2 citations · 2 across the 10 of their papers we have counts for
11 papers
REOPD: Reliability-Adaptive Reward Extrapolation for On-Policy Distillation
Yang Sun, Lichao Ma, Houyuan Qin +5
On-policy distillation (OPD) trains a student on its own trajectories under dense token-level supervision from a teacher. Reward-extrapolation methods such as ExOPD amplify the tea…
EviProp: Seeded Relevance Diffusion on Chunk-Page Graphs for Long Multimodal Document Retrieval
Hongwei Zhang, Xiaoman Wang, Zehui Ling +7
Retrieving evidence pages from visually rich long documents is a key challenge in document question answering. Existing page-level visual retrievers operate under an independent ma…
IA-RAG: Interval-Algebra-Driven Temporal Reasoning for Dynamic Knowledge Retrieval
Xiaoman Wang, Yaoze Zhang, Wenzhuo Fan +7
Retrieval-Augmented Generation (RAG) has shown strong effectiveness in grounding Large Language Models (LLMs) with external knowledge. However, existing RAG and Graph RAG framework…
HetaRAG: Hybrid Deep Retrieval-Augmented Generation across Heterogeneous Data Stores
Guohang Yan, Yue Zhang, Pinlong Cai +7
Retrieval-augmented generation (RAG) has become a dominant paradigm for mitigating knowledge hallucination and staleness in large language models (LLMs) while preserving data secur…
DeepWriter: A Fact-Grounded Multimodal Writing Assistant Based On Offline Knowledge Base
Song Mao, Lejun Cheng, Pinlong Cai +3
Large Language Models (LLMs) have demonstrated remarkable capabilities in various applications. However, their use as writing assistants in specialized domains like finance, medici…
From Ranking to Selection: A Simple but Efficient Dynamic Passage Selector for Retrieval Augmented Generation
Siyuan Meng, Junming Liu, Yirong Chen +5
Retrieval-augmented generation (RAG) systems are often bottlenecked by their reranking modules, which typically score passages independently and select a fixed Top-K size. This app…
LeanRAG: Knowledge-Graph-Based Generation with Semantic Aggregation and Hierarchical Retrieval
Yaoze Zhang, Rong Wu, Pinlong Cai +5
Retrieval-Augmented Generation (RAG) plays a crucial role in grounding Large Language Models by leveraging external knowledge, whereas the effectiveness is often compromised by the…
MELLA: Bridging Linguistic Capability and Cultural Groundedness for Low-Resource Language MLLMs
Yufei Gao, Jiaying Fei, Nuo Chen +4
Multimodal Large Language Models (MLLMs) perform strongly in high-resource languages, yet often produce fluent but culturally "thin" descriptions in low-resource settings. We argue…
Investigating Redundancy in Multimodal Large Language Models with Multiple Vision Encoders
Yizhou Wang, Song Mao, Yang Chen +8
Recent multimodal large language models (MLLMs) increasingly integrate multiple vision encoders to improve performance on various benchmarks, assuming that diverse pretraining obje…
RAKG:Document-level Retrieval Augmented Knowledge Graph Construction
Hairong Zhang, Jiaheng Si, Guohang Yan +5
With the rise of knowledge graph based retrieval-augmented generation (RAG) techniques such as GraphRAG and Pike-RAG, the role of knowledge graphs in enhancing the reasoning capabi…
Aligning Vision to Language: Annotation-Free Multimodal Knowledge Graph Construction for Enhanced LLMs Reasoning
Junming Liu, Siyuan Meng, Yanting Gao +7
Multimodal reasoning in Large Language Models (LLMs) struggles with incomplete knowledge and hallucination artifacts, challenges that textual Knowledge Graphs (KGs) only partially…