11 citations · 49 across the 10 of their papers we have counts for
7 papers · 1 filter
Can Long-Context Language Models Subsume Retrieval, RAG, SQL, and More?
Jinhyuk Lee, Anthony Chen, Zhuyun Dai +16
Long-context language models (LCLMs) have the potential to revolutionize our approach to tasks traditionally reliant on external tools like retrieval systems or databases. Leveragi…
Gecko: Versatile Text Embeddings Distilled from Large Language Models
Jinhyuk Lee, Zhuyun Dai, Xiaoqi Ren +17
We present Gecko, a compact and versatile text embedding model. Gecko achieves strong retrieval performance by leveraging a key idea: distilling knowledge from large language model…
QUEST: A Retrieval Dataset of Entity-Seeking Queries with Implicit Set Operations
Chaitanya Malaviya, Peter Shaw, Ming-Wei Chang +2
Formulating selective information needs results in queries that implicitly specify set operations, such as intersection, union, and difference. For instance, one might search for "…
Answering Complicated Question Intents Expressed in Decomposed Question Sequences
Mohit Iyyer, Wen-tau Yih, Ming-Wei Chang
Recent work in semantic parsing for question answering has focused on long and complicated questions, many of which would seem unnatural if asked in a normal conversation between t…
Toward Socially-Infused Information Extraction: Embedding Authors, Mentions, and Entities
Yi Yang, Ming-Wei Chang, Jacob Eisenstein
Entity linking is the task of identifying mentions of entities in text, and linking them to entries in a knowledge base. This task is especially difficult in microblogs, as there i…
S-MART: Novel Tree-based Structured Learning Algorithms Applied to Tweet Entity Linking
Yi Yang, Ming-Wei Chang
Non-linear models recently receive a lot of attention as people are starting to discover the power of statistical and embedding features. However, tree-based models are seldom stud…