3 citations · 7 across the 12 of their papers we have counts for
4 papers · 1 filter
CL-bench Life: Can Language Models Learn from Real-Life Context?
Shihan Dou, Yujiong Shen, Chenhao Huang +35
Today's AI assistants such as OpenClaw are designed to handle context effectively, making context learning an increasingly important capability for models. As these systems move be…
Little Giants: Synthesizing High-Quality Embedding Data at Scale
Haonan Chen, Liang Wang, Nan Yang +4
Synthetic data generation has become an increasingly popular way of training models without the need for large, manually labeled datasets. For tasks like text embedding, synthetic…
A Survey of Conversational Search
Fengran Mo, Kelong Mao, Ziliang Zhao +7
As a cornerstone of modern information access, search engines have become indispensable in everyday life. With the rapid advancements in AI and natural language processing (NLP) te…
Generalizing Conversational Dense Retrieval via LLM-Cognition Data Augmentation
Haonan Chen, Zhicheng Dou, Kelong Mao +2
Conversational search utilizes muli-turn natural language contexts to retrieve relevant passages. Existing conversational dense retrieval models mostly view a conversation as a fix…