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Self-Taught Evaluators
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Self-Taught Evaluators
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Model-based evaluation is at the heart of successful model development -- as a reward model for training, and as a replacement for human evaluation. To train such evaluators, the standard approach is to collect a large amount of human preference judgments over model responses, which is costly and the data becomes stale as models improve. In this work, we present an approach that aims to im-prove evaluators without human annotations, using synthetic training data only. Starting from unlabeled instructions, our iterative self-improvement scheme generates contrasting model outputs and trains an LLM-as-a-Judge to produce reasoning traces and final judgments, repeating this training at each new iteration using the improved predictions. Without any labeled preference data, our Self-Taught Evaluator can improve a strong LLM (Llama3-70B-Instruct) from 75.4 to 88.3 (88.7 with majority vote) on RewardBench. This outperforms commonly used LLM judges such as GPT-4 and matches the performance of the top-performing reward models trained with labeled examples.
Forward citations
Cited by 12 Pith papers
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Towards Spec Learning: Inference-Time Alignment from Preference Pairs
Proposes compiling preference pairs into readable natural-language specifications for inference-time LLM alignment, claiming outperformance over DPO on dense-preference domains.
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Power Distribution Bridges Sampling, Self-Reward RL, and Self-Distillation
The power distribution is the target of power sampling, the closed-form solution to self-reward KL-regularized RL, and the basis for power self-distillation that matches sampling performance at lower cost.
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MultiBreak: A Scalable and Diverse Multi-turn Jailbreak Benchmark for Evaluating LLM Safety
MultiBreak is a large diverse multi-turn jailbreak benchmark that achieves substantially higher attack success rates on LLMs than prior datasets and reveals topic-specific vulnerabilities in multi-turn settings.
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Conversation for Non-verifiable Learning: Self-Evolving LLMs through Meta-Evaluation
CoNL lets LLMs self-improve on non-verifiable tasks by rewarding critiques that produce better solutions in multi-agent conversations, jointly optimizing generation and judging without external feedback.
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On the Shelf Life of Fine-Tuned LLM-Judges: Future-Proofing, Backward-Compatibility, and Question Generalization
Fine-tuned LLM judges struggle with future-proofing to newer generators but maintain backward-compatibility more easily; DPO training and continual learning improve adaptation while all models degrade on unseen questions.
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Improving Large Vision and Language Models by Learning from a Panel of Peers
A panel of LVLMs that generate, evaluate, and learn from each other's outputs improves average benchmark scores by 9 points across 15 tasks.
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PASTA: A Paraphrasing And Self-Training Approach for Knowledge Updating in LLMs
PASTA combines data augmentation and a self-learning DPO process to integrate new factual knowledge from news articles into LLMs, raising accuracy from 0.02 to 0.82 on post-cutoff questions while preserving general ca...
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Towards Spec Learning: Inference-Time Alignment from Preference Pairs
Spec learning compiles brief instructions and preference pairs into readable natural-language specifications that condition LLMs at inference time and can outperform DPO on domains with dense preference signals.
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Codifying the Judge: Scalable Evaluation via Program Distillation
LLM judge logic can be distilled into a committee of Python scoring programs that match mid-size LLM judge accuracy on filtered preference datasets, at orders-of-magnitude higher throughput.
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Advancing Multimodal Judge Models through a Capability-Oriented Benchmark and MCTS-Driven Data Generation
A capability-oriented multimodal judge benchmark and MCTS-based preference-data generation improve judge models on some benchmarks, but the paper's SOTA claim is not supported by its own numbers.
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A Survey on LLM-as-a-Judge
A survey on LLM-as-a-Judge that reviews reliability strategies, proposes evaluation methods, and introduces a novel benchmark for assessing such systems.
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LLMs-as-Judges: A Comprehensive Survey on LLM-based Evaluation Methods
A survey that organizes LLMs-as-judges research into functionality, methodology, applications, meta-evaluation, and limitations.
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