Cover Image

Do LLM Evaluators Prefer Themselves for a Reason?

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Chen, Wei-Lin, Wei, Zhepei, Zhu, Xinyu, Feng, Shi, Meng, Yu
Format:	Preprint
Published:	2025
Subjects:	Computation and Language
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

AdaDecode: Accelerating LLM Decoding with Adaptive Layer Parallelism
by: Wei, Zhepei, et al.
Published: (2025)

The Surprising Effectiveness of Negative Reinforcement in LLM Reasoning
by: Zhu, Xinyu, et al.
Published: (2025)

Aligning Large Language Models via Fully Self-Synthetic Data
by: Yin, Shangjian, et al.
Published: (2025)

InstructRAG: Instructing Retrieval-Augmented Generation via Self-Synthesized Rationales
by: Wei, Zhepei, et al.
Published: (2024)

You Only Need Minimal RLVR Training: Extrapolating LLMs via Rank-1 Trajectories
by: Wei, Zhepei, et al.
Published: (2026)

CHIMERA: Compact Synthetic Data for Generalizable LLM Reasoning
by: Zhu, Xinyu, et al.
Published: (2026)

Do Before You Judge: Self-Reference as a Pathway to Better LLM Evaluation
by: Lin, Wei-Hsiang, et al.
Published: (2025)

Beyond the Surface: Measuring Self-Preference in LLM Judgments
by: Chen, Zhi-Yuan, et al.
Published: (2025)

Enhancing LLM Reasoning via Non-Human-Like Reasoning Path Preference Optimization
by: Lu, Junjie, et al.
Published: (2025)

Teaching Models to Teach Themselves: Reasoning at the Edge of Learnability
by: Sundaram, Shobhita, et al.
Published: (2026)

Beyond Outcome Reward: Decoupling Search and Answering Improves LLM Agents
by: Wang, Yiding, et al.
Published: (2025)

Language Models can Evaluate Themselves via Probability Discrepancy
by: Xia, Tingyu, et al.
Published: (2024)

Reference-based Metrics Disprove Themselves in Question Generation
by: Nguyen, Bang, et al.
Published: (2024)

Probability-Consistent Preference Optimization for Enhanced LLM Reasoning
by: Yang, Yunqiao, et al.
Published: (2025)

Think Deep, Not Just Long: Measuring LLM Reasoning Effort via Deep-Thinking Tokens
by: Chen, Wei-Lin, et al.
Published: (2026)

ELSPR: Evaluator LLM Training Data Self-Purification on Non-Transitive Preferences via Tournament Graph Reconstruction
by: Yu, Yan, et al.
Published: (2025)

Full-Step-DPO: Self-Supervised Preference Optimization with Step-wise Rewards for Mathematical Reasoning
by: Xu, Huimin, et al.
Published: (2025)

Preference Optimization for Reasoning with Pseudo Feedback
by: Jiao, Fangkai, et al.
Published: (2024)

Separate the Wheat from the Chaff: Winnowing Down Divergent Views in Retrieval Augmented Generation
by: Wang, Song, et al.
Published: (2025)

Do LLMs Recognize Your Preferences? Evaluating Personalized Preference Following in LLMs
by: Zhao, Siyan, et al.
Published: (2025)

MAPO: Advancing Multilingual Reasoning through Multilingual Alignment-as-Preference Optimization
by: She, Shuaijie, et al.
Published: (2024)

Navigating Through Paper Flood: Advancing LLM-based Paper Evaluation through Domain-Aware Retrieval and Latent Reasoning
by: Zheng, Wuqiang, et al.
Published: (2025)

RAVEL: Reasoning Agents for Validating and Evaluating LLM Text Synthesis
by: Feng, Andrew Zhuoer, et al.
Published: (2026)

Budget-Aware Anytime Reasoning with LLM-Synthesized Preference Data
by: Zhang, Xuanming, et al.
Published: (2026)

LLM-Guided Knowledge Distillation for Temporal Knowledge Graph Reasoning
by: Xing, Wang, et al.
Published: (2026)

Large Language Models are Demonstration Pre-Selectors for Themselves
by: Jin, Jiarui, et al.
Published: (2025)

From Leaky Thoughts to Private Reasoning: Controlling What LRMs Say to Themselves
by: Puerto, Haritz, et al.
Published: (2026)

Advancing LLM Reasoning Generalists with Preference Trees
by: Yuan, Lifan, et al.
Published: (2024)

AlpsBench: An LLM Personalization Benchmark for Real-Dialogue Memorization and Preference Alignment
by: Xiao, Jianfei, et al.
Published: (2026)

Self-Training with Direct Preference Optimization Improves Chain-of-Thought Reasoning
by: Wang, Tianduo, et al.
Published: (2024)

When LLMs Benchmark Themselves: Deconstructing Self-Bias in Automated Evaluation
by: Xu, Wenda, et al.
Published: (2025)

Chain of Preference Optimization: Improving Chain-of-Thought Reasoning in LLMs
by: Zhang, Xuan, et al.
Published: (2024)

Found in Conversation: LLMs Teach Themselves to Close the Multi-Turn Gap
by: Chen, Tianlang, et al.
Published: (2026)

LLM Reasoning Engine: Specialized Training for Enhanced Mathematical Reasoning
by: Chen, Shuguang, et al.
Published: (2024)

Self-Jailbreaking: Language Models Can Reason Themselves Out of Safety Alignment After Benign Reasoning Training
by: Yong, Zheng-Xin, et al.
Published: (2025)

LocalSUG: City-Preference-Enhanced LLM for Query Suggestion in Local-Life Services
by: Chen, Jinwen, et al.
Published: (2026)

LLMs Judge Themselves: A Game-Theoretic Framework for Human-Aligned Evaluation
by: Yang, Gao, et al.
Published: (2025)

How Well Do Large Reasoning Models Translate? A Comprehensive Evaluation for Multi-Domain Machine Translation
by: Ye, Yongshi, et al.
Published: (2025)

Can LLMs Explain Themselves Counterfactually?
by: Dehghanighobadi, Zahra, et al.
Published: (2025)

Unleashing LLM Reasoning Capability via Scalable Question Synthesis from Scratch
by: Ding, Yuyang, et al.
Published: (2024)