4 papers
Fact-Checking with Large Language Models via Probabilistic Certainty and Consistency
Haoran Wang, Maryam Khalid, Qiong Wu +2
Large language models (LLMs) are increasingly used in applications requiring factual accuracy, yet their outputs often contain hallucinated responses. While fact-checking can mitig…
Qianfan-VL: Domain-Enhanced Universal Vision-Language Models
Daxiang Dong, Mingming Zheng, Dong Xu +32
We present Qianfan-VL, a series of multimodal large language models ranging from 3B to 70B parameters, achieving state-of-the-art performance through innovative domain enhancement…
SWE-Dev: Building Software Engineering Agents with Training and Inference Scaling
Haoran Wang, Zhenyu Hou, Yao Wei +2
Large language models (LLMs) have advanced rapidly from conversational problem solving to addressing real-world tasks involving tool use, such as software engineering (SWE). Recent…
Understanding the Generalization of In-Context Learning in Transformers: An Empirical Study
Xingxuan Zhang, Haoran Wang, Jiansheng Li +6
Large language models (LLMs) like GPT-4 and LLaMA-3 utilize the powerful in-context learning (ICL) capability of Transformer architecture to learn on the fly from limited examples.…