8 citations · 8 across the 6 of their papers we have counts for
9 papers
IdeaScientist: Orchestrating Agents for Grounded Scientific Ideation
Jiarui Liu, Renjie Tao, Yiwei Liao +15
Despite rapid progress in automating scientific research, generating promising and well grounded research solutions remains a central challenge. We isolate research ideation as a s…
MemLife: Curating and Reasoning over Long-Term Egocentric Video Memories
Guangzhi Xiong, Xinyuan Zhang, Xiao Yang +14
Long-term egocentric video enables personalized AI assistants to reason about daily life. However, as video histories grow to hundreds of hours spanning months or years, reprocessi…
WearVQA: A Visual Question Answering Benchmark for Wearables in Egocentric Authentic Real-world scenarios
Eun Chang, Zhuangqun Huang, Yiwei Liao +19
We introduce WearVQA, the first benchmark specifically designed to evaluate the Visual Question Answering (VQA) capabilities of multi-model AI assistant on wearable devices like sm…
CRAG-MM: Multi-modal Multi-turn Comprehensive RAG Benchmark
Jiaqi Wang, Xiao Yang, Kai Sun +38
Wearable devices such as smart glasses are transforming the way people interact with their surroundings, enabling users to seek information regarding entities in their view. Multi-…
KERAG: Knowledge-Enhanced Retrieval-Augmented Generation for Advanced Question Answering
Yushi Sun, Kai Sun, Yifan Ethan Xu +4
Retrieval-Augmented Generation (RAG) mitigates hallucination in Large Language Models (LLMs) by incorporating external data, with Knowledge Graphs (KGs) offering crucial informatio…
TruthRL: Incentivizing Truthful LLMs via Reinforcement Learning
Zhepei Wei, Xiao Yang, Kai Sun +12
While large language models (LLMs) have demonstrated strong performance on factoid question answering, they are still prone to hallucination and untruthful responses, particularly…
PrismRAG: Boosting RAG Factuality with Distractor Resilience and Strategized Reasoning
Mohammad Kachuee, Teja Gollapudi, Minseok Kim +10
Retrieval-augmented generation (RAG) often falls short when retrieved context includes confusing semi-relevant passages, or when answering questions require deep contextual underst…
Are Large Language Models a Good Replacement of Taxonomies?
Yushi Sun, Hao Xin, Kai Sun +5
Large language models (LLMs) demonstrate an impressive ability to internalize knowledge and answer natural language questions. Although previous studies validate that LLMs perform…
CRAG -- Comprehensive RAG Benchmark
Xiao Yang, Kai Sun, Hao Xin +24
Retrieval-Augmented Generation (RAG) has recently emerged as a promising solution to alleviate Large Language Model (LLM)'s deficiency in lack of knowledge. Existing RAG datasets,…