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cs.CL2023★ 1 cited
How Far Are LLMs from Believable AI? A Benchmark for Evaluating the Believability of Human Behavior Simulation
Yang Xiao, Yi Cheng, Jinlan Fu +3
In recent years, AI has demonstrated remarkable capabilities in simulating human behaviors, particularly those implemented with large language models (LLMs). However, due to the la…
cs.CL2023
Self-Detoxifying Language Models via Toxification Reversal
Chak Tou Leong, Yi Cheng, Jiashuo Wang +2
Language model detoxification aims to minimize the risk of generating offensive or harmful content in pretrained language models (PLMs) for safer deployment. Existing methods can b…
cs.CL2023★ 1 cited
Aligning Language Models with Human Preferences via a Bayesian Approach
Jiashuo Wang, Haozhao Wang, Shichao Sun +1
In the quest to advance human-centric natural language generation (NLG) systems, ensuring alignment between NLG models and human preferences is crucial. For this alignment, current…