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1. Charting the Future: Using Chart Question-Answering for Scalable Evaluation of LLM-Driven Data Visualizations

   Ford, James; Zhao, Xingmeng; Schumacher, Dan; Rios, Anthony (May 2025, Proceedings of the 31st International Conference on Computational Linguistics)

   Rambow, Owen; Wanner, Leo; Apidianaki, Marianna; Khalifa, Hend; Eugenio, Barbara; Schockaert, Steven (Ed.)

   We propose a novel framework that leverages Visual Question Answering (VQA) models to automate the evaluation of LLM-generated data visualizations. Traditional evaluation methods often rely on human judgment, which is costly and unscalable, or focus solely on data accuracy, neglecting the effectiveness of visual communication. By employing VQA models, we assess data representation quality and the general communicative clarity of charts. Experiments were conducted using two leading VQA benchmark datasets, ChartQA and PlotQA, with visualizations generated by OpenAI’s GPT-3.5 Turbo and Meta’s Llama 3.1 70B-Instruct models. Our results indicate that LLM-generated charts do not match the accuracy of the original non-LLM-generated charts based on VQA performance measures. Moreover, while our results demonstrate that few-shot prompting significantly boosts the accuracy of chart generation, considerable progress remains to be made before LLMs can fully match the precision of human-generated graphs. This underscores the importance of our work, which expedites the research process by enabling rapid iteration without the need for human annotation, thus accelerating advancements in this field. 

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2. Charting the Future: Using Chart Question-Answering for Scalable Evaluation of LLM-Driven Data Visualizations

   Ford, James; Zhao, Xingmeng; Schumacher, Dan; Rios, Anthony (January 2025, Proceedings of the International Conference on Computational Linguistics)

   We propose a novel framework that leverages Visual Question Answering (VQA) models to automate the evaluation of LLM-generated data visualizations. Traditional evaluation methods often rely on human judgment, which is costly and unscalable, or focus solely on data accuracy, neglecting the effectiveness of visual communication. By employing VQA models, we assess data representation quality and the general communicative clarity of charts. Experiments were conducted using two leading VQA benchmark datasets, ChartQA and PlotQA, with visualizations generated by OpenAI’s GPT-3.5 Turbo and Meta’s Llama 3.1 70B-Instruct models. Our results indicate that LLM-generated charts do not match the accuracy of the original non-LLM-generated charts based on VQA performance measures. Moreover, while our results demonstrate that few-shot prompting significantly boosts the accuracy of chart generation, considerable progress remains to be made before LLMs can fully match the precision of human-generated graphs. This underscores the importance of our work, which expedites the research process by enabling rapid iteration without the need for human annotation, thus accelerating advancements in this field. 

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3. GuideLLM: Exploring LLM-Guided Conversation with Applications in Autobiography Interviewing

   Duan, J; Zhao, X; Zhang, Z; Ko, E; Boddy, L; Wang, C; Li, T; Rasgon, A; Hong, J; Lee, M K; et al (February 2025, https://doi.org/10.48550/arXiv.2502.06494)

   Although Large Language Models (LLMs) succeed in human-guided conversations such as instruction following and question answering, the potential of LLM-guided conversations-where LLMs direct the discourse and steer the conversation's objectives-remains under-explored. In this study, we first characterize LLM-guided conversation into three fundamental components: (i) Goal Navigation; (ii) Context Management; (iii) Empathetic Engagement, and propose GuideLLM as an installation. We then implement an interviewing environment for the evaluation of LLM-guided conversation. Specifically, various topics are involved in this environment for comprehensive interviewing evaluation, resulting in around 1.4k turns of utterances, 184k tokens, and over 200 events mentioned during the interviewing for each chatbot evaluation. We compare GuideLLM with 6 state-of-the-art LLMs such as GPT-4o and Llama-3-70b-Instruct, from the perspective of interviewing quality, and autobiography generation quality. For automatic evaluation, we derive user proxies from multiple autobiographies and employ LLM-as-a-judge to score LLM behaviors. We further conduct a human-involved experiment by employing 45 human participants to chat with GuideLLM and baselines. We then collect human feedback, preferences, and ratings regarding the qualities of conversation and autobiography. Experimental results indicate that GuideLLM significantly outperforms baseline LLMs in automatic evaluation and achieves consistent leading performances in human ratings. 

