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We introduce GuessingGame, a protocol for evaluating large language models (LLMs) as strategic question-askers in open-ended, open-domain settings. A Guesser LLM identifies a hidden object by posing free-form questions to an Oracle without…

计算与语言 · 计算机科学 2025-09-25 Dylan Hutson , Daniel Vennemeyer , Aneesh Deshmukh , Justin Zhan , Tianyu Jiang

Automated negotiation in complex, multi-party and multi-issue settings critically depends on accurate opponent modeling. However, conventional numerical-only approaches fail to capture the qualitative information embedded in natural…

Generative artificial intelligence (GenAI) can rapidly produce large and diverse volumes of content. This lends to it a quality of creativity which can be empowering in the early stages of design. In seeking to understand how creative ways…

人机交互 · 计算机科学 2024-03-20 Gionnieve Lim , Simon T. Perrault

This paper presents an innovative exploration of the application potential of large language models (LLM) in addressing the challenging task of automatically generating behavior trees (BTs) for complex tasks. The conventional manual BT…

计算与语言 · 计算机科学 2024-01-17 Fu Li , Xueying Wang , Bin Li , Yunlong Wu , Yanzhen Wang , Xiaodong Yi

Large Language Models (LLMs) have significantly advanced natural language processing, demonstrating exceptional reasoning, tool usage, and memory capabilities. As their applications expand into multi-agent environments, there arises a need…

计算与语言 · 计算机科学 2024-11-28 Lin Xu , Zhiyuan Hu , Daquan Zhou , Hongyu Ren , Zhen Dong , Kurt Keutzer , See Kiong Ng , Jiashi Feng

Many real-world optimization problems are guided by complex, subjective preferences that are difficult to express as explicit closed-form objectives. In response, we introduce Language-in-the-Loop Optimization (LILO), a Bayesian…

Large Language Models (LLMs) have quickly become an invaluable assistant for a variety of tasks. However, their effectiveness is constrained by their ability to tailor responses to human preferences and behaviors via personalization. Prior…

计算与语言 · 计算机科学 2024-11-21 Lucie Charlotte Magister , Katherine Metcalf , Yizhe Zhang , Maartje ter Hoeve

This paper investigates the potential of AI models, particularly large language models (LLMs), to support knowledge exploration and augment human creativity during ideation. We present "Latent Lab" an interactive tool for discovering…

人工智能 · 计算机科学 2023-11-23 Kevin Dunnell , Trudy Painter , Andrew Stoddard , Andy Lippman

Driven by curiosity, humans have continually sought to explore and understand the world around them, leading to the invention of various tools to satiate this inquisitiveness. Despite not having the capacity to process and memorize vast…

人工智能 · 计算机科学 2024-01-11 Haojie Pan , Zepeng Zhai , Hao Yuan , Yaojia Lv , Ruiji Fu , Ming Liu , Zhongyuan Wang , Bing Qin

The study of biological materials and bio-inspired materials science is well established; however, surprisingly little knowledge has been systematically translated to engineering solutions. To accelerate discovery and guide insights, an…

材料科学 · 物理学 2023-12-12 Rachel K. Luu , Markus J. Buehler

Accurate beam prediction is a key enabler for next-generation wireless communication systems. In this paper, we propose a multimodal large language model (LLM)-based beam prediction framework that effectively utilizes contextual…

信号处理 · 电气工程与系统科学 2026-03-24 Tianhao Mao , Le Liang , Jie Yang , Xiao Li , Shi Jin , Geoffrey Ye Li

Large Language Models (LLMs) are a transformational technology, fundamentally changing how people obtain information and interact with the world. As people become increasingly reliant on them for an enormous variety of tasks, a body of…

计算机与社会 · 计算机科学 2025-05-08 Nouar Aldahoul , Hazem Ibrahim , Matteo Varvello , Aaron Kaufman , Talal Rahwan , Yasir Zaki

A Large Language Model (LLM) represents a cutting-edge artificial intelligence model that generates coherent content, including grammatically precise sentences, human-like paragraphs, and syntactically accurate code snippets. LLMs can play…

软件工程 · 计算机科学 2023-12-11 Robson Santos , Italo Santos , Cleyton Magalhaes , Ronnie de Souza Santos

As the construction industry advances toward digital transformation, BIM (Building Information Modeling)-based design has become a key driver supporting intelligent construction. Despite Large Language Models (LLMs) have shown potential in…

人工智能 · 计算机科学 2026-02-25 Jia-Rui Lin , Yun-Hong Cai , Xiang-Rui Ni , Shaojie Zhou , Peng Pan

We consider the problem of aligning a large language model (LLM) to model the preferences of a human population. Modeling the beliefs, preferences, and behaviors of a specific population can be useful for a variety of different…

计算与语言 · 计算机科学 2024-04-01 Keiichi Namikoshi , Alex Filipowicz , David A. Shamma , Rumen Iliev , Candice L. Hogan , Nikos Arechiga

There is growing interest in using machine learning (ML) to support clinical diagnosis, but most approaches rely on static, fully observed datasets and fail to reflect the sequential, resource-aware reasoning clinicians use in practice.…

人工智能 · 计算机科学 2025-11-18 Silas Ruhrberg Estévez , Nicolás Astorga , Mihaela van der Schaar

Large Language Models (LLMs) are now widely used for query reformulation and expansion in Information Retrieval, with many studies reporting substantial effectiveness gains. However, these results are typically obtained under heterogeneous…

Large Language Models (LLMs) have emerged as formidable instruments capable of comprehending and producing human-like text. This paper explores the potential of LLMs, to shape user perspectives and subsequently influence their decisions on…

人工智能 · 计算机科学 2024-09-04 Ganesh Prasath Ramani , Shirish Karande , Santhosh V , Yash Bhatia

As the applications of large language models (LLMs) expand across diverse fields, the ability of these models to adapt to ongoing changes in data, tasks, and user preferences becomes crucial. Traditional training methods, relying on static…

机器学习 · 计算机科学 2024-06-11 Junhao Zheng , Shengjie Qiu , Chengming Shi , Qianli Ma

Large Language Models (LLMs) have demonstrated remarkable in-context learning capabilities, enabling flexible utilization of limited historical information to play pivotal roles in reasoning, problem-solving, and complex pattern recognition…

机器学习 · 计算机科学 2025-03-31 Zhonglin Jiang , Qian Tang , Zequn Wang