June 11, 2024, 4:41 a.m. | Behnam Mohammadi

cs.CL updates on arXiv.org arxiv.org

arXiv:2406.05587v1 Announce Type: new
Abstract: Large Language Models (LLMs) have revolutionized natural language processing but can exhibit biases and may generate toxic content. While alignment techniques like Reinforcement Learning from Human Feedback (RLHF) reduce these issues, their impact on creativity, defined as syntactic and semantic diversity, remains unexplored. We investigate the unintended consequences of RLHF on the creativity of LLMs through three experiments focusing on the Llama-2 series. Our findings reveal that aligned models exhibit lower entropy in token predictions, …

abstract alignment arxiv biases chat creativity cs.ai cs.cl diversity feedback generate human human feedback impact language language models language processing large language large language models llms natural natural language natural language processing price processing reduce reinforcement reinforcement learning rlhf semantic type while

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