most citedMulti-modal Preference Alignment Remedies Degradation of Visual Instruction Tuning on Language Models

5 citations · 5 across the 4 of their papers we have counts for

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cs.CL2024

Abstract2Appendix: Academic Reviews Enhance LLM Long-Context Capabilities

Shengzhi Li, Kittipat Kampa, Rongyu Lin +2

Large language models (LLMs) have shown remarkable performance across various tasks, yet their ability to handle long-context reading remains challenging. This study explores the e…

cs.CL2024★ 5 cited

Multi-modal Preference Alignment Remedies Degradation of Visual Instruction Tuning on Language Models

Shengzhi Li, Rongyu Lin, Shichao Pei

Multi-modal large language models (MLLMs) are expected to support multi-turn queries of interchanging image and text modalities in production. However, the current MLLMs trained wi…