Papers by Ethan Selfridge

2 papers
Two-tiered Encoder-based Hallucination Detection for Retrieval-Augmented Generation in the Wild (2024.emnlp-industry)

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Challenge: Existing solutions for hallucination detection do not consider latency, train or evaluate on production data.
Approach: They propose to use customer service conversation data to evaluate existing methods . they propose to train small encoder models on a new dataset to outperform existing methods.
Outcome: The proposed model outperforms existing methods and highlights the value of combining small amounts of in-domain data with public datasets.
The economic trade-offs of large language models: A case study (2023.acl-industry)

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Challenge: Large Language Models (LLMs) are a natural fit for contact-based customer service, but their efficacy must be balanced with the cost of training and serving them.
Approach: They propose a cost framework for evaluating an NLP model’s utility for the enterprise as a function of the usefulness of the responses that they generate.
Outcome: The proposed model can be used to help human agents handle complex customer service calls and can be modified to improve their performance.

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