Impact of Decoding Methods on Human Alignment of Conversational LLMs
<|reference_start|>Impact of Decoding Methods on Human Alignment of Conversational LLMs: To be included into chatbot systems, Large language models (LLMs) must be aligned with human conversational conventions. However, being trained mainly on web-scraped data gives existing LLMs a voice closer to informational text than actual human speech. In this paper, we examine the effect of decoding methods on the alignment between LLM-generated and human conversations, including Beam Search, Top K Sampling, and Nucleus Sampling. We present new measures of alignment in substance, style, and psychometric orientation, and experiment with two conversation datasets. Our results provide subtle insights: better alignment is attributed to fewer beams in Beam Search and lower values of P in Nucleus Sampling. We also find that task-oriented and open-ended datasets perform differently in terms of alignment, indicating the significance of taking into account the context of the interaction.<|reference_end|>
@article{furniturewala2024impact,
    title={Impact of Decoding Methods on Human Alignment of Conversational LLMs},
    author={Shaz Furniturewala, Kokil Jaidka, Yashvardhan Sharma},
    journal={arXiv preprint arXiv:2407.19526},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.19526},
    primaryClass={cs.CL}
}