24 citations · 27 across the 10 of their papers we have counts for
10 papers
Improving Language Model Reasoning with Self-motivated Learning
Yunlong Feng, Yang Xu, Libo Qin +2
Large-scale high-quality training data is important for improving the performance of models. After trained with data that has rationales (reasoning steps), models gain reasoning ca…
UniRetriever: Multi-task Candidates Selection for Various Context-Adaptive Conversational Retrieval
Hongru Wang, Boyang Xue, Baohang Zhou +5
Conversational retrieval refers to an information retrieval system that operates in an iterative and interactive manner, requiring the retrieval of various external resources, such…
Evaluating Robustness of Generative Search Engine on Adversarial Factual Questions
Xuming Hu, Xiaochuan Li, Junzhe Chen +8
Generative search engines have the potential to transform how people seek information online, but generated responses from existing large language models (LLMs)-backed generative s…
YODA: Teacher-Student Progressive Learning for Language Models
Jianqiao Lu, Wanjun Zhong, Yufei Wang +10
Although large language models (LLMs) have demonstrated adeptness in a range of tasks, they still lag behind human learning efficiency. This disparity is often linked to the inhere…
Improving Factual Consistency for Knowledge-Grounded Dialogue Systems via Knowledge Enhancement and Alignment
Boyang Xue, Weichao Wang, Hongru Wang +7
Pretrained language models (PLMs) based knowledge-grounded dialogue systems are prone to generate responses that are factually inconsistent with the provided knowledge source. In s…
Large Language Models as Source Planner for Personalized Knowledge-grounded Dialogue
Hongru Wang, Minda Hu, Yang Deng +7
Open-domain dialogue system usually requires different sources of knowledge to generate more informative and evidential responses. However, existing knowledge-grounded dialogue sys…