88,339 research outputs found
Generator-Retriever-Generator: A Novel Approach to Open-domain Question Answering
Open-domain question answering (QA) tasks usually require the retrieval of
relevant information from a large corpus to generate accurate answers. We
propose a novel approach called Generator-Retriever-Generator (GRG) that
combines document retrieval techniques with a large language model (LLM), by
first prompting the model to generate contextual documents based on a given
question. In parallel, a dual-encoder network retrieves documents that are
relevant to the question from an external corpus. The generated and retrieved
documents are then passed to the second LLM, which generates the final answer.
By combining document retrieval and LLM generation, our approach addresses the
challenges of open-domain QA, such as generating informative and contextually
relevant answers. GRG outperforms the state-of-the-art generate-then-read and
retrieve-then-read pipelines (GENREAD and RFiD) improving their performance at
least by +5.2, +4.2, and +1.6 on TriviaQA, NQ, and WebQ datasets, respectively.
We provide code, datasets, and checkpoints
\footnote{\url{https://github.com/abdoelsayed2016/GRG}
Dual-Feedback Knowledge Retrieval for Task-Oriented Dialogue Systems
Efficient knowledge retrieval plays a pivotal role in ensuring the success of
end-to-end task-oriented dialogue systems by facilitating the selection of
relevant information necessary to fulfill user requests. However, current
approaches generally integrate knowledge retrieval and response generation,
which poses scalability challenges when dealing with extensive knowledge bases.
Taking inspiration from open-domain question answering, we propose a
retriever-generator architecture that harnesses a retriever to retrieve
pertinent knowledge and a generator to generate system responses.~Due to the
lack of retriever training labels, we propose relying on feedback from the
generator as pseudo-labels to train the retriever. To achieve this, we
introduce a dual-feedback mechanism that generates both positive and negative
feedback based on the output of the generator. Our method demonstrates superior
performance in task-oriented dialogue tasks, as evidenced by experimental
results on three benchmark datasets.Comment: Accepted to EMNLP 2023 (Main Conference
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