VLMs as judges exhibit informativeness bias by favoring detailed but image-inconsistent answers; BIRCH mitigates it by first correcting answers against the image, reducing bias up to 17% and improving performance up to 9.8%.
Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena , booktitle =
3 Pith papers cite this work. Polarity classification is still indexing.
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MGDA-Decoupled applies geometry-based multi-objective optimization within the DPO framework to find shared descent directions that account for each objective's convergence dynamics, yielding higher win rates on UltraFeedback.
LIMO achieves 63.3% on AIME24 and 95.6% on MATH500 via supervised fine-tuning on roughly 1% of the data used by prior models, supporting the claim that minimal strategic examples suffice when pre-training has already encoded domain knowledge.
citing papers explorer
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When Vision-Language Models Judge Without Seeing: Exposing Informativeness Bias
VLMs as judges exhibit informativeness bias by favoring detailed but image-inconsistent answers; BIRCH mitigates it by first correcting answers against the image, reducing bias up to 17% and improving performance up to 9.8%.
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MGDA-Decoupled: Geometry-Aware Multi-Objective Optimisation for DPO-based LLM Alignment
MGDA-Decoupled applies geometry-based multi-objective optimization within the DPO framework to find shared descent directions that account for each objective's convergence dynamics, yielding higher win rates on UltraFeedback.
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LIMO: Less is More for Reasoning
LIMO achieves 63.3% on AIME24 and 95.6% on MATH500 via supervised fine-tuning on roughly 1% of the data used by prior models, supporting the claim that minimal strategic examples suffice when pre-training has already encoded domain knowledge.