these biases into evaluation metrics could lead to more human-like reasoning. For example, rewarding models that exhibit confirmation bias or availability heuristics in appropriate contexts.

Subjective and Contextual Evaluation: Recognizing that "good" reasoning can be subjective and context-dependent. This might involve:

Human-in-the-Loop Evaluation: Utilizing human judgment to assess the creativity and intuitiveness of generated rationales.
Domain-Specific Metrics: Developing evaluation metrics tailored to specific domains, as what constitutes "creative" reasoning can vary significantly.
Developing such evaluation metrics is an ongoing research area. It requires a deeper understanding of human cognition and the development of novel evaluation paradigms that move beyond traditional notions of logical validity and correctness.