@inproceedings{miehling-etal-2024-language,
title = "Language Models in Dialogue: Conversational Maxims for Human-{AI} Interactions",
author = "Miehling, Erik and
Nagireddy, Manish and
Sattigeri, Prasanna and
Daly, Elizabeth M. and
Piorkowski, David and
Richards, John T.",
editor = "Al-Onaizan, Yaser and
Bansal, Mohit and
Chen, Yun-Nung",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2024",
month = nov,
year = "2024",
address = "Miami, Florida, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.findings-emnlp.843/",
doi = "10.18653/v1/2024.findings-emnlp.843",
pages = "14420--14437",
abstract = "Modern language models, while sophisticated, exhibit some inherent shortcomings, particularly in conversational settings. We claim that many of the observed shortcomings can be attributed to violation of one or more conversational principles. By drawing upon extensive research from both the social science and AI communities, we propose a set of maxims {--} quantity, quality, relevance, manner, benevolence, and transparency {--} for describing effective human-AI conversation. We first justify the applicability of the first four maxims (from Grice) in the context of human-AI interactions. We then argue that two new maxims, benevolence (concerning the generation of, and engagement with, harmful content) and transparency (concerning recognition of one`s knowledge boundaries, operational constraints, and intents), are necessary for addressing behavior unique to modern human-AI interactions. We evaluate the degree to which various language models are able to understand these maxims and find that models possess an internal prioritization of principles that can significantly impact accurate interpretability of the maxims."
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%0 Conference Proceedings
%T Language Models in Dialogue: Conversational Maxims for Human-AI Interactions
%A Miehling, Erik
%A Nagireddy, Manish
%A Sattigeri, Prasanna
%A Daly, Elizabeth M.
%A Piorkowski, David
%A Richards, John T.
%Y Al-Onaizan, Yaser
%Y Bansal, Mohit
%Y Chen, Yun-Nung
%S Findings of the Association for Computational Linguistics: EMNLP 2024
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, Florida, USA
%F miehling-etal-2024-language
%X Modern language models, while sophisticated, exhibit some inherent shortcomings, particularly in conversational settings. We claim that many of the observed shortcomings can be attributed to violation of one or more conversational principles. By drawing upon extensive research from both the social science and AI communities, we propose a set of maxims – quantity, quality, relevance, manner, benevolence, and transparency – for describing effective human-AI conversation. We first justify the applicability of the first four maxims (from Grice) in the context of human-AI interactions. We then argue that two new maxims, benevolence (concerning the generation of, and engagement with, harmful content) and transparency (concerning recognition of one‘s knowledge boundaries, operational constraints, and intents), are necessary for addressing behavior unique to modern human-AI interactions. We evaluate the degree to which various language models are able to understand these maxims and find that models possess an internal prioritization of principles that can significantly impact accurate interpretability of the maxims.
%R 10.18653/v1/2024.findings-emnlp.843
%U https://aclanthology.org/2024.findings-emnlp.843/
%U https://doi.org/10.18653/v1/2024.findings-emnlp.843
%P 14420-14437
Markdown (Informal)
[Language Models in Dialogue: Conversational Maxims for Human-AI Interactions](https://aclanthology.org/2024.findings-emnlp.843/) (Miehling et al., Findings 2024)
ACL