Surface-Based Retrieval Reduces Perplexity of Retrieval-Augmented Language Models

• URL: http://arxiv.org/abs/2305.16243v3
• Date: Tue, 4 Jul 2023 07:59:15 GMT
• Title: Surface-Based Retrieval Reduces Perplexity of Retrieval-Augmented Language Models
• Authors: Ehsan Doostmohammadi, Tobias Norlund, Marco Kuhlmann, Richard Johansson
• Abstract summary: We study the state-of-the-art Retro model and observe that its performance gain is better explained by surface-level similarities. Inspired by this, we replace the semantic retrieval in Retro with a surface-level method based on BM25, obtaining a significant reduction in perplexity.
• Score: 1.0552465253379135
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Augmenting language models with a retrieval mechanism has been shown to significantly improve their performance while keeping the number of parameters low. Retrieval-augmented models commonly rely on a semantic retrieval mechanism based on the similarity between dense representations of the query chunk and potential neighbors. In this paper, we study the state-of-the-art Retro model and observe that its performance gain is better explained by surface-level similarities, such as token overlap. Inspired by this, we replace the semantic retrieval in Retro with a surface-level method based on BM25, obtaining a significant reduction in perplexity. As full BM25 retrieval can be computationally costly for large datasets, we also apply it in a re-ranking scenario, gaining part of the perplexity reduction with minimal computational overhead.

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