Impact of Decoding Methods on Human Alignment of Conversational LLMs

• URL: http://arxiv.org/abs/2407.19526v1
• Date: Sun, 28 Jul 2024 16:31:09 GMT
• Title: Impact of Decoding Methods on Human Alignment of Conversational LLMs
• Authors: Shaz Furniturewala, Kokil Jaidka, Yashvardhan Sharma,
• Abstract summary: We present new measures of alignment in substance, style, and psychometric orientation. We find that task-oriented and open-ended datasets perform differently in terms of alignment.
• Score: 11.673312535250263
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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