Combining the best of both worlds: Retrieval-enhanced generation for knowledge-based natural language processing

Knowledge-intensive natural language processing (NLP) involves tasks that require deep understanding and manipulation of vast factual information. These tasks challenge models to effectively access, retrieve, and use external knowledge sources, producing accurate and relevant results. NLP models have advanced significantly, yet their ability to handle knowledge-based tasks still needs to be improved due to their static nature and inability to dynamically incorporate external knowledge.

The main challenge in knowledge-rich NLP tasks is that large pre-trained language models are required to properly access and manipulate knowledge. These models often need help justifying their decisions and updating their knowledge base. This limitation results in models that cannot effectively handle tasks requiring dynamic knowledge access and integration. Consequently, new architectures are needed that can dynamically and flexibly incorporate external information.

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Current research includes frameworks such as REALM and ORQA, which combine pre-trained neural language models with various retrievables to better access information. Memory networks, stack-augmented networks, and memory layers underpin nonparametric memory systems. General-purpose architectures such as BERT, GPT-2, and BART perform well on various NLP tasks. Retrieval-based methods, such as dense-passage retrieval, improve performance in answering open-domain queries, fact-checking, and query generation, demonstrating the benefits of integrating retrieval methods into NLP models. .

Researchers at Facebook AI Research, University College London, and New York University have introduced Retrieval-Augmented Generation (RAG) models to overcome these limitations. RAG models combine the parametric memory of pre-trained seq2seq models with non-parametric memory from Wikipedia's dense vector index. This hybrid approach increases the efficiency of creative tasks by dynamically accessing and integrating external knowledge, thereby overcoming the static nature of traditional models.

RAG models use a pre-trained neural retriever to access relevant passages from Wikipedia and a seq2seq transformer (BART) to generate answers. The retriever provides the top-K documents based on the input query, and the generator produces the output by conditioning these documents. There are two variants of RAG: RAG-Sequence, which uses the same document for all tokens, and RAG-Token, which allows different documents for each token. This structure enables the model to generate more accurate and contextually relevant responses by leveraging both parametric and non-parametric memory.

The performance of RAG models is remarkable in a number of cognitive tasks. On open-domain QA tasks, RAG models generate new state-of-the-art results. For example, in Natural Questions (NQ), TriviaQA, and WebQuestions, RAG achieved high exact match scores, outperforming parametric seq2seq models and task-specific retrieval and extract architectures. RAG retrieval conducted using DPR retrieval with retrieval monitoring on naturalistic questions and trivia QA contributed significantly to these results. Furthermore, for MS-MARCO NLG, RAG-Sequence outperformed BART by 2.6 Bleu points and 2.6 Rouge-L points, producing more realistic, distinctive and diverse language.

The researchers demonstrated that the RAG model offers several advantages. They showed that combining parametric and nonparametric memory with generation tasks significantly improved performance. RAG models produced more factual and specific responses than BART, with human raters preferring RAG results. In the FEVER validation, RAG achieved results within 4.3% of state-of-the-art models, demonstrating its utility in both generative and classification tasks.

Finally, the introduction of RAG models represents an important advance in handling knowledge-rich NLP tasks. By efficiently combining parametric and non-parametric memories, RAG models offer a robust solution for dynamic knowledge access and generation, setting a new standard in the field. A research team from Facebook AI Research, University College London, and New York University has paved the way for future developments in NLP, highlighting the potential for further improvements in dynamic knowledge integration.

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Nikhil is an intern consultant at Marktech Post. He is pursuing an integrated dual degree in Materials at the Indian Institute of Technology, Kharagpur. Nikhil is an AI/ML enthusiast who is always researching applications in fields like biomaterials and biomedical science. With a strong background in materials science, he is exploring new developments and creating partnership opportunities.

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