most citedEvaluation Metrics in the Era of GPT-4: Reliably Evaluating Large Language Models on Sequence to Sequence Tasks

1 citations · 1 across the 1 of their papers we have counts for

collaborators

3 papers

cs.CL2024

Grammatical Error Correction for Code-Switched Sentences by Learners of English

Kelvin Wey Han Chan, Christopher Bryant, Li Nguyen +2

Code-switching (CSW) is a common phenomenon among multilingual speakers where multiple languages are used in a single discourse or utterance. Mixed language utterances may still co…

cs.CL2024

Prompting open-source and commercial language models for grammatical error correction of English learner text

Christopher Davis, Andrew Caines, Øistein Andersen +6

Thanks to recent advances in generative AI, we are able to prompt large language models (LLMs) to produce texts which are fluent and grammatical. In addition, it has been shown tha…

cs.CL2023★ 1 cited

Evaluation Metrics in the Era of GPT-4: Reliably Evaluating Large Language Models on Sequence to Sequence Tasks

Andrea Sottana, Bin Liang, Kai Zou +1

Large Language Models (LLMs) evaluation is a patchy and inconsistent landscape, and it is becoming clear that the quality of automatic evaluation metrics is not keeping up with the…