# Explanation, Debate, Align: A Weak-to-Strong Framework for Language Model Generalization

arXiv: https://arxiv.org/abs/2409.07335 · HTML: https://mlsanity.com/paper/2409.07335/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Human values and AI feedback alignment, one of 10 directions in LLM alignment / RLHF / preference optimisation.
- Builds on 14 of its 31 references that are in the map.
- First posted on arXiv 11 Sep 2024.

## Placed in

- LLM alignment / RLHF / preference optimisation / [Human values and AI feedback alignment](https://mlsanity.com/alignment/human-values-and-ai-feedback-alignment/)

## Builds on (most cited first, 14 of 14)

- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter](https://mlsanity.com/paper/1910.01108/) (2019)
- [Learning to summarize from human feedback](https://mlsanity.com/paper/2009.01325/) (2020)
- [Explanation in Artificial Intelligence: Insights from the Social Sciences](https://mlsanity.com/paper/1706.07269/) (2017)
- [Weak-to-Strong Generalization: Eliciting Strong Capabilities With Weak Supervision](https://mlsanity.com/paper/2312.09390/) (2023)
- [Scalable agent alignment via reward modeling: a research direction](https://mlsanity.com/paper/1811.07871/) (2018)
- [AI safety via debate](https://mlsanity.com/paper/1805.00899/) (2018)
- [Cooperative Inverse Reinforcement Learning](https://mlsanity.com/paper/1606.03137/) (2016)
- [Learning to Learn from Weak Supervision by Full Supervision](https://mlsanity.com/paper/1711.11383/) (2017)
- [Adapting LLMs for Efficient, Personalized Information Retrieval: Methods and Implications](https://mlsanity.com/paper/2311.12287/) (2023)
- [Interactive Plan Explicability in Human-Robot Teaming](https://mlsanity.com/paper/1901.05642/) (2019)
- [Order Matters: Generating Progressive Explanations for Planning Tasks in Human-Robot Teaming](https://mlsanity.com/paper/2004.07822/) (2020)
- [SumRecom: A Personalized Summarization Approach by Learning from Users' Feedback](https://mlsanity.com/paper/2408.07294/) (2024)
- [Experimental Assessment of Human-Robot Teaming for Multi-Step Remote Manipulation with Expert Operators](https://mlsanity.com/paper/2011.10898/) (2020)

## Built on by (most cited first, 1 of 1)

- [Towards Acyclic Preference Evaluation of Language Models via Multiple Evaluators](https://mlsanity.com/paper/2410.12869/) (2024)

## Abstract (arXiv)

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.