Meta-Rewarding Language Models: Self-Improving Alignment with LLM-as-a-Meta-Judge
Large Language Models (LLMs) are rapidly surpassing human knowledge in many domains. While improving these models traditionally relies on costly human data, recent self-rewarding mechanisms (Yuan et al., 2024) have shown that LLMs can improve by judging their own responses instead of relying on human labelers. However, existing methods have primarily focused on improving model responses rather than judgment capabilities, resulting in rapid saturation during iterative training. To address this issue, we introduce a novel Meta-Rewarding step to the self-improvement process, where the model judges its own judgements and uses that feedback to refine its judgment skills. Surprisingly, this unsupervised approach improves the model's ability to judge {\em and} follow instructions, as demonstrated by a win rate improvement of Llama-3-8B-Instruct from 22.9% to 39.4% on AlpacaEval 2, and 20.6% to 29.1% on Arena-Hard. These results strongly suggest the potential for self-improving models without human supervision.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
CREAM: Consistency Regularized Self-Rewarding Language Models
Recent self-rewarding large language models (LLM) have successfully applied LLM-as-a-Judge to iteratively improve the alignment performance without the need of human annotations for preference data. These methods commonl…
Language ModelingLanguage ModellingConversation for Non-verifiable Learning: Self-Evolving LLMs through Meta-Evaluation
Training large language models (LLMs) for non-verifiable tasks, such as creative writing, dialogue, and ethical reasoning, remains challenging due to the absence of ground-truth labels. While LLM-as-Judge approaches offe…
Dynamic Rewarding with Prompt Optimization Enables Tuning-free Self-Alignment of Language Models
Aligning Large Language Models (LLMs) traditionally relies on costly training and human preference annotations. Self-alignment seeks to reduce these expenses by enabling models to align themselves. To further lower costs…
Language Imbalance Driven Rewarding for Multilingual Self-improving
Large Language Models (LLMs) have achieved state-of-the-art performance across numerous tasks. However, these advancements have predominantly benefited "first-class" languages such as English and Chinese, leaving many ot…
Arithmetic ReasoningInstruction FollowingThe Mind's Eye: A Multi-Faceted Reward Framework for Guiding Visual Metaphor Generation
Visual metaphor generation is a challenging task that aims to generate an image given an input text metaphor. Inherently, it needs language understanding to bind a source concept with a target concept, in a way that pres…