cTBLS: Augmenting Large Language Models with Conversational Tables

• URL: http://arxiv.org/abs/2303.12024v3
• Date: Wed, 31 May 2023 00:44:56 GMT
• Title: cTBLS: Augmenting Large Language Models with Conversational Tables
• Authors: Anirudh S Sundar, Larry Heck
• Abstract summary: Conversational Tables (cTBLS) is a three-step architecture to retrieve and generate dialogue responses grounded on retrieved tabular information. Human evaluators prefer cTBLs +80% of the time (coherency, fluency) and judge informativeness to be 4x better than the previous state-of-the-art.
• Score: 0.76146285961466
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Optimizing accuracy and performance while eliminating hallucinations of open-domain conversational large language models (LLMs) is an open research challenge. A particularly promising direction is to augment and ground LLMs with information from structured sources. This paper introduces Conversational Tables (cTBLS), a three-step architecture to retrieve and generate dialogue responses grounded on retrieved tabular information. cTBLS uses Transformer encoder embeddings for Dense Table Retrieval and obtains up to 125% relative improvement over the retriever in the previous state-of-the-art system on the HyrbiDialogue dataset. cTBLS then uses a shared process between encoder and decoder models to perform a coarse+fine tabular knowledge (e.g., cell) ranking combined with a GPT-3.5 LLM response generator to yield a 2x relative improvement in ROUGE scores. Finally, human evaluators prefer cTBLs +80% of the time (coherency, fluency) and judge informativeness to be 4x better than the previous state-of-the-art.

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