Search Results for author:
Found 8 papers, 7 papers with code
VisualPRM: An Effective Process Reward Model for Multimodal Reasoning
We introduce VisualPRM, an advanced multimodal Process Reward Model (PRM) with 8B parameters, which improves the reasoning abilities of existing Multimodal Large Language Models (MLLMs) across different model scales and families with Best-of-N (BoN) evaluation strategies.
Expanding Performance Boundaries of Open-Source Multimodal Models with Model, Data, and Test-Time Scaling
We introduce InternVL 2. 5, an advanced multimodal large language model (MLLM) series that builds upon InternVL 2. 0, maintaining its core model architecture while introducing significant enhancements in training and testing strategies as well as data quality.
Ranked #1 on
Video Question Answering
on NExT-QA
Enhancing the Reasoning Ability of Multimodal Large Language Models via Mixed Preference Optimization
To address this, we introduce a preference optimization (PO) process to enhance the multimodal reasoning capabilities of MLLMs.
Mini-InternVL: A Flexible-Transfer Pocket Multimodal Model with 5% Parameters and 90% Performance
Multimodal large language models (MLLMs) have demonstrated impressive performance in vision-language tasks across a broad spectrum of domains.
MMInstruct: A High-Quality Multi-Modal Instruction Tuning Dataset with Extensive Diversity
To construct MMInstruct, we propose an instruction generation data engine that leverages GPT-4V, GPT-3. 5, and manual correction.
Ranked #124 on
Visual Question Answering
on MM-Vet
OmniCorpus: A Unified Multimodal Corpus of 10 Billion-Level Images Interleaved with Text
In this paper, we introduce OmniCorpus, a 10 billion-scale image-text interleaved dataset.
How Far Are We to GPT-4V? Closing the Gap to Commercial Multimodal Models with Open-Source Suites
Compared to both open-source and proprietary models, InternVL 1. 5 shows competitive performance, achieving state-of-the-art results in 8 of 18 benchmarks.
Ranked #6 on
Multiple-choice
on Neptune-Full
ControlLLM: Augment Language Models with Tools by Searching on Graphs
We present ControlLLM, a novel framework that enables large language models (LLMs) to utilize multi-modal tools for solving complex real-world tasks.
