Web Science and Digital Libraries Research Group

Figure 1: Chen et al. NetLogo Chat asking for details about human’s needs. Introduction The integration of Large Language Models (LLMs) into computational programming is transforming the way users learn and practice coding. The study titled " Learning Agent-Based Modeling with LLM Companions: Experiences of Novices and Experts Using ChatGPT & NetLogo Chat " explores this phenomenon within the context of agent-based modeling (ABM), published at the CHI'24 conference. ABM, a method used widely in natural and social sciences, relies on simulating complex systems by applying simple rules to individual agents. The research investigates how both novices and experts interact with an LLM-enhanced interface, NetLogo Chat, designed specifically for learning and practicing ABM with the NetLogo programming language. Data The study gathered insights from 30 participants with diverse backgrounds, ranging from academia to industry, who were familiar with ABM or NetLogo. Participa...

Figure 1 Cheng et al.: RELIC allows users to search for information from large language models (A), view the model’s top response, and understand the variations between response samples to verify the correctness of the generated information (B). For long-form generated text, users can inspect the consistency of each individual claim (C) and find contradicting or supporting evidence from other samples (D). Steps (1-6) illustrate the user’s verification process of InstructGPT’s response regarding Don Featherstone.(Figure 1 in original paper ) Large Language Models (LLMs) have revolutionized natural language processing, achieving remarkable performance across various tasks, such as translation, summarization, and question-answering. Despite their success, understanding and evaluating their responses remains a significant challenge. In the paper " RELIC (Response Evaluation through Linguistic Insight and Consistency)," published in CHI 2024 by Furui Cheng , Vilém Zouhar , Si...