TriSum: Learning Summarization Ability from Large Language Models with Structured Rationale

Conceitos essenciais

Distilling LLMs' text summarization abilities into a compact, local model enhances performance and interpretability.

Resumo

TriSum introduces a framework to distill large language models' (LLMs) text summarization capabilities into a smaller, more efficient local model. By extracting aspect-triple rationales and summaries from LLMs, refining them using dual-scoring methods, and training a local model with curriculum learning, TriSum outperforms baselines on various benchmarks. This approach not only improves performance but also enhances interpretability by providing insights into the summarization rationale. The study aims to streamline powerful summarization models in resource-limited contexts while leveraging LLMs' inherent abilities.

Estatísticas

Our method enhances local model performance on various benchmarks (CNN/DailyMail, XSum, and ClinicalTrial), outperforming baselines by 4.5%, 8.5%, and 7.4%, respectively.
Recent research has taken transformer architecture further for summarization tasks utilizing LLMs such as ChatGPT, GPT-4, and PaLM which have billions of parameters.
Knowledge distillation has been applied across various fields to transfer knowledge from large models to smaller ones for usability in resource-limited environments.

Citações

"TriSum introduces a new approach that distills LLMs’ abstractive text summarization power into a small local model."
"Our method enhances local model performance on various benchmarks, outperforming baselines significantly."
"Through extensive experiments we show that incorporating LLM-generated rationales boosts our local model’s summarization performance."

Principais Insights Extraídos De

TriSum

by Pengcheng Ji... às arxiv.org 03-18-2024

https://arxiv.org/pdf/2403.10351.pdf

Perguntas Mais Profundas

How can the TriSum framework be adapted for other natural language processing tasks beyond text summarization?

The TriSum framework's adaptability to other NLP tasks lies in its core principles of distilling LLM capabilities into smaller local models through rationale probing, golden rationale selection, and curriculum learning. This approach can be extended to tasks like machine translation by prompting LLMs for aspect-triple rationales related to translation quality and coherence. For question-answering systems, the framework could extract essential aspects from questions and relevant information from documents to generate accurate responses. In sentiment analysis, TriSum could identify key aspects influencing sentiment in texts and distill this knowledge into a compact model for classification purposes. By adjusting the input prompts and training data specifics, the same methodology used in text summarization within TriSum can be applied creatively across various NLP domains.

What are the potential drawbacks or limitations of distilling LLMs' capabilities into smaller local models?

While distilling LLM capabilities into smaller local models offers benefits such as reduced computational demands and enhanced privacy due to localized processing, there are several potential drawbacks and limitations to consider:

Loss of Complexity: Smaller models may not capture all nuances present in large language models, potentially leading to a loss of performance on complex tasks.
Overfitting: Training a local model solely based on distilled knowledge from an LLM may result in overfitting to specific datasets or scenarios.
Generalization Challenges: The distilled model might struggle with generalizing well beyond the scope of the training data it was exposed to during distillation.
Interpretability vs Performance Trade-off: Simplifying complex reasoning processes from LLMs into more interpretable rationales could come at the cost of overall performance on certain tasks that require intricate decision-making.

How might the concept of rationale generation impact future development of AI models in interpretability?

Rationale generation plays a crucial role in enhancing AI model interpretability by providing insights into decision-making processes. This concept impacts future AI model development in several ways:

Explainable AI: Rationales offer transparency by explaining why an AI system made specific decisions or predictions, increasing trustworthiness.
Error Analysis: Understanding generated rationales helps identify errors or biases within AI systems, enabling targeted improvements.
Human-AI Collaboration: Rationales facilitate collaboration between humans and AI systems as they provide understandable justifications for outputs.
Regulatory Compliance: Rationale generation aligns with regulatory requirements mandating explainable AI systems for sensitive applications like healthcare or finance.

In conclusion, integrating rationale generation techniques will likely drive advancements towards more interpretable and trustworthy AI models across various industries requiring transparent decision-making processes..