Enabling Small Language Models to Perform Step-by-Step Reasoning through Symbolic Chain-of-Thought Distillation

Concetti Chiave

Smaller language models can be trained to perform step-by-step reasoning through a process called Symbolic Chain-of-Thought Distillation, where they learn to generate coherent and effective chain-of-thoughts by distilling from a larger teacher model.

Sintesi

This paper introduces Symbolic Chain-of-Thought Distillation (SCoTD), a method to enable smaller language models (125M-1.3B parameters) to perform step-by-step reasoning, a capability typically only exhibited by much larger models.
The key insights are:

Sampling multiple chain-of-thought (CoT) demonstrations per input instance from a large teacher model (e.g., GPT-3) and using them to fine-tune a smaller student model is an effective strategy. Sampling 30 CoTs per instance is found to be particularly beneficial.

The student model trained with SCoTD can outperform the supervised fine-tuning baseline, especially on challenging contrast sets and unseen tasks. This suggests that learning with explanations can support more robust generalization.

Ablation studies show that the sheer volume of the sampled CoTs is a key contributing factor, rather than specific properties like diversity or teacher likelihood. A simple random downsampling performs reasonably well.

Human evaluations indicate that the student model's generated CoTs are comparable in quality to the much larger teacher model, despite the student having 100x fewer parameters.

The paper demonstrates the effectiveness of SCoTD across several commonsense QA benchmarks, different student model sizes, and in both supervised and few-shot settings. The results highlight the potential of distilling reasoning capabilities from large to small models.

Statistiche

The student model (OPT-1.3B) achieves 67.0% accuracy on OpenBookQA, compared to 2.8% without CoT.
The student model (OPT-1.3B) achieves 83.8% accuracy on QuaRel, compared to 9.7% without CoT.
The student model (OPT-1.3B) achieves 67.0% accuracy on CommonsenseQA, compared to 20.5% without CoT.

Citazioni

"Sampling many reasoning chains per instance from the teacher is paramount."
"After distillation, student chain-of-thoughts are judged by humans as comparable to the teacher, despite orders of magnitude fewer parameters."

Approfondimenti chiave tratti da

Symbolic Chain-of-Thought Distillation: Small Models Can Also "Think" Step-by-Step

by Liunian Haro... alle arxiv.org 04-17-2024

https://arxiv.org/pdf/2306.14050.pdf

Domande più approfondite

How can we further improve the quality and diversity of the chain-of-thought samples generated by the teacher model to enhance the student's learning?

To enhance the quality and diversity of chain-of-thought samples for improved student learning, several strategies can be implemented:

Fine-tuning Teacher Model: Continuously fine-tune the teacher model on diverse datasets to expose it to a wide range of reasoning patterns and scenarios, leading to more varied and high-quality chain-of-thought samples.

Ensemble Methods: Utilize ensemble methods to combine multiple teacher models to generate a more diverse set of rationales, capturing a broader spectrum of reasoning strategies.

Data Augmentation: Introduce data augmentation techniques to the training data used for generating chain-of-thought samples, such as paraphrasing, adding noise, or introducing variations in the input instances to encourage the teacher model to produce diverse explanations.

Curriculum Learning: Implement a curriculum learning approach where the difficulty of the input instances is gradually increased, challenging the teacher model to generate more complex and varied chain-of-thought samples.

Human-in-the-Loop: Incorporate human feedback to evaluate and guide the generation of chain-of-thought samples, ensuring that the explanations are coherent, relevant, and diverse.

By incorporating these strategies, the quality and diversity of chain-of-thought samples can be enhanced, leading to more effective learning for the student model.

What other types of knowledge, beyond step-by-step reasoning, can be effectively distilled from large to small models using similar techniques?

Beyond step-by-step reasoning, various types of knowledge can be distilled from large to small models using similar techniques:

Causal Reasoning: Large models can distill the ability to understand causal relationships between events or variables, enabling smaller models to make inferences based on cause and effect.

Temporal Reasoning: Knowledge about temporal sequences and dependencies can be distilled, allowing smaller models to comprehend and reason about time-related information effectively.

Domain-Specific Knowledge: Large models can transfer domain-specific knowledge, such as medical expertise or legal principles, to smaller models, enabling them to perform specialized tasks within those domains.

Multi-hop Reasoning: Complex reasoning involving multiple steps or hops can be distilled, enhancing the smaller models' capability to connect disparate pieces of information to arrive at a conclusion.

Commonsense Knowledge: Distilling commonsense knowledge, such as understanding everyday scenarios and social norms, can help smaller models make more human-like decisions and predictions.

By leveraging similar distillation techniques, a wide range of knowledge types can be effectively transferred from large to small models, expanding their capabilities across various domains and tasks.

Can the SCoTD approach be extended to other modalities beyond text, such as vision or multimodal tasks?

Yes, the Symbolic Chain-of-Thought Distillation (SCoTD) approach can be extended to other modalities beyond text, including vision and multimodal tasks. Here's how it can be applied:

Vision Tasks: For vision tasks, a large vision model can generate visual explanations or reasoning chains for image understanding. These explanations can be distilled into smaller vision models to enhance their interpretability and reasoning capabilities.

Multimodal Tasks: In multimodal tasks combining text and images, a large multimodal model can provide explanations that bridge the gap between modalities. By distilling these multimodal explanations, smaller models can learn to integrate information from different sources effectively.

Audio Tasks: For audio tasks, a large model can generate chain-of-thought explanations for speech recognition or sound classification. Distilling this knowledge can help smaller models improve their performance on audio-related tasks.

Sensor Data Fusion: In scenarios involving sensor data fusion, where information from multiple sensors needs to be integrated, SCoTD can be used to distill the reasoning process from a large model to smaller models for efficient fusion and decision-making.

By extending the SCoTD approach to other modalities, models can benefit from rich explanations and reasoning strategies across a wide range of tasks, leading to enhanced performance and interpretability in various domains.