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Publications of Torsten Hoefler
Maciej Besta, Lorenzo Paleari, Ales Kubicek, Piotr Nyczyk, Robert Gerstenberger, Patrick Iff, Tomasz Lehmann, Hubert Niewiadomski, Torsten Hoefler:

 CheckEmbed: Effective Verification of LLM Solutions to Open-Ended Tasks

(arXiv:2406.02524. Jun. 2024)


Large Language Models (LLMs) are revolutionizing various domains, yet verifying their answers remains a significant challenge, especially for intricate open-ended tasks such as consolidation, summarization, and extraction of knowledge. In this work, we propose CheckEmbed: an accurate, scalable, and simple LLM verification approach. CheckEmbed is driven by a straightforward yet powerful idea: in order to compare LLM solutions to one another or to the ground-truth, compare their corresponding answer-level embeddings obtained with a model such as GPT Text Embedding Large. This reduces a complex textual answer to a single embedding, facilitating straightforward, fast, and meaningful verification. We develop a comprehensive verification pipeline implementing the CheckEmbed methodology. The CheckEmbed pipeline also comes with metrics for assessing the truthfulness of the LLM answers, such as embedding heatmaps and their summaries. We show how to use these metrics for deploying practical engines that decide whether an LLM answer is satisfactory or not. We apply the pipeline to real-world document analysis tasks, including term extraction and document summarization, showcasing significant improvements in accuracy, cost-effectiveness, and runtime performance compared to existing token-, sentence-, and fact-level schemes such as BERTScore or SelfCheckGPT.


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  author={Maciej Besta and Lorenzo Paleari and Ales Kubicek and Piotr Nyczyk and Robert Gerstenberger and Patrick Iff and Tomasz Lehmann and Hubert Niewiadomski and Torsten Hoefler},
  title={{CheckEmbed: Effective Verification of LLM Solutions to Open-Ended Tasks}},

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