Explanation, Debate, Align: A Weak-to-Strong Framework for Language Model Generalization
The rapid advancement of artificial intelligence systems has brought the challenge of AI alignment to the forefront of research, particularly in complex decision-making and task execution. As these systems surpass human-level performance in sophisticated problems, ensuring their alignment with human values, intentions, and ethical guidelines becomes crucial. Building on previous work in explanation generation for human-agent alignment, we address the more complex dynamics of multi-agent systems and human-AI teams. This paper introduces a novel approach to model alignment through weak-to-strong generalization in the context of language models. We present a framework where a strong model facilitates the improvement of a weaker model, bridging the gap between explanation generation and model alignment. Our method, formalized as a facilitation function, allows for the transfer of capabilities from advanced models to less capable ones without direct access to extensive training data. Our results suggest that this facilitation-based approach not only enhances model performance but also provides insights into the nature of model alignment and the potential for scalable oversight of AI systems.
Code (0)
등록된 구현이 없습니다.
Tasks
Decision MakingExplanation GenerationLanguage ModelingLanguage ModellingSimilar Papers 제목 키워드 기반
Debate Helps Weak-to-Strong Generalization
Common methods for aligning already-capable models with desired behavior rely on the ability of humans to provide supervision. However, future superhuman models will surpass the capability of humans. Therefore, humans wi…
Debating with More Persuasive LLMs Leads to More Truthful Answers
Common methods for aligning large language models (LLMs) with desired behaviour heavily rely on human-labelled data. However, as models grow increasingly sophisticated, they will surpass human expertise, and the role of …
PersuasivenessDebating for Better Reasoning: An Unsupervised Multimodal Approach
As Large Language Models (LLMs) gain expertise across diverse domains and modalities, scalable oversight becomes increasingly challenging, particularly when their capabilities may surpass human evaluators. Debate has eme…
Question AnsweringVisual Question AnsweringVisual Question Answering (VQA)Improving Weak-to-Strong Generalization with Scalable Oversight and Ensemble Learning
This paper presents a follow-up study to OpenAI's recent superalignment work on Weak-to-Strong Generalization (W2SG). Superalignment focuses on ensuring that high-level AI systems remain consistent with human values and …
Ensemble LearningIn-Context LearningCan LLMs Produce Faithful Explanations For Fact-checking? Towards Faithful Explainable Fact-Checking via Multi-Agent Debate
Fact-checking research has extensively explored verification but less so the generation of natural-language explanations, crucial for user trust. While Large Language Models (LLMs) excel in text generation, their capabil…
Fact CheckingText Generation