Dense Passage Retrieval for Open-Domain Question Answering
Dense Passage Retrieval learns embeddings for questions and passages so relevant evidence can be found by vector similarity.
Paper & contextCombining retrieval with language understanding.
Chen studies language understanding, retrieval, and reasoning. This selection connects dense retrieval and sentence representations with more recent work on data selection, long-context models, and agents that use language to solve multi-step problems.
10 papers
Dense Passage Retrieval learns embeddings for questions and passages so relevant evidence can be found by vector similarity.
Paper & contextSimCSE uses contrastive learning to build sentence embeddings with a simple training setup.
Paper & contextBIG-bench collects a broad set of language-model evaluation tasks to study capabilities and limitations beyond a single benchmark.
Paper & contextSelected research in language models. Read the full paper, including the methods, experiments, and reported results.
Paper & contextSelected research in language models. Read the full paper, including the methods, experiments, and reported results.
Paper & contextSelected research in language models. Read the full paper, including the methods, experiments, and reported results.
Paper & contextSelected research in language models. Read the full paper, including the methods, experiments, and reported results.
Paper & contextSelected research in language models. Read the full paper, including the methods, experiments, and reported results.
Paper & contextSelected research in language models. Read the full paper, including the methods, experiments, and reported results.
Paper & contextSelected research in language models. Read the full paper, including the methods, experiments, and reported results.
Paper & contextAn independent editorial profile. Inclusion does not imply Council membership or endorsement. Research is collaborative; coauthorship does not imply sole credit.
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