Tech · Apple Machine Learning
Understanding Alignment in Multimodal LLMs: A Comprehensive Study
Compiled by KHAO Editorial — aggregated from 1 source + 2 references discovered via search. See llms.txt for citation guidance.
✓ KHAO Verified
Understanding Alignment in Multimodal LLMs: A Comprehensive Study.
Key facts
- Recently, multiple works have introduced preference datasets for MLLMs and examined different alignment methods, including Direct Preference Optimization (DPO) and Proximal Policy Optimization (PPO)
- Authors Elmira Amirloo*, Jean-Philippe Fauconnier*, Christoph Roesmann*, Christian Kerl†, Rinu Boney†, Yusu Qian, Zirui Wang, Afshin Dehghan, Yinfei Yang, Zhe Gan, Peter Grasch
- Understanding Alignment in Multimodal LLMs: A Comprehensive Study
- Preference alignment has become a crucial component in enhancing the performance of Large Language Models (LLMs), yet its impact in Multimodal Large Language Models (MLLMs) remains comparatively
Summary
Authors Elmira Amirloo*, Jean-Philippe Fauconnier*, Christoph Roesmann*, Christian Kerl†, Rinu Boney†, Yusu Qian, Zirui Wang, Afshin Dehghan, Yinfei Yang, Zhe Gan, Peter Grasch. Preference alignment has become a crucial component in enhancing the performance of Large Language Models (LLMs), yet its impact in Multimodal Large Language Models (MLLMs) remains comparatively underexplored. Similar to language models, MLLMs for image understanding tasks encounter challenges like hallucination. Recently, multiple works have introduced preference datasets for MLLMs and examined different alignment methods, including Direct Preference Optimization (DPO) and Proximal Policy Optimization (PPO).