Enhancing Factuality in Large Language Model Alignment Through Factuality-Aware Supervised Fine-Tuning and Direct Preference Optimization

Core Concepts

Factuality-aware alignment, comprising factuality-aware supervised fine-tuning (SFT) and direct preference optimization (DPO), can guide large language models to generate more factual responses while maintaining their instruction-following capability.

Abstract

The paper studies the underlying causes of hallucination in the standard alignment process for large language models (LLMs) and proposes a factuality-aware alignment approach to address the issue. The key findings are: The supervised fine-tuning (SFT) stage may inadvertently encourage hallucination by fine-tuning the LLM on human-created responses that contain new or unknown information to the model. The reinforcement learning (RL) stage with standard reward functions often prefers longer and more detailed responses, which tend to stimulate the LLM to yield more false claims. To tackle these issues, the authors propose factuality-aware alignment (FLAME), which comprises: Factuality-aware SFT: For fact-based instructions, the LLM is fine-tuned on its own generated responses to avoid introducing unknown information. For non-fact-based instructions, human-created responses are used. Factuality-aware DPO: In addition to the standard instruction-following reward, a separate factuality reward model is used to create preference pairs that explicitly optimize for factuality, especially for fact-based instructions. Experiments show that FLAME can guide LLMs to output more factual responses while maintaining their instruction-following capability, outperforming the standard alignment process.

Stats

"LLMs are still prone to generate false claims (hallucination)." "Fine-tuning LLMs on diverse instructions paired with human-created high-quality responses may inadvertently promote hallucination." "Standard reward functions in RL often prefer longer and more detailed responses, which tends to stimulate the LLM to yield more false claims."

Quotes

"Alignment is a standard procedure to make pre-trained large language models (LLMs) (Brown et al., 2020; Touvron et al., 2023) follow natural language instructions and serve as helpful AI assistants." "We have observed, however, that the conventional alignment process fails to enhance the factual accuracy of LLMs, and often leads to the generation of more false facts (i.e. hallucination)." "Our ultimate goal is to improve the factuality of the standard alignment process, which is challenging since LLMs may be given diverse and complex instructions."

Key Insights Distilled From

FLAME: Factuality-Aware Alignment for Large Language Models

by Sheng-Chieh ... at arxiv.org 05-03-2024

https://arxiv.org/pdf/2405.01525.pdf

Deeper Inquiries

How can the factuality-aware alignment approach be extended to optimize for multiple alignment skill sets beyond just factuality and instruction-following?

To extend the factuality-aware alignment approach to optimize for multiple alignment skill sets, we can introduce additional reward models that focus on specific alignment objectives. For example, we can incorporate reward models for logical reasoning, problem-solving, creativity, and other relevant skills. Each reward model would evaluate the model's responses based on the specific skill set it aims to optimize. By combining these multiple reward models, we can guide the large language models (LLMs) to excel in various alignment tasks beyond factuality and instruction-following. Furthermore, we can implement a hierarchical reward system where different reward models operate at different levels of abstraction. The lower-level reward models can focus on specific alignment skills, while higher-level reward models can integrate these skills to optimize for more complex alignment objectives. This hierarchical approach allows for a more nuanced optimization process that considers multiple alignment skill sets simultaneously.

What are the potential trade-offs between factuality and other alignment objectives, and how can they be balanced effectively?

One potential trade-off between factuality and other alignment objectives, such as instruction-following or creativity, is that optimizing for one objective may come at the expense of the other. For example, focusing too much on factuality may lead to overly cautious responses that lack creativity or engagement. On the other hand, prioritizing instruction-following or creativity may result in the generation of more false facts or inaccurate information. To balance these objectives effectively, it is essential to design a comprehensive reward system that considers the relative importance of each alignment objective. By assigning appropriate weights to each reward model based on the desired outcome, we can guide the LLMs to achieve a balance between factuality and other alignment goals. Additionally, incorporating a diverse set of training data that covers a wide range of alignment tasks can help LLMs develop a more holistic understanding of how to optimize for multiple objectives simultaneously. Regular monitoring and fine-tuning of the reward system based on the model's performance on various tasks can also help maintain a balance between factuality and other alignment objectives. Continuous evaluation and adjustment of the reward models ensure that the LLMs are incentivized to produce accurate, informative, and engaging responses across different alignment skill sets.

How can the factuality reward model be further improved to better distinguish fact-based and non-fact-based sentences in a response, and what are the implications of such improvements on the overall alignment process?

To enhance the factuality reward model's ability to distinguish fact-based and non-fact-based sentences in a response, several strategies can be implemented: Fine-grained Factuality Scoring: Develop a more nuanced scoring system that evaluates the factuality of individual sentences within a response. This approach allows for a more granular assessment of factual accuracy and can help identify specific segments that contain false claims. Contextual Understanding: Incorporate contextual information and domain-specific knowledge to better assess the factual accuracy of sentences. By considering the context in which the information is presented, the model can make more informed judgments about the veracity of the content. Multi-Modal Verification: Integrate multi-modal verification techniques, such as cross-referencing information from reliable sources or fact-checking databases, to validate the accuracy of statements within responses. This approach enhances the factuality assessment by leveraging external sources of information. Machine Learning Techniques: Utilize advanced machine learning algorithms, such as deep learning models or reinforcement learning, to train the factuality reward model on a diverse set of data and improve its ability to distinguish between fact-based and non-fact-based sentences. Improving the factuality reward model's capability to differentiate between fact-based and non-fact-based sentences has significant implications for the overall alignment process. It enhances the model's accuracy in generating factual responses while minimizing the risk of propagating false information. By ensuring that the LLMs prioritize factuality in their outputs, the alignment process becomes more reliable, trustworthy, and aligned with the intended objectives of providing accurate and informative responses.