Quan Z. Sheng
3 papers · Latest:
Information Retrieval
Purifying Multimodal Retrieval: Fragment-Level Evidence Selection for RAG
FES-RAG purifies multimodal retrieval by selecting specific fragments, not whole documents, improving MLLM generation and reducing noise.
2604.27600
Natural Language ProcessingMEG-RAG: Quantifying Multi-modal Evidence Grounding for Evidence Selection in RAG
MEG-RAG introduces a semantic-aware metric and reranking framework to improve multimodal evidence grounding in RAG systems, enhancing generation accuracy.
2604.24564
Natural Language ProcessingAgenticAI-DialogGen: Topic-Guided Conversation Generation for Fine-Tuning and Evaluating Short- and Long-Term Memories of LLMs
AgenticAI-DialogGen is an agent-based framework that generates topic-guided conversations and a dataset to fine-tune LLMs for improved short- and long-term memory.
2604.12179
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