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相关论文: From Prompts to Templates: A Systematic Prompt Tem…

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Large Language Models (LLMs), such as ChatGPT, exhibit advanced capabilities in generating text, images, and videos. However, their effective use remains constrained by challenges in prompt formulation, personalization, and opaque…

人机交互 · 计算机科学 2025-03-04 Si Thu , A. Baki Kocaballi

To help users do complex work, researchers have developed techniques to integrate AI and human intelligence into user interfaces (UIs). With the recent introduction of large language models (LLMs), which can generate text in response to a…

人机交互 · 计算机科学 2023-07-04 Stephen MacNeil , Andrew Tran , Joanne Kim , Ziheng Huang , Seth Bernstein , Dan Mogil

Large language models (LLMs) enable system builders today to create competent NLP systems through prompting, where they only need to describe the task in natural language and provide a few examples. However, in other ways, LLMs are a step…

计算与语言 · 计算机科学 2023-08-24 Vijay Viswanathan , Chenyang Zhao , Amanda Bertsch , Tongshuang Wu , Graham Neubig

With the advent of Large Language Models (LLMs), generating rule-based data for real-world applications has become more accessible. Due to the inherent ambiguity of natural language and the complexity of rule sets, especially in long…

计算与语言 · 计算机科学 2025-04-21 Teng Wang , Zhenqi He , Wing-Yin Yu , Xiaojin Fu , Xiongwei Han

Large Language Models (LLMs) have transformed human-computer interaction by enabling natural language-based communication with AI-powered chatbots. These models are designed to be intuitive and user-friendly, allowing users to articulate…

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,…

Large Language Model (LLM) assistants, such as ChatGPT, have emerged as potential alternatives to search methods for helping users navigate complex, feature-rich software. LLMs use vast training data from domain-specific texts, software…

人机交互 · 计算机科学 2024-02-14 Anjali Khurana , Hari Subramonyam , Parmit K Chilana

This research focuses on how Large Language Models (LLMs) can help with (path) planning for mobile embodied agents such as robots, in a human-in-the-loop and interactive manner. A novel framework named LLM A*, aims to leverage the…

机器人学 · 计算机科学 2025-05-16 Hengjia Xiao , Peng Wang , Mingzhe Yu , Mattia Robbiani

Large Language Models (LLMs) have emerged as coding assistants, capable of generating source code from natural language prompts. With the increasing adoption of LLMs in software development, academic research and industry based projects are…

LLMs have gained immense popularity among researchers and the general public for its impressive capabilities on a variety of tasks. Notably, the efficacy of LLMs remains significantly dependent on the quality and structure of the input…

机器学习 · 计算机科学 2025-04-08 Wenliang Zheng , Sarkar Snigdha Sarathi Das , Yusen Zhang , Rui Zhang

The widespread adoption of large language models (LLMs) such as ChatGPT, Gemini, and DeepSeek has significantly changed how people approach tasks in education, professional work, and creative domains. This paper investigates how the…

人机交互 · 计算机科学 2025-08-29 Rizal Khoirul Anam

Interaction with Large Language Models (LLMs) is primarily carried out via prompting. A prompt is a natural language instruction designed to elicit certain behaviour or output from a model. In theory, natural language prompts enable…

人机交互 · 计算机科学 2024-03-15 Michael Desmond , Michelle Brachman

Large Language Models (LLMs) have gained widespread popularity due to their ability to perform ad-hoc Natural Language Processing (NLP) tasks with a simple natural language prompt. Part of the appeal for LLMs is their approachability to the…

人机交互 · 计算机科学 2025-02-25 Aditi Mishra , Utkarsh Soni , Anjana Arunkumar , Jinbin Huang , Bum Chul Kwon , Chris Bryan

The performance of pre-trained Large Language Models (LLMs) is often sensitive to nuances in prompt templates, requiring careful prompt engineering, adding costs in terms of computing and human effort. In this study, we present experiments…

计算与语言 · 计算机科学 2025-05-27 Liang Cheng , Tianyi LI , Zhaowei Wang , Mark Steedman

The use of large language models (LLMs) in natural language processing (NLP) tasks is rapidly increasing, leading to changes in how researchers approach problems in the field. To fully utilize these models' abilities, a better understanding…

计算与语言 · 计算机科学 2023-11-07 Bishal Santra , Sakya Basak , Abhinandan De , Manish Gupta , Pawan Goyal

Large language models have demonstrated outstanding performance on a wide range of tasks such as question answering and code generation. On a high level, given an input, a language model can be used to automatically complete the sequence in…

计算与语言 · 计算机科学 2023-05-31 Luca Beurer-Kellner , Marc Fischer , Martin Vechev

The use of Large Language Models (LLMs) has drawn growing interest within the scientific community. LLMs can handle large volumes of textual data and support methods for evidence synthesis. Although recent studies highlight the potential of…

Large Language Models (LLMs) are transforming programming practices, offering significant capabilities for code generation activities. While researchers have explored the potential of LLMs in various domains, this paper focuses on their use…

软件工程 · 计算机科学 2026-05-04 Deborah Etsenake , Meiyappan Nagappan

The evolution of Large Language Models (LLMs) has showcased remarkable capacities for logical reasoning and natural language comprehension. These capabilities can be leveraged in solutions that semantically and textually model complex…

人机交互 · 计算机科学 2024-04-17 Syed Mekael Wasti , Ken Q. Pu , Ali Neshati

The adoption of Large Language Models (LLMs) is reshaping software development as developers integrate these LLMs into their applications. In such applications, prompts serve as the primary means of interacting with LLMs. Despite the…

软件工程 · 计算机科学 2025-01-14 Mahan Tafreshipour , Aaron Imani , Eric Huang , Eduardo Almeida , Thomas Zimmermann , Iftekhar Ahmed
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