Atlas reaches over 42% accuracy on Natural Questions with only 64 examples, outperforming a 540B-parameter model by 3% with 50x fewer parameters.
arXiv preprint arXiv:2006.03659
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Contrastive learning trains unsupervised dense retrievers that beat BM25 on most BEIR datasets and support cross-lingual retrieval across scripts.
Authors create LLM-Fake Theory integrating social psychology, then use a prompt engineering pipeline to build the MegaFake dataset of LLM-generated fake news for advancing detection methods.
citing papers explorer
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Atlas: Few-shot Learning with Retrieval Augmented Language Models
Atlas reaches over 42% accuracy on Natural Questions with only 64 examples, outperforming a 540B-parameter model by 3% with 50x fewer parameters.
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Unsupervised Dense Information Retrieval with Contrastive Learning
Contrastive learning trains unsupervised dense retrievers that beat BM25 on most BEIR datasets and support cross-lingual retrieval across scripts.
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MegaFake: A Theory-Driven Dataset of Fake News Generated by Large Language Models
Authors create LLM-Fake Theory integrating social psychology, then use a prompt engineering pipeline to build the MegaFake dataset of LLM-generated fake news for advancing detection methods.