Papers with judge model
Beyond the Surface: Measuring Self-Preference in LLM Judgments (2025.emnlp-main)
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| Challenge: | Existing methods measure self-preference bias by comparing the scores a judge model assigns to its own responses with those assigned to other models. |
| Approach: | They propose to use gold judgments as proxies for the actual quality of responses . they propose to measure self-preference bias as the difference between the judge model's own and other models' scores . |
| Outcome: | The proposed method can assess self-preference bias across large language models . it uses gold judgments as proxies for the ground truth scores of the judge model . |
Multi-Task Reinforcement Learning for Enhanced Multimodal LLM-as-a-Judge (2026.acl-industry)
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| Challenge: | Existing MLLMs are optimized for single-task scenarios and struggle to generalize to diverse contexts. |
| Approach: | They propose a framework that integrates multitask reinforcement learning and generalization capabilities of MLLMs to optimize the judge model across multiple tasks. |
| Outcome: | The proposed framework outperforms baseline models in judgment consistency and correlation with human preferences. |
DART: Leveraging Multi-Agent Disagreement for Tool Recruitment in Multimodal Reasoning (2026.eacl-long)
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Nithin Sivakumaran, Justin Chen, David Wan, Yue Zhang, Jaehong Yoon, Elias Stengel-Eskin, Mohit Bansal
| Challenge: | a key strength of human intelligence is the ability to debate and discuss reasoning with others. |
| Approach: | They propose a multi-agent framework that uses disagreements between visual agents to identify useful visual tools that can resolve inter-agency disagreement. |
| Outcome: | The proposed framework beats the strongest baseline on A-OKVQA and MMMU, respectively. |
Persona-judge: Personalized Alignment of Large Language Models via Token-level Self-judgment (2025.findings-acl)
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| Challenge: | Existing methods for aligning language models with human preferences rely on reward signals and additional annotated data, limiting their scalability and adaptability to diverse human values. |
| Approach: | They propose a discriminative paradigm that leverages the intrinsic preference judgment capabilities of the model to align language models with human preferences. |
| Outcome: | The proposed model is scalable and efficient, paving the way for more adaptive personalized alignment. |
Mediocrity is the key for LLM as a Judge Anchor Selection (2026.acl-long)
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| Challenge: | a poor selection of an anchor can dramatically reduce correlation with human rankings . traditional reference-based metrics are often ill-suited for open-ended generation . |
| Approach: | They evaluate 22 different anchors on a Arena-Hard-v2.0 dataset and quantify the effect size of anchor selection. |
| Outcome: | The proposed model is better or worse than all other models, but it is rarely indicative of the relative ranking of the models. |
ConciseRL: Conciseness-Guided Reinforcement Learning for Efficient Reasoning Models (2025.findings-emnlp)
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| Challenge: | Large language models excel at complex tasks by breaking down problems into structured reasoning steps. |
| Approach: | They propose a hyperparameter-free conciseness score to guide models toward generating correct reasoning traces. |
| Outcome: | The proposed method reduces token usage by 31x on simple problems while improving accuracy by 7% on the hardest problems. |
MR. Judge: Multimodal Reasoner as a Judge (2025.emnlp-main)
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| Challenge: | Effective reward modeling is especially valuable in reinforcement learning (RLHF) . |
| Approach: | They propose a paradigm for empowering general-purpose MLLMs judges with strong reasoning capabilities by using multiple-choice problem models instead of directly assigning scores. |
| Outcome: | The proposed model surpasses GPT-4o on VL-RewardBench and improves performance on MM-Vet by up to 7.7%. |
When in Doubt, Consult: Expert Debate for Sexism Detection via Confidence-Based Routing (2026.acl-long)
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| Challenge: | sexist content on social media is increasingly pervasive, often appearing in subtle, context-dependent forms that evade traditional classification methods. |
| Approach: | They propose a framework that unifies targeted training procedures to regularize supervision to scarce and noisy data with selective reasoning-based inference to handle ambiguous or borderline cases. |
| Outcome: | The proposed framework outperforms existing approaches across several public benchmarks . it bridges the gap between efficiency and reasoning with a dynamic routing mechanism . |