"Is ChatGPT a Better Explainer than My Professor?": Evaluating the Explanation Capabilities of LLMs in Conversation Compared to a Human Baseline

Explanations form the foundation of knowledge sharing and build upon communication principles, social dynamics, and learning theories. We focus specifically on conversational approaches for explanations because the context is highly adaptive and interactive. Our research leverages previous work on explanatory acts, a framework for understanding the different strategies that explainers and explainees employ in a conversation to both explain, understand, and engage with the other party. We use the 5-Levels dataset was constructed from the WIRED YouTube series by Wachsmuth et al., and later annotated by Booshehri et al. with explanatory acts. These annotations provide a framework for understanding how explainers and explainees structure their response when crafting a response. With the rise of generative AI in the past year, we hope to better understand the capabilities of Large Language Models (LLMs) and how they can augment expert explainer's capabilities in conversational settings. To achieve this goal, the 5-Levels dataset (We use Booshehri et al.'s 2023 annotated dataset with explanatory acts.) allows us to audit the ability of LLMs in engaging in explanation dialogues. To evaluate the effectiveness of LLMs in generating explainer responses, we compared 3 different strategies, we asked human annotators to evaluate 3 different strategies: human explainer response, GPT4 standard response, GPT4 response with Explanation Moves.

Tweetorial Hooks: Generative AI Tools to Motivate Science on Social Media

Communicating science and technology is essential for the public to understand and engage in a rapidly changing world. Tweetorials are an emerging phenomenon where experts explain STEM topics on social media in creative and engaging ways. However, STEM experts struggle to write an engaging "hook" in the first tweet that captures the reader's attention. We propose methods to use large language models (LLMs) to help users scaffold their process of writing a relatable hook for complex scientific topics. We demonstrate that LLMs can help writers find everyday experiences that are relatable and interesting to the public, avoid jargon, and spark curiosity. Our evaluation shows that the system reduces cognitive load and helps people write better hooks. Lastly, we discuss the importance of interactivity with LLMs to preserve the correctness, effectiveness, and authenticity of the writing.