4 citations · 8 across the 21 of their papers we have counts for
6 papers · 1 filter
D2-ScaleAgent: Dual-Dimensional Scaling for Long Document Understanding
Hao Zhang, Longrong Yang, Lunhao Duan +3
Multi-modal retrieval-augmented generation (RAG) is a key technique for visually rich long document understanding. Existing multi-modal RAG methods are progressively advancing towa…
Triplets Better Than Pairs: Towards Stable and Effective Self-Play Fine-Tuning for LLMs
Yibo Wang, Hai-Long Sun, Qing-Guo Chen +4
Recently, self-play fine-tuning (SPIN) has been proposed to adapt large language models to downstream applications with scarce expert-annotated data, by iteratively generating synt…
Scaling Beyond Context: A Survey of Multimodal Retrieval-Augmented Generation for Document Understanding
Sensen Gao, Shanshan Zhao, Xu Jiang +7
Document understanding is critical for applications from financial analysis to scientific discovery. Current approaches, whether OCR-based pipelines feeding Large Language Models (…
OmniEvalKit: A Modular, Lightweight Toolbox for Evaluating Large Language Model and its Omni-Extensions
Yi-Kai Zhang, Xu-Xiang Zhong, Shiyin Lu +3
The rapid advancements in Large Language Models (LLMs) have significantly expanded their applications, ranging from multilingual support to domain-specific tasks and multimodal int…
Wings: Learning Multimodal LLMs without Text-only Forgetting
Yi-Kai Zhang, Shiyin Lu, Yang Li +7
Multimodal large language models (MLLMs), initiated with a trained LLM, first align images with text and then fine-tune on multimodal mixed inputs. However, the MLLM catastrophical…
Advancing Tool-Augmented Large Language Models: Integrating Insights from Errors in Inference Trees
Sijia Chen, Yibo Wang, Yi-Feng Wu +5
Tool-augmented large language models (LLMs) leverage tools, often in the form of APIs, to improve their reasoning capabilities on complex tasks. This enables them to act as intelli…