Summary of Language Models in Dialogue: Conversational Maxims For Human-ai Interactions, by Erik Miehling et al.
Language Models in Dialogue: Conversational Maxims for Human-AI Interactions
by Erik Miehling, Manish Nagireddy, Prasanna Sattigeri, Elizabeth M. Daly, David Piorkowski, John T. Richards
First submitted to arxiv on: 22 Mar 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed paper addresses the limitations of modern language models in conversational settings by introducing a set of conversational principles drawn from social science and AI research. The authors argue that these principles, including quantity, quality, relevance, manner, benevolence, and transparency, are essential for effective human-AI conversation. By examining the internal prioritization of principles within various language models, the study finds that models’ ability to interpret conversational maxims can be significantly impacted by their internal prioritizations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper explores ways to improve conversations between humans and AI systems by proposing a set of principles for effective communication. The authors identify limitations in current language models and suggest new principles like benevolence and transparency are needed to address unique aspects of human-AI interactions. They evaluate various language models’ ability to understand these principles, showing that internal prioritizations can affect their interpretation accuracy. |
