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Language is essentially a complex, intricate system of human expressions governed by grammatical rules. It poses a significant challenge to develop capable AI algorithms for comprehending and grasping a language. As a major approach,…

In recent years, Large Language Models (LLMs) have made significant strides towards Artificial General Intelligence. However, training these models from scratch requires substantial computational resources and vast amounts of text data. In…

计算与语言 · 计算机科学 2024-10-03 Wenzhen Zheng , Wenbo Pan , Xu Xu , Libo Qin , Li Yue , Ming Zhou

Large language models (LLMs) have demonstrated strong capabilities across various language tasks, notably through instruction-tuning methods. However, LLMs face challenges in visualizing complex, real-world data through charts and plots.…

机器学习 · 计算机科学 2025-02-18 Fatemeh Pesaran Zadeh , Juyeon Kim , Jin-Hwa Kim , Gunhee Kim

Equality saturation (EqSat) is a powerful optimization paradigm that compactly represents many equivalent programs in an e-graph and delays commitment until extraction selects a lowest-cost program. Making EqSat effective, therefore,…

人工智能 · 计算机科学 2026-04-21 Chenyun Yin , Youwei Xiao , Yuze Luo , Yuyang Zou , Yun Liang

Research on large language models (LLMs) has shown remarkable performance in domains such as mathematics, programming, and literary creation. However, most studies have focused on semantic memory-based question answering, neglecting LLMs'…

计算与语言 · 计算机科学 2025-02-25 WenTao Liu , Ruohua Zhang , Aimin Zhou , Feng Gao , JiaLi Liu

Embodied decision-making enables agents to translate high-level goals into executable actions through continuous interactions within the physical world, forming a cornerstone of general-purpose embodied intelligence. Large language models…

As large language models (LLMs) advance, the ultimate vision for their role in science is emerging: we could build an AI collaborator to effectively assist human beings throughout the entire scientific research process. We refer to this…

Multimodal Large Language Models (MLLMs) are widely regarded as crucial in the exploration of Artificial General Intelligence (AGI). The core of MLLMs lies in their capability to achieve cross-modal alignment. To attain this goal, current…

计算与语言 · 计算机科学 2024-11-26 Fei Zhao , Taotian Pang , Chunhui Li , Zhen Wu , Junjie Guo , Shangyu Xing , Xinyu Dai

Large Language models (LLMs), while powerful, exhibit harmful social biases. Debiasing is often challenging due to computational costs, data constraints, and potential degradation of multi-task language capabilities. This work introduces a…

计算与语言 · 计算机科学 2024-09-17 Pengrui Han , Rafal Kocielnik , Adhithya Saravanan , Roy Jiang , Or Sharir , Anima Anandkumar

The era of large language models (LLM) raises questions not only about how to train models, but also about how to evaluate them. Despite numerous existing benchmarks, insufficient attention is often given to creating assessments that test…

Large Language Models (LLMs) are advanced Artificial Intelligence (AI) systems that have undergone extensive training using large datasets in order to understand and produce language that closely resembles that of humans. These models have…

软件工程 · 计算机科学 2023-08-10 Alessio Buscemi

Large language models (LLMs) are transforming education by answering questions, explaining complex concepts, and generating content across a wide range of subjects. Despite strong performance on academic benchmarks, they often fail to…

计算与语言 · 计算机科学 2025-10-21 Numaan Naeem , Abdellah El Mekki , Muhammad Abdul-Mageed

The rapid advancement of Large Language Models (LLMs) has revolutionized various sectors by automating routine tasks, marking a step toward the realization of Artificial General Intelligence (AGI). However, they still struggle to…

机器学习 · 计算机科学 2024-02-21 Zihao Tang , Zheqi Lv , Shengyu Zhang , Fei Wu , Kun Kuang

In this paper, we introduce CodingTeachLLM, a large language model (LLM) designed for coding teaching. Specially, we aim to enhance the coding ability of LLM and lead it to better teaching mode in education context. Thus, we propose an…

机器学习 · 计算机科学 2025-04-02 Zhangquan Chen , Chunjiang Liu , Haobin Duan

With the rapid evolution of Natural Language Processing (NLP), Large Language Models (LLMs) like ChatGPT have emerged as powerful tools capable of transforming various sectors. Their vast knowledge base and dynamic interaction capabilities…

计算机与社会 · 计算机科学 2024-01-02 Kevin Wang , Jason Ramos , Ramon Lawrence

The growing complexity of construction management (CM) projects, coupled with challenges such as strict regulatory requirements and labor shortages, requires specialized analytical tools that streamline project workflow and enhance…

计算与语言 · 计算机科学 2025-04-15 Ruoxin Xiong , Yanyu Wang , Suat Gunhan , Yimin Zhu , Charles Berryman

The transformative influence of Large Language Models (LLMs) is profoundly reshaping the Artificial Intelligence (AI) technology domain. Notably, ChatGPT distinguishes itself within these models, demonstrating remarkable performance in…

软件工程 · 计算机科学 2024-05-28 Sayed Erfan Arefin , Tasnia Ashrafi Heya , Hasan Al-Qudah , Ynes Ineza , Abdul Serwadda

Training large language models (LLMs) to follow instructions has significantly enhanced their ability to tackle unseen tasks. However, despite their strong generalization capabilities, instruction-following LLMs encounter difficulties when…

计算与语言 · 计算机科学 2025-05-29 Maja Stahl , Timon Ziegenbein , Joonsuk Park , Henning Wachsmuth

Large Language Models (LLMs) have significantly advanced natural language processing (NLP), providing versatile capabilities across various applications. However, their application to complex, domain-specific tasks, such as cyber-security,…

计算与语言 · 计算机科学 2024-08-20 Matan Levi , Yair Alluouche , Daniel Ohayon , Anton Puzanov

This study deeply explores the application of large language model (LLM) in personalized recommendation system of e-commerce. Aiming at the limitations of traditional recommendation algorithms in processing large-scale and multi-dimensional…

信息检索 · 计算机科学 2024-10-18 Wei Xu , Jue Xiao , Jianlong Chen