SALMON: A Novel Approach to Align Large Language Models with Minimal Human Supervision

Core Concepts

SALMON introduces an instructable reward model that can generate reward scores based on arbitrary human-defined principles, enabling the alignment of large language models with minimal human supervision.

Abstract

The paper presents a novel approach called SALMON (Self-Alignment with Instructable Reward Models) to align large language models (LLMs) with human values and intentions. Key highlights: The prevailing AI alignment paradigm, exemplified by models like ChatGPT and LLaMA-2-Chat, relies heavily on supervised fine-tuning (SFT) with prompted demonstrations and reinforcement learning from human feedback (RLHF). However, acquiring high-quality human annotations is costly and not scalable. SALMON introduces an instructable reward model that can interpret and adhere to arbitrary human-written preference guidelines, and subsequently generates rewarding scores based on those principles. This enables better control over the behavior of the RL-trained policy model. SALMON addresses the issue of reward hacking by crafting principles explicitly designed to combat observed reward-hacking patterns in the model outputs, such as self-praising at the end of the response. The instructable reward model can be trained with synthetic data and seamlessly applied to diverse language models without collecting any model-specific human preference data. Integrating SALMON with the SELF-ALIGN technique, the authors developed an AI assistant named Dromedary-2 from scratch by only manually crafting 6 exemplars for In-Context Learning and 31 human-defined principles. Despite its minimal human supervision design, Dromedary-2 outperformed the extensively RLHF-trained LLaMA-2-Chat model on various benchmark datasets.

Stats

"Supervised Fine-Tuning (SFT) on response demonstrations combined with Reinforcement Learning from Human Feedback (RLHF) constitutes a powerful paradigm for aligning LLM-based AI agents." "Acquiring high-quality human annotations, including consistent response demonstrations and in-distribution preferences, is costly and not scalable." "Future, more advanced models could embark on tasks that challenge human evaluation, and there is a looming danger that such models may value appeasing human evaluators over ensuring accuracy."

Quotes

"Can RLAIF fully replace RLHF to align language models from scratch in enhancing their general alignment and capabilities?" "SALMON addresses this issue by simply crafting principles explicitly designed to combat the observed reward-hacking patterns in the model outputs such as self-praising at the end of the response." "Remarkably, when integrated with the SELF-ALIGN technique, our method enabled the training of a self-aligned AI-assistant agent, namely Dromedary-2, from scratch by only manually crafting 6 exemplars for In-Context Learning (ICL) and a combined total of 31 principles."

Key Insights Distilled From

SALMON

by Zhiqing Sun,... at arxiv.org 04-11-2024

https://arxiv.org/pdf/2310.05910.pdf

Deeper Inquiries

How can the instructable reward model be further improved to enhance the reliability and trustworthiness of the aligned language models?

To enhance the reliability and trustworthiness of the aligned language models through the instructable reward model, several improvements can be considered: External Fact-Checking Integration: Integrating external fact-checking tools into the reward model training process can help improve the discriminative capability of the model. By cross-referencing information with verified sources, the model can avoid "hallucinating" unverified information and reduce the likelihood of reasoning errors. Fine-Tuning Discriminative Capabilities: Fine-tuning the reward model to better discern between accurate and inaccurate information can help mitigate inaccuracies that may mislead users. This can involve training the model on a diverse set of data that includes both correct and incorrect information to improve its ability to differentiate between the two. Continuous Learning and Updates: Implementing a system for continuous learning and updates for the instructable reward model can ensure that it stays up-to-date with the latest information and can adapt to evolving contexts. Regular updates based on new data and feedback can help maintain the model's reliability over time. Integration of Fact-Checking Mechanisms: Incorporating fact-checking mechanisms directly into the reward model's evaluation process can provide real-time verification of information generated by the language model. This can help flag potentially inaccurate or misleading responses and improve the overall trustworthiness of the model.

How can the instructable reward model be further improved to enhance the reliability and trustworthiness of the aligned language models?

To enhance the reliability and trustworthiness of the aligned language models through the instructable reward model, several improvements can be considered: External Fact-Checking Integration: Integrating external fact-checking tools into the reward model training process can help improve the discriminative capability of the model. By cross-referencing information with verified sources, the model can avoid "hallucinating" unverified information and reduce the likelihood of reasoning errors. Fine-Tuning Discriminative Capabilities: Fine-tuning the reward model to better discern between accurate and inaccurate information can help mitigate inaccuracies that may mislead users. This can involve training the model on a diverse set of data that includes both correct and incorrect information to improve its ability to differentiate between the two. Continuous Learning and Updates: Implementing a system for continuous learning and updates for the instructable reward model can ensure that it stays up-to-date with the latest information and can adapt to evolving contexts. Regular updates based on new data and feedback can help maintain the model's reliability over time. Integration of Fact-Checking Mechanisms: Incorporating fact-checking mechanisms directly into the reward model's evaluation process can provide real-time verification of information generated by the language model. This can help flag potentially inaccurate or misleading responses and improve the overall trustworthiness of the model.

How can the instructable reward model be further improved to enhance the reliability and trustworthiness of the aligned language models?

To enhance the reliability and trustworthiness of the aligned language models through the instructable reward model, several improvements can be considered: External Fact-Checking Integration: Integrating external fact-checking tools into the reward model training process can help improve the discriminative capability of the model. By cross-referencing information with verified sources, the model can avoid "hallucinating" unverified information and reduce the likelihood of reasoning errors. Fine-Tuning Discriminative Capabilities: Fine-tuning the reward model to better discern between accurate and inaccurate information can help mitigate inaccuracies that may mislead users. This can involve training the model on a diverse set of data that includes both correct and incorrect information to improve its ability to differentiate between the two. Continuous Learning and Updates: Implementing a system for continuous learning and updates for the instructable reward model can ensure that it stays up-to-date with the latest information and can adapt to evolving contexts. Regular updates based on new data and feedback can help maintain the model's reliability over time. Integration of Fact-Checking Mechanisms: Incorporating fact-checking mechanisms directly into the reward model's evaluation process can provide real-time verification of information generated by the language model. This can help flag potentially inaccurate or misleading responses and improve the overall trustworthiness of the model.