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4. GTBench: Uncovering the Strategic Reasoning Capabilities of LLMs via Game-Theoretic Evaluations

   Duan, Jinhao; Zhang, Renming; Diffenderfer, James; Kailkhura, Bhavya; Sun, Lichao; Stengel-Eskin, Elias; Bansal, Mohit; Chen, Tianlong; Xu, Kaidi (December 2024, Neural Information Processing Systems Foundation, Inc. (NeurIPS))

   As Large Language Models (LLMs) are integrated into critical real-world applications, their strategic and logical reasoning abilities are increasingly crucial. This paper evaluates LLMs' reasoning abilities in competitive environments through game-theoretic tasks, e.g., board and card games that require pure logic and strategic reasoning to compete with opponents. We first propose GTBench, a language-driven environment composing 10 widely-recognized tasks, across a comprehensive game taxonomy: complete versus incomplete information, dynamic versus static, and probabilistic versus deterministic scenarios. Then, we (1) Characterize the game-theoretic reasoning of LLMs; and (2) Perform LLM-vs.-LLM competitions as reasoning evaluation. We observe that (1) LLMs have distinct behaviors regarding various gaming scenarios; for example, LLMs fail in complete and deterministic games yet they are competitive in probabilistic gaming scenarios; (2) Most open-source LLMs, e.g., CodeLlama-34b-Instruct and Llama-2-70b-chat, are less competitive than commercial LLMs, e.g., GPT-4, in complex games, yet the recently released Llama-3-70b-Instruct makes up for this shortcoming. In addition, code-pretraining greatly benefits strategic reasoning, while advanced reasoning methods such as Chain-of-Thought (CoT) and Tree-of-Thought (ToT) do not always help. We further characterize the game-theoretic properties of LLMs, such as equilibrium and Pareto Efficiency in repeated games. Detailed error profiles are provided for a better understanding of LLMs' behavior. We hope our research provides standardized protocols and serves as a foundation to spur further explorations in the strategic reasoning of LLMs. 

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5. EvidenceBot: A Privacy-Preserving, Customizable RAG-Based Tool for Enhancing Large Language Model Interactions

   https://doi.org/10.1145/3696630.3728607  

   Khan, Nafiz Imtiaz; Filkov, Vladimir (June 2025, Proceedings of the 33rd ACM International Conference on the Foundations of Software Engineering)

   Large Language Models (LLMs) have become pivotal in reshaping the world by enabling advanced natural language processing tasks such as document analysis, content generation, and conversational assistance. Their ability to process and generate human-like text has unlocked unprecedented opportunities across different domains such as healthcare, education, finance, and more. However, commercial LLM platforms face several limitations, including data privacy concerns, context size restrictions, lack of parameter configurability, and limited evaluation capabilities. These shortcomings hinder their effectiveness, particularly in scenarios involving sensitive information, large-scale document analysis, or the need for customized output. This underscores the need for a tool that combines the power of LLMs with enhanced privacy, flexibility, and usability. To address these challenges, we present EvidenceBot, a local, Retrieval-Augmented Generation (RAG)-based solution designed to overcome the limitations of commercial LLM platforms. Evidence-Bot enables secure and efficient processing of large document sets through its privacy-preserving RAG pipeline, which extracts and appends only the most relevant text chunks as context for queries. The tool allows users to experiment with hyperparameter configurations, optimizing model responses for specific tasks, and includes an evaluation module to assess LLM performance against ground truths using semantic and similarity-based metrics. By offering enhanced privacy, customization, and evaluation capabilities, EvidenceBot bridges critical gaps in the LLM ecosystem, providing a versatile resource for individuals and organizations seeking to leverage LLMs effectively. 

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