tl;dr: This lecture delves into the various methodologies and motivations behind incorporating retrieval systems into language models, highlighting how these approaches significantly enhance the precision and effectiveness of information retrieval tasks within the realm of NLP.
🎓 Lecturer: Yatin Nandwani [ / yatinnandwani ]
🔗 Get the Slides Here: lcs2.in/llm2401
📚 Suggested Readings:
[Chapter-6, Introduction to Information Retrieval](nlp.stanford.e...)
[Reading Wikipedia to Answer Open-Domain Questions](arxiv.org/pdf/...)
[Dense Passage Retrieval for Open-Domain Question Answering](aclanthology.o...)
[Unsupervised Dense Information Retrieval with Contrastive Learning](arxiv.org/pdf/...)
[Passage Re-ranking with BERT](arxiv.org/pdf/...)
[ColBERT: Efficient and Effective Passage Search via Contextualized Late Interaction over BERT](arxiv.org/pdf/...)
[Precise Zero-Shot Dense Retrieval without Relevance Labels](arxiv.org/pdf/...)
[Transformer Memory as a Differentiable Search Index](arxiv.org/pdf/...)
This lecture focuses on the integration of retrieval methods into language models, discussing how retrieval-based models enhance the capabilities of natural language processing systems. We will explore various retrieval techniques such as sparse and dense retrieval, cross-encoder reranking, differentiable search indexes, and table-of-contents aware search. These methods are pivotal for improving the accuracy and relevance of responses generated by language models, particularly in open-domain question answering and other NLP applications.
Негізгі бет LLMs | Retrieval-based Language Models-I | Lec16.1
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