8 papers · 1 filter
SFT or RL? An Early Investigation into Training R1-Like Reasoning Large Vision-Language Models
Hardy Chen, Haoqin Tu, Fali Wang +5
This work revisits the dominant supervised fine-tuning (SFT) then reinforcement learning (RL) paradigm for training Large Vision-Language Models (LVLMs), and reveals a key finding:…
MileBench: Benchmarking MLLMs in Long Context
Dingjie Song, Shunian Chen, Guiming Hardy Chen +3
Despite the advancements and impressive performance of Multimodal Large Language Models (MLLMs) on benchmarks, their effectiveness in real-world, long-context, and multi-image task…
ALLaVA: Harnessing GPT4V-Synthesized Data for Lite Vision-Language Models
Guiming Hardy Chen, Shunian Chen, Ruifei Zhang +7
Large vision-language models (LVLMs) have shown premise in a broad range of vision-language tasks with their strong reasoning and generalization capabilities. However, they require…
Humans or LLMs as the Judge? A Study on Judgement Biases
Guiming Hardy Chen, Shunian Chen, Ziche Liu +2
Adopting human and large language models (LLM) as judges (a.k.a human- and LLM-as-a-judge) for evaluating the performance of LLMs has recently gained attention. Nonetheless, this a…
MLLM-Bench: Evaluating Multimodal LLMs with Per-sample Criteria
Wentao Ge, Shunian Chen, Guiming Hardy Chen +14
Multimodal large language models (MLLMs) have broadened the scope of AI applications. Existing automatic evaluation methodologies for MLLMs are mainly limited in evaluating queries…
More Samples or More Prompts? Exploring Effective In-Context Sampling for LLM Few-Shot Prompt Engineering
Bingsheng Yao, Guiming Chen, Ruishi Zou +7
While most existing works on LLM prompting techniques focus only on how to select a better set of data samples inside one single prompt input (In-Context Learning or ICL), why can…