RewardAnything follows natural-language reward principles at inference time and, with the new RABench benchmark, demonstrates that principle-conditioned listwise training beats fixed-preference reward models on held-out principles.
Leveraging Large Language Models for NLG Evaluation: Advances and Challenges
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In the rapidly evolving domain of Natural Language Generation (NLG) evaluation, introducing Large Language Models (LLMs) has opened new avenues for assessing generated content quality, e.g., coherence, creativity, and context relevance. This paper aims to provide a thorough overview of leveraging LLMs for NLG evaluation, a burgeoning area that lacks a systematic analysis. We propose a coherent taxonomy for organizing existing LLM-based evaluation metrics, offering a structured framework to understand and compare these methods. Our detailed exploration includes critically assessing various LLM-based methodologies, as well as comparing their strengths and limitations in evaluating NLG outputs. By discussing unresolved challenges, including bias, robustness, domain-specificity, and unified evaluation, this paper seeks to offer insights to researchers and advocate for fairer and more advanced NLG evaluation techniques.
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RewardAnything: Generalizable Principle-Following Reward Models
RewardAnything follows natural-language reward principles at inference time and, with the new RABench benchmark, demonstrates that principle-conditioned listwise training beats fixed-preference reward models on held-out principles.