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Language Models Enable Simple Systems for Generating Structured Views of Heterogeneous Data Lakes.

, , , , , , and . Proc. VLDB Endow., 17 (2): 92-105 (2023)

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Zoology: Measuring and Improving Recall in Efficient Language Models., , , , , , , and . CoRR, (2023)Ask Me Anything: A simple strategy for prompting language models., , , , , , , and . ICLR, OpenReview.net, (2023)Metadata Shaping: A Simple Approach for Knowledge-Enhanced Language Models., , , and . ACL (Findings), page 1733-1745. Association for Computational Linguistics, (2022)Optimizing Declarative Graph Queries at Large Scale., , , , , , and . SIGMOD Conference, page 1411-1428. ACM, (2019)RELIC: Investigating Large Language Model Responses using Self-Consistency., , , , , and . CHI, page 647:1-647:18. ACM, (2024)Metadata Shaping: Natural Language Annotations for the Tail., , , and . CoRR, (2021)Benchmarking and Building Long-Context Retrieval Models with LoCo and M2-BERT., , , , and . CoRR, (2024)Simple linear attention language models balance the recall-throughput tradeoff., , , , , , , , and . CoRR, (2024)Contextual Embeddings: When Are They Worth It?, , , and . ACL, page 2650-2663. Association for Computational Linguistics, (2020)Resources and Evaluations for Multi-Distribution Dense Information Retrieval., , and . CoRR, (2023)