Search Results for author:
Found 13 papers, 9 papers with code
BlenderLLM: Training Large Language Models for Computer-Aided Design with Self-improvement
In this paper, we present BlenderLLM, a novel framework for training LLMs specifically for CAD tasks leveraging a self-improvement methodology.
Both Text and Images Leaked! A Systematic Analysis of Multimodal LLM Data Contamination
The rapid progression of multimodal large language models (MLLMs) has demonstrated superior performance on various multimodal benchmarks.
VLFeedback: A Large-Scale AI Feedback Dataset for Large Vision-Language Models Alignment
As large vision-language models (LVLMs) evolve rapidly, the demand for high-quality and diverse data to align these models becomes increasingly crucial.
Ranked #62 on
Visual Question Answering
on MM-Vet
Less is More: A Simple yet Effective Token Reduction Method for Efficient Multi-modal LLMs
The rapid advancement of Multimodal Large Language Models (MLLMs) has led to remarkable performances across various domains.
LongLLaVA: Scaling Multi-modal LLMs to 1000 Images Efficiently via a Hybrid Architecture
Expanding the long-context capabilities of Multi-modal Large Language Models~(MLLMs) is crucial for video understanding, high-resolution image understanding, and multi-modal agents.
Open-FinLLMs: Open Multimodal Large Language Models for Financial Applications
Financial LLMs hold promise for advancing financial tasks and domain-specific applications.
HuatuoGPT-Vision, Towards Injecting Medical Visual Knowledge into Multimodal LLMs at Scale
The rapid development of multimodal large language models (MLLMs), such as GPT-4V, has led to significant advancements.
MileBench: Benchmarking MLLMs in Long Context
Despite the advancements and impressive performance of Multimodal Large Language Models (MLLMs) on benchmarks, their effectiveness in real-world, long-context, and multi-image tasks is unclear due to the benchmarks' limited scope.
ALLaVA: Harnessing GPT4V-Synthesized Data for Lite Vision-Language Models
Large vision-language models (LVLMs) have shown premise in a broad range of vision-language tasks with their strong reasoning and generalization capabilities.
Humans or LLMs as the Judge? A Study on Judgement Biases
We further exploit these biases to conduct attacks on LLM judges.
Silkie: Preference Distillation for Large Visual Language Models
This paper explores preference distillation for large vision language models (LVLMs), improving their ability to generate helpful and faithful responses anchoring the visual context.
Ranked #66 on
Visual Question Answering
on MM-Vet
MLLM-Bench: Evaluating Multimodal LLMs with Per-sample Criteria
To this end, in our paper, we propose a new evaluation paradigm for MLLMs, which is evaluating MLLMs with per-sample criteria using potent MLLM as the judge.
HuatuoGPT-II, One-stage Training for Medical Adaption of LLMs
We validate the new protocol in the domains where proprietary LLMs like ChatGPT perform relatively poorly, such as Traditional Chinese Medicine.
