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Chris Tar

2 accepted papers

2024

Foundational Autoraters: Taming Large Language Models for Better Automatic Evaluation

EMNLP 2024main

As large language models (LLMs) evolve, evaluating their output reliably becomes increasingly difficult due to the high cost of human evaluation. To address this, we introduce FLAMe, a family of Foundational Large Autorater Models. FLAMe is trained on a diverse set of over 100 quality assessment tas…

Cited by 41SourcePDFScholar
2024

FreshLLMs: Refreshing Large Language Models with Search Engine Augmentation

ACL 2024findings

Since most large language models (LLMs) are trained once and never updated, they struggle to dynamically adapt to our ever-changing world. In this work, we present FreshQA, a dynamic QA benchmark that tests a model’s ability to answer questions that may require reasoning over up-to-date world knowle…