Feb. 13, 2024, 5:48 a.m. | Yifan Ding Amrit Poudel Qingkai Zeng Tim Weninger Balaji Veeramani Sanmitra Bhattacharya

cs.CL updates on arXiv.org arxiv.org

The ability of Large Language Models (LLMs) to generate factually correct output remains relatively unexplored due to the lack of fact-checking and knowledge grounding during training and inference. In this work, we aim to address this challenge through the Entity Disambiguation (ED) task. We first consider prompt engineering, and design a three-step hard-prompting method to probe LLMs' ED performance without supervised fine-tuning (SFT). Overall, the prompting method improves the micro-F_1 score of the original vanilla models by a large margin, …

aim challenge cs.cl design engineering fact-checking generate generative inference knowledge language language models large language large language models llms prompt through training work

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