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From the 1 of 25 linked papers with an AI index.

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20242026
most citedUnified Multimodal Understanding and Generation Models: Advances, Challenges, and Opportunities

1 citations · 1 across the 7 of their papers we have counts for

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cs.CL2026

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 (…

cs.CL2026

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…

cs.CL2025

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…

cs.CL2024

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…

cs.CL2024

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…