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Large language models (LLMs) have achieved superior performance in powering text-based AI agents, endowing them with decision-making and reasoning abilities akin to humans. Concurrently, there is an emerging research trend focused on…

Computer Vision and Pattern Recognition · Computer Science 2024-02-26 Junlin Xie , Zhihong Chen , Ruifei Zhang , Xiang Wan , Guanbin Li

With the rapid evolution of global autonomous driving technology, the demand for its core sensing hardware, Light Detection and Ranging (LiDAR), is escalating. As the light source part of the LiDAR system, lasers, particularly the…

Large-language-model (LLM)-based AI agents have recently showcased impressive versatility by employing dynamic reasoning, an adaptive, multi-step process that coordinates with external tools. This shift from static, single-turn inference to…

Machine Learning · Computer Science 2026-01-08 Jiin Kim , Byeongjun Shin , Jinha Chung , Minsoo Rhu

The planning ability of Large Language Models (LLMs) has garnered increasing attention in recent years due to their remarkable capacity for multi-step reasoning and their ability to generalize across a wide range of domains. While some…

Artificial Intelligence · Computer Science 2025-02-19 Mohamed Aghzal , Erion Plaku , Gregory J. Stein , Ziyu Yao

Large Language Model (LLM) has gained popularity and achieved remarkable results in open-domain tasks, but its performance in real industrial domain-specific scenarios is average due to its lack of specific domain knowledge. This issue has…

Computation and Language · Computer Science 2023-10-17 Fangkai Yang , Pu Zhao , Zezhong Wang , Lu Wang , Jue Zhang , Mohit Garg , Qingwei Lin , Saravan Rajmohan , Dongmei Zhang

Recent advancements in Large Language Models (LLMs) have empowered LLM agents to autonomously collect world information, over which to conduct reasoning to solve complex problems. Given this capability, increasing interests have been put…

Computation and Language · Computer Science 2024-07-02 Chenchen Ye , Ziniu Hu , Yihe Deng , Zijie Huang , Mingyu Derek Ma , Yanqiao Zhu , Wei Wang

Effective prompt engineering is critical to realizing the promised productivity gains of large language models (LLMs) in knowledge-intensive tasks. Yet, many users struggle to craft prompts that yield high-quality outputs, limiting the…

Human-Computer Interaction · Computer Science 2025-10-02 Niklas Gutheil , Valentin Mayer , Leopold Müller , Jörg Rommelt , Niklas Kühl

This paper surveys the development of large language model (LLM)-based agents for question answering (QA). Traditional agents face significant limitations, including substantial data requirements and difficulty in generalizing to new…

Computation and Language · Computer Science 2025-03-26 Murong Yue

The field of catalysis holds paramount importance in shaping the trajectory of sustainable development, prompting intensive research efforts to leverage artificial intelligence (AI) in catalyst design. Presently, the fine-tuning of…

Machine Learning · Computer Science 2024-05-29 Ludi Wang , Xueqing Chen , Yi Du , Yuanchun Zhou , Yang Gao , Wenjuan Cui

Large language models (LLM) are advanced AI systems trained on extensive textual data, leveraging deep learning techniques to understand and generate human-like language. Today's LLMs with billions of parameters are so huge that hardly any…

Distributed, Parallel, and Cluster Computing · Computer Science 2025-10-14 Sheikh Azizul Hakim , Saem Hasan

The massive successes of large language models (LLMs) encourage the emerging exploration of LLM-augmented Autonomous Agents (LAAs). An LAA is able to generate actions with its core LLM and interact with environments, which facilitates the…

Large language models (LLMs) have achieved remarkable progress in solving various natural language processing tasks due to emergent reasoning abilities. However, LLMs have inherent limitations as they are incapable of accessing up-to-date…

Computation and Language · Computer Science 2023-11-01 Pan Lu , Baolin Peng , Hao Cheng , Michel Galley , Kai-Wei Chang , Ying Nian Wu , Song-Chun Zhu , Jianfeng Gao

The application of Large Language Models (LLMs) in software engineering, particularly in static analysis tasks, represents a paradigm shift in the field. In this paper, we investigate the role that current LLMs can play in improving…

Software Engineering · Computer Science 2024-02-28 Ashwin Prasad Shivarpatna Venkatesh , Samkutty Sabu , Amir M. Mir , Sofia Reis , Eric Bodden

The emergence of Generative Artificial Intelligence (AI) and Large Language Models (LLMs) has marked a new era of Natural Language Processing (NLP), introducing unprecedented capabilities that are revolutionizing various domains. This paper…

Computation and Language · Computer Science 2024-08-26 Desta Haileselassie Hagos , Rick Battle , Danda B. Rawat

Although large language models (LLMs) have demonstrated their strong intelligence ability, the high demand for computation and storage hinders their practical application. To this end, many model compression techniques are proposed to…

Computation and Language · Computer Science 2024-11-01 Ge Yang , Changyi He , Jinyang Guo , Jianyu Wu , Yifu Ding , Aishan Liu , Haotong Qin , Pengliang Ji , Xianglong Liu

Significant efforts has been made to expand the use of Large Language Models (LLMs) beyond basic language tasks. While the generalizability and versatility of LLMs have enabled widespread adoption, evolving demands in application…

Software Engineering · Computer Science 2024-11-20 Dawen Zhang , Xiwei Xu , Chen Wang , Zhenchang Xing , Robert Mao

Psychological consultation is essential for improving mental health and well-being, yet challenges such as the shortage of qualified professionals and scalability issues limit its accessibility. To address these challenges, we explore the…

Computation and Language · Computer Science 2024-08-30 Rafael Souza , Jia-Hao Lim , Alexander Davis

Large language models (LLMs) and large multimodal models (LMMs) have achieved unprecedented breakthrough, showcasing remarkable capabilities in natural language understanding, generation, and complex reasoning. This transformative potential…

Machine Learning · Computer Science 2025-10-24 Hyun Jong Yang , Hyunsoo Kim , Hyeonho Noh , Seungnyun Kim , Byonghyo Shim

Objectives: Despite the recent adoption of large language models (LLMs) for biomedical information extraction, challenges in prompt engineering and algorithms persist, with no dedicated software available. To address this, we developed…

Machine Learning · Computer Science 2025-04-02 Enshuo Hsu , Kirk Roberts

Event extraction has gained extensive research attention due to its broad range of applications. However, the current mainstream evaluation method for event extraction relies on token-level exact match, which misjudges numerous…

Computation and Language · Computer Science 2025-03-05 Yi-Fan Lu , Xian-Ling Mao , Tian Lan , Heyan Huang , Chen Xu , Xiaoyan Gao