本文主要是对 ACL 2023 Tutorial: Retrieval-based Language Models and Applications部分的Section 3: Retrieval-based LMs: Architecture进行梳理总结Roadmap检索式LM的分类nearest-neighbor LMretrieve and rea…
RALM:(retrieval-augmented language models)包含两部分内容: document selection:选择作为限定条件的documents document reading:决定如何在LM生成过程中利用选择的documents 其中retriever选择BM25,DPR,contriever,spider等,LM选择GPT,OPT,LLaMA等 从上图可以知道: 更好的retrieval model会有更低的perplexity 更大的LM也会...
Following the usage-based approach, we present a formal exemplar model that stores instances of sentences across a natural language corpus, applying recent advances from models of semantic memory. In this model, an exemplar memory is used to generate expectations about the future structure of ...
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LegaLMFiT: Efficient Short Legal Text Classification with LSTM Language Model Pre-Training Large Transformer-based language models such as BERT have led to broad performance improvements on many NLP tasks. Domain-specific variants of these models... B Clavié,A Gheewala,P Briton,... 被引量: 0...
Assessing Wikipedia-Based Cross-Language Retrieval ModelsComputer Science - Information RetrievalComputer Science - Computation and LanguageThis work compares concept models for cross-language retrieval: First, we adapt probabilistic Latent Semantic Analysis (pLSA) for multilingual documents. Experiments with ...
Next, we introduce a pretraining–multitask fine-tuning framework based on masked language models. Through joint learning, we combine auxiliary task objectives and incorporate domain knowledge to enhance the model’s semantic representation ability and knowledge perception capability. Furthermore, we ...
The first focus lies on the task of cross-language information retrieval (CLIR). The Bilingual Latent Dirichlet allocation model (BiLDA) allows us to create an interlingual, language-independent representation of both queries and documents. We construct several BiLDA-based document models for CLIR,...
Two of them are mixture models of the language models. The remaining model exploits the difference between the models. We apply the proposed methods to a cQA archive and show that they significantly outperform a widely used language model and Okapi BM25. We also show that they achieve a ...
○ Hybrid Approach: RAG utilizes a hybrid approach that combines retrieval-based and generation-based techniques, allowing it to offer both contextually relevant responses and generate novel, coherent responses. ○ Advanced Language Models: RAG is built upon state-of-the-art language models such as ...