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Shuangyin Li

7 accepted papers

2026

RFI: Rectified Flow Intervention for Mitigating Object Hallucination in Large Vision-Language Models

AAAI 2026technical

Large Vision-Language Models (LVLMs) have demonstrated remarkable capabilities in multimodal understanding and generation by integrating visual and textual data. However, these models frequently exhibit object hallucination problems: generating outputs that are inconsistent with the input image. Exi

Cited by 0SourcePDFScholar
2026

VoG: Enhancing LLM Reasoning through Stepwise Verification on Knowledge Graphs

ICLR 2026poster

Large Language Models (LLMs) excel at various reasoning tasks but still encounter challenges such as hallucination and factual inconsistency in knowledge-intensive tasks, primarily due to a lack of external knowledge and factual verification. These challenges could be mitigated by leveraging knowled…

Cited by 0SourceScholar
2024

R2AG: Incorporating Retrieval Information into Retrieval Augmented Generation

EMNLP 2024finding

Retrieval augmented generation (RAG) has been applied in many scenarios to augment large language models (LLMs) with external documents provided by retrievers. However, a semantic gap exists between LLMs and retrievers due to differences in their training objectives and architectures. This misalignm…