Multi-Task Retrieval-Augmented Text Generation with Relevance Sampling

By Sebastian Hofstätter, Jiecao Chen, Karthik Raman, Hamed Zamani. 2022

14 citations recorded by the source corpus.

This paper studies multi-task training of retrieval-augmented generation models for knowledge-intensive tasks. We propose to clean the training set by utilizing a distinct property of knowledge-intensive generation: The connection of query-answer pairs to items in the knowledge base. We filter training examples via a threshold of confidence on the relevance labels, whether a pair is answerable by the knowledge base or not. We train a single Fusion-in-Decoder (FiD) generator on seven combined tasks of the KILT benchmark. The experimental results suggest that our simple yet effective approach substantially improves competitive baselines on two strongly imbalanced tasks; and shows either smaller improvements or no significant regression on the remaining tasks. Furthermore, we demonstrate our multi-task training with relevance label sampling scales well with increased model capacity and achieves state-of-the-art results in five out of seven KILT tasks.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Retrieval-Augmented Generation, Natural Language Processing

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Citations in the corpus

Few-shot Learning with Retrieval Augmented Language Models (2022, methodology): “…to train retrieval-augmented generators using a second “informed” retriever with access to the output, which the test-time retriever can be distilled from, and Hofstätter et al. (2022) recently proposed a training set ﬁltering/weighting approach to train stronger retrieval-augmented generators.”