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Recent studies have uncovered the potential of Large Language Models (LLMs) in addressing complex sequential decision-making tasks through the provision of high-level instructions. However, LLM-based agents lack specialization in tackling…

Artificial Intelligence · Computer Science 2024-05-28 Zihao Zhou , Bin Hu , Chenyang Zhao , Pu Zhang , Bin Liu

Student modeling is central to many educational technologies as it enables predicting future learning outcomes and designing targeted instructional strategies. However, open-ended learning domains pose challenges for accurately modeling…

Computation and Language · Computer Science 2024-05-07 Manh Hung Nguyen , Sebastian Tschiatschek , Adish Singla

Large Language Models (LLMs), like LLaMA, have exhibited remarkable performance across various tasks. Nevertheless, when deployed to specific domains such as law or medicine, the models still confront the challenge of a deficiency in…

Computation and Language · Computer Science 2023-10-17 Quzhe Huang , Mingxu Tao , Chen Zhang , Zhenwei An , Cong Jiang , Zhibin Chen , Zirui Wu , Yansong Feng

We explore the potential of enhancing LLM performance in astronomy-focused question-answering through targeted, continual pre-training. By employing a compact 7B-parameter LLaMA-2 model and focusing exclusively on a curated set of astronomy…

Transforming a large language model (LLM) into a Vision-Language Model (VLM) can be achieved by mapping the visual tokens from a vision encoder into the embedding space of an LLM. Intriguingly, this mapping can be as simple as a shallow MLP…

Computer Vision and Pattern Recognition · Computer Science 2026-02-26 Benno Krojer , Shravan Nayak , Oscar Mañas , Vaibhav Adlakha , Desmond Elliott , Siva Reddy , Marius Mosbach

This paper explores ideas and provides a potential roadmap for the development and evaluation of physics-specific large-scale AI models, which we call Large Physics Models (LPMs). These models, based on foundation models such as Large…

This study investigates the potential of Large Language Models (LLMs) for reconstructing and constructing the physical world solely based on textual knowledge. It explores the impact of model performance on spatial understanding abilities.…

Computation and Language · Computer Science 2024-10-24 Yongqiang Huang , Wentao Ye , Liyao Li , Junbo Zhao

Educational interventions are effective tools for enhancing student learning. While Large Language Models (LLMs) allow for generating adaptive feedback at scale, current studies lack clear methodologies for providing Just-in-Time (JiT)…

Computation and Language · Computer Science 2026-05-27 Younghun Lee , Amir Bralin , Nobel Sanjay Rebello , Dan Goldwasser

The advent of Large Language Models (LLMs) has ushered in a new era for design science in Information Systems, demanding a paradigm shift in tailoring LLMs design for business contexts. We propose and test a novel framework to customize…

Computers and Society · Computer Science 2024-05-15 Wen Wang , Zhenyue Zhao , Tianshu Sun

Online education platforms, leveraging the internet to distribute education resources, seek to provide convenient education but often fall short in real-time communication with students. They often struggle to address the diverse obstacles…

Artificial Intelligence · Computer Science 2024-04-29 Qingyao Li , Lingyue Fu , Weiming Zhang , Xianyu Chen , Jingwei Yu , Wei Xia , Weinan Zhang , Ruiming Tang , Yong Yu

Large language models (LLMs) achieve impressive results over various tasks, and ever-expanding public repositories contain an abundance of pre-trained models. Therefore, identifying the best-performing LLM for a given task is a significant…

Computation and Language · Computer Science 2025-11-13 Idan Kashani , Avi Mendelson , Yaniv Nemcovsky

The emergence of large language models (LLMs) has revolutionized artificial intelligence, offering unprecedented capabilities in reasoning, generalization, and zero-shot learning. These strengths open new frontiers in wireless…

Artificial Intelligence · Computer Science 2026-03-18 Le Liang , Hao Ye , Yucheng Sheng , Ouya Wang , Jiacheng Wang , Shi Jin , Geoffrey Ye Li

The era of Large Language Models (LLMs) presents a new opportunity for interpretability--agentic interpretability: a multi-turn conversation with an LLM wherein the LLM proactively assists human understanding by developing and leveraging a…

Artificial Intelligence · Computer Science 2025-06-17 Been Kim , John Hewitt , Neel Nanda , Noah Fiedel , Oyvind Tafjord

Large Language Models (LLMs) have been transformative. They are pre-trained foundational models that are self-supervised and can be adapted with fine tuning to a wide range of natural language tasks, each of which previously would have…

Computation and Language · Computer Science 2023-02-22 Terrence Sejnowski

Recent large language models (LLMs) have demonstrated remarkable performance on a variety of natural language processing (NLP) tasks, leading to intense excitement about their applicability across various domains. Unfortunately, recent work…

Computation and Language · Computer Science 2023-02-13 Yaqi Xie , Chen Yu , Tongyao Zhu , Jinbin Bai , Ze Gong , Harold Soh

In recent years, pre-trained large language models (LLMs) have demonstrated remarkable efficiency in achieving an inference-time few-shot learning capability known as in-context learning. However, existing literature has highlighted the…

Computation and Language · Computer Science 2024-02-14 Xinyi Wang , Wanrong Zhu , Michael Saxon , Mark Steyvers , William Yang Wang

Large Language Models (LLMs) create exciting possibilities for powerful language processing tools to accelerate research in materials science. While LLMs have great potential to accelerate materials understanding and discovery, they…

Materials Science · Physics 2024-09-26 Santiago Miret , N M Anoop Krishnan

This work explores expanding the capabilities of large language models (LLMs) pretrained on text to generate 3D meshes within a unified model. This offers key advantages of (1) leveraging spatial knowledge already embedded in LLMs, derived…

Machine Learning · Computer Science 2024-11-15 Zhengyi Wang , Jonathan Lorraine , Yikai Wang , Hang Su , Jun Zhu , Sanja Fidler , Xiaohui Zeng

Large language models (LLMs) often struggle with complex mathematical tasks, prone to "hallucinating" incorrect answers due to their reliance on statistical patterns. This limitation is further amplified in average Small LangSLMs with…

Large language models (LLMs) achieve state-of-the-art results across many natural language tasks, but their internal mechanisms remain difficult to interpret. In this work, we extract, process, and visualize latent state geometries in…

Machine Learning · Computer Science 2026-01-06 Alex Ning , Vainateya Rangaraju , Yen-Ling Kuo