15 citations · 18 across the 8 of their papers we have counts for
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cs.CL2023
Detrimental Contexts in Open-Domain Question Answering
Philhoon Oh, James Thorne
For knowledge intensive NLP tasks, it has been widely accepted that accessing more information is a contributing factor to improvements in the model's end-to-end performance. Howev…
cs.CL2023
Knowledge Corpus Error in Question Answering
Yejoon Lee, Philhoon Oh, James Thorne
Recent works in open-domain question answering (QA) have explored generating context passages from large language models (LLMs), replacing the traditional retrieval step in the QA…
cs.CL2023★ 15 cited
Prometheus: Inducing Fine-grained Evaluation Capability in Language Models
Seungone Kim, Jamin Shin, Yejin Cho +8
Recently, using a powerful proprietary Large Language Model (LLM) (e.g., GPT-4) as an evaluator for long-form responses has become the de facto standard. However, for practitioners…