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Towards a Holistic Evaluation of LLMs on Factual Knowledge Recall
April 26, 2024, 4:42 a.m. | Jiaqing Yuan, Lin Pan, Chung-Wei Hang, Jiang Guo, Jiarong Jiang, Bonan Min, Patrick Ng, Zhiguo Wang
cs.LG updates on arXiv.org arxiv.org
Abstract: Large language models (LLMs) have shown remarkable performance on a variety of NLP tasks, and are being rapidly adopted in a wide range of use cases. It is therefore of vital importance to holistically evaluate the factuality of their generated outputs, as hallucinations remain a challenging issue.
In this work, we focus on assessing LLMs' ability to recall factual knowledge learned from pretraining, and the factors that affect this ability. To that end, we construct …
abstract arxiv cases cs.ai cs.cl cs.lg evaluation generated hallucinations importance knowledge language language models large language large language models llms nlp performance recall tasks type use cases vital
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