Improving Attributed Text Generation of Large Language Models via Preference Learning

Core Concepts

Large language models can improve text generation by incorporating preference learning for attribution tasks.

Abstract

Large language models face challenges in generating reliable content.
Attribution methods are crucial for providing evidence and credibility in text generation.
The APO framework addresses challenges by modeling attribution as preference learning.
A curated collection is used for post-training, and an automatic method synthesizes attribution preference data.
Progressive preference optimization is proposed for fine-tuning attribution mechanisms.
Extensive experiments show that APO achieves state-of-the-art citation F1 with higher answer quality.
Various datasets are used for evaluation, showcasing the effectiveness of APO in improving text generation.

Stats

Recent works aim to reduce misinformation and hallucinations by resorting to attribution as a means to provide evidence (i.e., citations).
Extensive experiments on three datasets demonstrate that APO achieves state-of-the-art citation F1 with higher answer quality.
Large language models have demonstrated emergent abilities and have gained widespread application in Natural Language Processing (NLP).

Quotes

"We address these challenges by conceptualizing the attribution task for LLMs as preference learning and proposing an Automatic Preference Optimization (APO) framework."
"Extensive experiments on three datasets demonstrate that APO achieves state-of-the-art citation F1 with higher answer quality."

Key Insights Distilled From

Improving Attributed Text Generation of Large Language Models via Preference Learning

by Dongfang Li,... at arxiv.org 03-28-2024

https://arxiv.org/pdf/2403.18381.pdf

Deeper Inquiries

How can the APO framework be adapted for other applications beyond text generation?

The APO framework can be adapted for various applications beyond text generation by leveraging the concept of preference learning. One way to adapt it is in the field of recommendation systems. In recommendation systems, the preferences of users can be used to optimize the recommendations provided. By applying the APO framework, the system can learn from user preferences and improve the quality of recommendations over time. Additionally, in personalized medicine, the APO framework can be utilized to learn from patient preferences and optimize treatment plans tailored to individual needs. This can lead to more effective and personalized healthcare solutions. Furthermore, in the field of autonomous vehicles, the APO framework can be used to learn from driver preferences and optimize driving behavior to enhance safety and comfort for passengers.

What are potential counterarguments to the effectiveness of preference learning in improving text generation?

One potential counterargument to the effectiveness of preference learning in improving text generation is the subjectivity of preferences. Preferences can vary greatly among individuals, and what one person considers a high-quality text may differ from another person's perspective. This subjectivity can make it challenging to generalize preference learning models across a diverse set of users. Additionally, the quality of the preference data used to train the model can impact its effectiveness. Biased or incomplete preference data may lead to suboptimal results in text generation. Moreover, the interpretability of preference learning models can be a concern. Understanding how the model incorporates preferences and makes decisions may be complex and challenging to explain, leading to potential trust issues among users.

How can the concept of preference learning be applied to unrelated fields to enhance understanding and decision-making?

The concept of preference learning can be applied to unrelated fields to enhance understanding and decision-making by capturing user preferences and optimizing outcomes based on those preferences. In e-commerce, preference learning can be used to personalize product recommendations, leading to increased customer satisfaction and sales. In healthcare, preference learning can help tailor treatment plans to patient preferences, improving patient outcomes and adherence to treatment. In finance, preference learning can be utilized to optimize investment strategies based on individual risk tolerance and financial goals. In education, preference learning can assist in personalized learning paths for students, catering to their learning styles and preferences. Overall, applying preference learning in various fields can lead to more tailored and effective solutions that align with user preferences and needs.