4 citations · 8 across the 9 of their papers we have counts for
6 papers · 1 filter
Towards Verifiable Multimodal Deep Research: A Multi-Agent Harness for Interleaved Report Generation
Chenghao Zhang, Guanting Dong, Yufan Liu +3
Large Language Models (LLMs) have advanced autonomous agents from deep search, which retrieves concise factual answers, to deep research, which synthesizes scattered evidence into…
Towards Mixed-Modal Retrieval for Universal Retrieval-Augmented Generation
Chenghao Zhang, Guanting Dong, Xinyu Yang +1
Retrieval-Augmented Generation (RAG) has emerged as a powerful paradigm for enhancing large language models (LLMs) by retrieving relevant documents from an external corpus. However…
Progressive Multimodal Reasoning via Active Retrieval
Guanting Dong, Chenghao Zhang, Mengjie Deng +3
Multi-step multimodal reasoning tasks pose significant challenges for multimodal large language models (MLLMs), and finding effective ways to enhance their performance in such scen…
Understand What LLM Needs: Dual Preference Alignment for Retrieval-Augmented Generation
Guanting Dong, Yutao Zhu, Chenghao Zhang +3
Retrieval-augmented generation (RAG) has demonstrated effectiveness in mitigating the hallucination problem of large language models (LLMs). However, the difficulty of aligning the…
FlashRAG: A Modular Toolkit for Efficient Retrieval-Augmented Generation Research
Jiajie Jin, Yutao Zhu, Guanting Dong +7
With the advent of large language models (LLMs) and multimodal large language models (MLLMs), the potential of retrieval-augmented generation (RAG) has attracted considerable resea…
INTERS: Unlocking the Power of Large Language Models in Search with Instruction Tuning
Yutao Zhu, Peitian Zhang, Chenghao Zhang +5
Large language models (LLMs) have demonstrated impressive capabilities in various natural language processing tasks. Despite this, their application to information retrieval (IR) t…