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cs.CL2026
TokenSwap: Benchmarking and Reducing the Modality Gap in Multimodal LLMs
Andong Hua, Colton Bishop, Igor Mordatch +5
Multimodal large language models (MLLMs) should exhibit cross-modal consistency, generating consistent responses given semantically equivalent inputs across modalities. However, we…
cs.CL2024★ 1 cited
Inference-Aware Fine-Tuning for Best-of-N Sampling in Large Language Models
Yinlam Chow, Guy Tennenholtz, Izzeddin Gur +7
Recent studies have indicated that effectively utilizing inference-time compute is crucial for attaining better performance from large language models (LLMs). In this work, we prop…