15 citations · 18 across the 17 of their papers we have counts for
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cs.CL2023★ 1 cited
How Well Do Large Language Models Truly Ground?
Hyunji Lee, Sejune Joo, Chaeeun Kim +4
To reduce issues like hallucinations and lack of control in Large Language Models (LLMs), a common method is to generate responses by grounding on external contexts given as input,…
cs.CL2023★ 1 cited
KTRL+F: Knowledge-Augmented In-Document Search
Hanseok Oh, Haebin Shin, Miyoung Ko +2
We introduce a new problem KTRL+F, a knowledge-augmented in-document search task that necessitates real-time identification of all semantic targets within a document with the aware…
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…