cTBL: Augmenting Large Language Models for Conversational Tables

03/21/2023

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by   Anirudh S Sundar, et al.

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An open challenge in multimodal conversational AI requires augmenting large language models with information from textual and non-textual sources for multi-turn dialogue. To address this problem, this paper introduces Conversational Tables (cTBL), a three-step encoder-decoder approach to retrieve tabular information and generate dialogue responses grounded on the retrieved information. cTBL uses Transformer encoder embeddings for Dense Table Retrieval and obtains up to 5 sparse retrieval on the HyrbiDialogue dataset. Additionally, cTBL performs tabular knowledge retrieval using both encoder and decoder models, resulting in up to 46 response generation on HyrbiDialogue.