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of 24
pro vyhledávání: '"Dejean, Herve"'
Retrieval-Augmented Generation (RAG) allows overcoming the limited knowledge of LLMs by extending the input with external information. As a consequence, the contextual inputs to the model become much longer which slows down decoding time directly tra
Externí odkaz:
http://arxiv.org/abs/2407.09252
Autor:
Rau, David, Déjean, Hervé, Chirkova, Nadezhda, Formal, Thibault, Wang, Shuai, Nikoulina, Vassilina, Clinchant, Stéphane
Retrieval-Augmented Generation allows to enhance Large Language Models with external knowledge. In response to the recent popularity of generative LLMs, many RAG approaches have been proposed, which involve an intricate number of different configurat
Externí odkaz:
http://arxiv.org/abs/2407.01102
Autor:
Chirkova, Nadezhda, Rau, David, Déjean, Hervé, Formal, Thibault, Clinchant, Stéphane, Nikoulina, Vassilina
Retrieval-augmented generation (RAG) has recently emerged as a promising solution for incorporating up-to-date or domain-specific knowledge into large language models (LLMs) and improving LLM factuality, but is predominantly studied in English-only s
Externí odkaz:
http://arxiv.org/abs/2407.01463
The late interaction paradigm introduced with ColBERT stands out in the neural Information Retrieval space, offering a compelling effectiveness-efficiency trade-off across many benchmarks. Efficient late interaction retrieval is based on an optimized
Externí odkaz:
http://arxiv.org/abs/2404.13950
Learned sparse models such as SPLADE have successfully shown how to incorporate the benefits of state-of-the-art neural information retrieval models into the classical inverted index data structure. Despite their improvements in effectiveness, learne
Externí odkaz:
http://arxiv.org/abs/2404.13357
We present a comparative study between cross-encoder and LLMs rerankers in the context of re-ranking effective SPLADE retrievers. We conduct a large evaluation on TREC Deep Learning datasets and out-of-domain datasets such as BEIR and LoTTE. In the f
Externí odkaz:
http://arxiv.org/abs/2403.10407
A companion to the release of the latest version of the SPLADE library. We describe changes to the training structure and present our latest series of models -- SPLADE-v3. We compare this new version to BM25, SPLADE++, as well as re-rankers, and show
Externí odkaz:
http://arxiv.org/abs/2403.06789
Middle training methods aim to bridge the gap between the Masked Language Model (MLM) pre-training and the final finetuning for retrieval. Recent models such as CoCondenser, RetroMAE, and LexMAE argue that the MLM task is not sufficient enough to pre
Externí odkaz:
http://arxiv.org/abs/2306.02867
Sparse neural retrievers, such as DeepImpact, uniCOIL and SPLADE, have been introduced recently as an efficient and effective way to perform retrieval with inverted indexes. They aim to learn term importance and, in some cases, document expansions, t
Externí odkaz:
http://arxiv.org/abs/2304.12702
Parameter-Efficient transfer learning with Adapters have been studied in Natural Language Processing (NLP) as an alternative to full fine-tuning. Adapters are memory-efficient and scale well with downstream tasks by training small bottle-neck layers
Externí odkaz:
http://arxiv.org/abs/2303.13220