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Large language models (LLMs) have demonstrated impressive abilities in generating unstructured natural language according to instructions. However, their performance can be inconsistent when tasked with producing text that adheres to…

计算与语言 · 计算机科学 2024-02-22 Yinghao Li , Rampi Ramprasad , Chao Zhang

The ability of Large Language Models (LLMs) to generate high-quality text and code has fuelled their rise in popularity. In this paper, we aim to demonstrate the potential of LLMs within the realm of optimization algorithms by integrating…

人工智能 · 计算机科学 2024-02-14 Camilo Chacón Sartori , Christian Blum , Gabriela Ochoa

Large Language Models (LLMs) are nowadays extensively used for various types of software engineering tasks, primarily code generation. Previous research has shown how suitable prompt engineering could help developers in improving their code…

Instruction-tuned large language models (LLMs) are capable of generating stories in response to open-ended user requests, but the resulting stories tend to be limited in their diversity. Older, symbolic approaches to story generation (such…

计算与语言 · 计算机科学 2024-07-23 Phoebe J. Wang , Max Kreminski

Can LLMs provide support to creative writers by giving meaningful writing feedback? In this paper, we explore the challenges and limitations of model-generated writing feedback by defining a new task, dataset, and evaluation frameworks. To…

计算与语言 · 计算机科学 2025-07-23 Hannah Rashkin , Elizabeth Clark , Fantine Huot , Mirella Lapata

Reasoning based on Large Language Models (LLMs) has garnered increasing attention due to outstanding performance of these models in mathematical and complex logical tasks. Beginning with the Chain-of-Thought (CoT) prompting technique,…

人工智能 · 计算机科学 2025-11-27 Yuto Suzuki , Farnoush Banaei-Kashani

The rise of large language models (LLMs) has highlighted the importance of prompt engineering as a crucial technique for optimizing model outputs. While experimentation with various prompting methods, such as Few-shot, Chain-of-Thought, and…

人工智能 · 计算机科学 2026-03-27 Michael Hewing , Vincent Leinhos

Large language models (LLMs) have taken the scientific world by storm, changing the landscape of natural language processing and human-computer interaction. These powerful tools can answer complex questions and, surprisingly, perform…

人工智能 · 计算机科学 2023-11-14 Pier Luca Lanzi , Daniele Loiacono

Large language models (LLMs) have changed the reality of how software is produced. Within the wider software engineering community, among many other purposes, they are explored for code generation use cases from different types of input. In…

软件工程 · 计算机科学 2025-08-01 Fabian Stiehle , Hans Weytjens , Ingo Weber

Large Language Models (LLMs) are rapidly transforming various fields, and their potential in Business Process Management (BPM) is substantial. This paper assesses the capabilities of LLMs on business process modeling using a framework for…

数据库 · 计算机科学 2024-12-03 Humam Kourani , Alessandro Berti , Daniel Schuster , Wil M. P. van der Aalst

The powerful ability to understand, follow, and generate complex language emerging from large language models (LLMs) makes LLM-generated text flood many areas of our daily lives at an incredible speed and is widely accepted by humans. As…

计算与语言 · 计算机科学 2024-04-22 Junchao Wu , Shu Yang , Runzhe Zhan , Yulin Yuan , Derek F. Wong , Lidia S. Chao

Large Language Models (LLMs) have ushered in a new era of text annotation, as their ease-of-use, high accuracy, and relatively low costs have meant that their use has exploded in recent months. However, the rapid growth of the field has…

计算与语言 · 计算机科学 2024-02-09 Petter Törnberg

Large Language Models (LLMs) have enabled new ways to satisfy information needs. Although great strides have been made in applying them to settings like document ranking and short-form text generation, they still struggle to compose…

Large Language Models (LLMs) have recently demonstrated impressive capabilities across various real-world applications. However, due to the current text-in-text-out paradigm, it remains challenging for LLMs to handle dynamic and complex…

人工智能 · 计算机科学 2024-10-25 Timothy Wei , Annabelle Miin , Anastasia Miin

Large language models (LLMs) are becoming increasingly better at a wide range of Natural Language Processing tasks (NLP), such as text generation and understanding. Recently, these models have extended their capabilities to coding tasks,…

机器学习 · 计算机科学 2024-10-23 Nishat Raihan , Mohammed Latif Siddiq , Joanna C. S. Santos , Marcos Zampieri

Graph model generation from natural language description is an important task with many applications in software engineering. With the rise of large language models (LLMs), there is a growing interest in using LLMs for graph model…

软件工程 · 计算机科学 2025-08-04 Boqi Chen , Ou Wei , Bingzhou Zheng , Gunter Mussbacher

The ability of large language models to generate complex texts allows them to be widely integrated into many aspects of life, and their output can quickly fill all network resources. As the impact of LLMs grows, it becomes increasingly…

计算与语言 · 计算机科学 2024-11-12 Yongye Su , Yuqing Wu

Large language models (LLMs) are gaining increasing popularity in both academia and industry, owing to their unprecedented performance in various applications. As LLMs continue to play a vital role in both research and daily use, their…

In Natural Language Processing (NLP), Large Language Models (LLMs) have demonstrated high text generation quality. However, in real-world applications, LLMs must meet increasingly complex requirements. Beyond avoiding misleading or…

计算与语言 · 计算机科学 2024-08-23 Xun Liang , Hanyu Wang , Yezhaohui Wang , Shichao Song , Jiawei Yang , Simin Niu , Jie Hu , Dan Liu , Shunyu Yao , Feiyu Xiong , Zhiyu Li

Decoding strategies for generative large language models (LLMs) are a critical but often underexplored aspect of text generation tasks. Guided by specific hyperparameters, these strategies aim to transform the raw probability distributions…

计算与语言 · 计算机科学 2024-12-17 Esteban Garces Arias , Meimingwei Li , Christian Heumann , Matthias Aßenmacher