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Large language models (LLMs) provide a new way to build chatbots by accepting natural language prompts. Yet, it is unclear how to design prompts to power chatbots to carry on naturalistic conversations while pursuing a given goal, such as…

人机交互 · 计算机科学 2024-05-08 Jing Wei , Sungdong Kim , Hyunhoon Jung , Young-Ho Kim

The rapid advancements in large language models (LLMs) have greatly expanded the potential for automated code-related tasks. Two primary methodologies are used in this domain: prompt engineering and fine-tuning. Prompt engineering involves…

软件工程 · 计算机科学 2025-02-21 Jiho Shin , Clark Tang , Tahmineh Mohati , Maleknaz Nayebi , Song Wang , Hadi Hemmati

Patients with schizophrenia often present with cognitive impairments that may hinder their ability to learn about their condition. These individuals could benefit greatly from education platforms that leverage the adaptability of Large…

计算与语言 · 计算机科学 2024-10-18 Per Niklas Waaler , Musarrat Hussain , Igor Molchanov , Lars Ailo Bongo , Brita Elvevåg

The recent advancements in Large Language Models (LLMs), particularly conversational LLMs like ChatGPT, have prompted changes in a range of fields, including design. This study aims to examine the capabilities of ChatGPT in a human-centered…

人机交互 · 计算机科学 2023-02-16 A. Baki Kocaballi

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

Prompt engineering is an increasingly important skill set needed to converse effectively with large language models (LLMs), such as ChatGPT. Prompts are instructions given to an LLM to enforce rules, automate processes, and ensure specific…

Large language models (LLMs) have demonstrated remarkable potential in natural language understanding and generation, making them valuable tools for enhancing conversational interactions. However, LLMs encounter challenges such as lacking…

人机交互 · 计算机科学 2023-11-10 Guinan Su , Yanwu Yang , Jie Guo

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…

Conversational systems based on Large Language Models (LLMs), such as ChatGPT, show exceptional proficiency in context understanding and response generation. However, despite their impressive capabilities, they still possess limitations,…

计算与语言 · 计算机科学 2023-10-17 Yang Deng , Lizi Liao , Liang Chen , Hongru Wang , Wenqiang Lei , Tat-Seng Chua

Human-LLM conversations are increasingly becoming more pervasive in peoples' professional and personal lives, yet many users still struggle to elicit helpful responses from LLM Chatbots. One of the reasons for this issue is users' lack of…

Large Language Models, such as Generative Pre-trained Transformer 3 (aka. GPT-3), have been developed to understand language through the analysis of extensive text data, allowing them to identify patterns and connections between words.…

计算与语言 · 计算机科学 2023-10-03 Baphumelele Masikisiki , Vukosi Marivate , Yvette Hlope

The conversational nature of chatbots poses challenges to designers since their development is different from other software and requires investigating new practices in the context of human-AI interaction and their impact on user…

人机交互 · 计算机科学 2023-01-18 Geovana Ramos Sousa Silva , Edna Dias Canedo

In this paper, we propose MPC (Modular Prompted Chatbot), a new approach for creating high-quality conversational agents without the need for fine-tuning. Our method utilizes pre-trained large language models (LLMs) as individual modules…

计算与语言 · 计算机科学 2023-08-17 Gibbeum Lee , Volker Hartmann , Jongho Park , Dimitris Papailiopoulos , Kangwook Lee

While Large Language Models (LLMs) are being quickly adapted to many domains, including healthcare, their strengths and pitfalls remain under-explored. In our study, we examine the effects of prompt engineering to guide Large Language…

计算与语言 · 计算机科学 2024-09-04 Daniil Filienko , Yinzhou Wang , Caroline El Jazmi , Serena Xie , Trevor Cohen , Martine De Cock , Weichao Yuwen

As conversational models become increasingly available to the general public, users are engaging with this technology in social interactions. Such unprecedented interaction experiences may pose considerable social and psychological risks to…

计算与语言 · 计算机科学 2023-08-28 Ekaterina Svikhnushina , Pearl Pu

This paper presents prompt design techniques for software engineering, in the form of patterns, to solve common problems when using large language models (LLMs), such as ChatGPT to automate common software engineering activities, such as…

软件工程 · 计算机科学 2023-03-15 Jules White , Sam Hays , Quchen Fu , Jesse Spencer-Smith , Douglas C. Schmidt

The use of large language model (LLM)-powered chatbots, such as ChatGPT, has become popular across various domains, supporting a range of tasks and processes. However, due to the intrinsic complexity of LLMs, effective prompting is more…

Large Language Models (LLMs), which simulate human users, are frequently employed to evaluate chatbots in applications such as tutoring and customer service. Effective evaluation necessitates a high degree of human-like diversity within…

计算与语言 · 计算机科学 2024-09-04 Xiaoyu Lin , Xinkai Yu , Ankit Aich , Salvatore Giorgi , Lyle Ungar

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) can be used as accessible and intelligent chatbots by constructing natural language queries and directly inputting the prompt into the large language model. However, different prompt' constructions often lead to…

计算与语言 · 计算机科学 2023-12-14 Jinta Weng , Jiarui Zhang , Yue Hu , Daidong Fa , Xiaofeng Xuand , Heyan Huang
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