arXiv AI
Jul 23
Self-Preference Bias in Rubric-Based Evaluation of Large Language Models
arXiv:2604. 06996v2 Announce Type: replace-cross Abstract: LLM-as-a-judge has become the de facto approach for evaluating LLM outputs.
Quantifying and Mitigating Self-Preference Bias of LLM Judges
arXiv:2604. 22891v4 Announce Type: replace-cross Abstract: LLM-as-a-Judge has become a dominant approach in automated evaluation systems, playing critical roles in model alignment, leaderboard construction, quality control, and so on.
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arXiv Machine Learning
Jun 15
From Uncertain Judgments to Calibrated Rankings: Conformal Elo Estimation for LLM Evaluation
arXiv:2606. 13221v2 Announce Type: replace Abstract: Evaluating new large language models typically requires costly human annotation campaigns at scale.
arXiv AI
Jun 24
Breaking the Mirror: Activation-Based Mitigation of Self-Preference in LLM Evaluators
arXiv:2509. 03647v2 Announce Type: replace-cross Abstract: Large language models (LLMs) increasingly serve as automated evaluators, yet they suffer from "self-preference bias": a tendency to favor their own outputs over those of other models.
arXiv AI
Jun 3
CoEval: Ranking Language Models for Custom Tasks Without Labeled Data or Trustworthy Benchmarks
arXiv:2606. 03650v1 Announce Type: cross Abstract: Choosing or ranking language models for a specific application is hardest when no task-specific labeled data exists, and standard public benchmarks cannot be trusted, their items having likely leaked into pretraining, so scores reflect memorization rather than fitness.
arXiv Machine Learning
1d ago
Limits to scalable evaluation at the frontier: LLM as Judge won't beat twice the data
arXiv:2410. 13341v4 Announce Type: replace Abstract: High quality annotations are increasingly a bottleneck in the explosively growing machine learning ecosystem.
arXiv AI
Jun 24
Are LLM Evaluators Really Narcissists? Sanity Checking Self-Preference Evaluations
arXiv:2601. 22548v4 Announce Type: replace-cross Abstract: Recent research has shown that large language models (LLMs) favor their own outputs when acting as judges, undermining the integrity of automated post-training and evaluation workflows.
Hugging Face Trending Papers
Jun 3
Self-Evaluation Is Already There: Eliciting Latent Judge Calibration in Base LLMs with Minimal Data
Large language models are increasingly evaluated by other models, raising a natural question: can a model predict how a judge will score its own output? We find that the ability is largely present before any targeted training: prompted few-shot, a base model already predicts an external judge's multi-attribute quality scores on open-ended responses well above chance across three benchmarks.
arXiv AI
Jun 11
A Survey on Evaluating Quality and Trustworthiness in LLM-Generated Data
arXiv:2601. 17717v3 Announce Type: replace Abstract: Large Language Models (LLMs) have emerged as powerful tools for generating data across various modalities.
arXiv AI
Jul 10
Rethinking LLM-as-a-Judge: Representation-as-a-Judge with Small Language Models via Semantic Capacity Asymmetry
arXiv:2601. 22588v2 Announce Type: replace-cross Abstract: Large language models (LLMs) are widely used as reference-free evaluators via prompting, but this "LLM-as-a-Judge" paradigm is costly, opaque, and sensitive to prompt design.
arXiv AI
Jul 10
When the Judge Changes, So Does the Measurement: Auditing LLM-as-Judge Reliability
arXiv:2607. 08535v1 Announce Type: cross Abstract: An LLM-as-judge score can move even when the candidate responses stay fixed, simply because the evaluator has changed.
arXiv AI
Jul 10
When LLMs Agree, Are They Right? Auditing Self-Consistency and Cross-Model Agreement as Confidence Signals
arXiv:2607. 08065v1 Announce Type: new Abstract: LLM-as-judge (Zheng et al.
arXiv AI
Jun 15
The Coin Flip Judge? Reliability and Bias in LLM-as-a-Judge Evaluation
arXiv:2606. 13685v1 Announce Type: cross Abstract: LLM-as-a-Judge is now widely used to rank model outputs, train reward models, and populate public leaderboards, but its run-to-run reliability remains under-characterized.