4 papers
PatchCue: Enhancing Vision-Language Model Reasoning with Patch-Based Visual Cues
Yukun Qi, Pei Fu, Hang Li +5
Vision-Language Models (VLMs) have achieved remarkable progress on a wide range of challenging multimodal understanding and reasoning tasks. However, existing reasoning paradigms,…
KDCM: Reducing Hallucination in LLMs through Explicit Reasoning Structures
Jinbo Hao, Kai Yang, Qingzhen Su +2
To mitigate hallucinations in large language models (LLMs), we propose a framework that focuses on errors induced by prompts. Our method extends a chain-style knowledge distillatio…
Mitigating Prompt-Induced Hallucinations in Large Language Models via Structured Reasoning
Jinbo Hao, Kai Yang, Qingzhen Su +3
To address hallucination issues in large language models (LLMs), this paper proposes a method for mitigating prompt-induced hallucinations. Building on a knowledge distillation cha…
VeritasFi: An Adaptable, Multi-tiered RAG Framework for Multi-modal Financial Question Answering
Zhenghan Tai, Hanwei Wu, Qingchen Hu +24
Retrieval-Augmented Generation (RAG) is becoming increasingly essential for Question Answering (QA) in the financial sector, where accurate and contextually grounded insights from…