English
Related papers

Related papers: Promptor: A Conversational and Autonomous Prompt G…

200 papers

Large language models (LLMs) have shown remarkable success across a wide range of natural language generation tasks, where proper prompt designs make great impacts. While existing prompting methods are normally restricted to providing…

Computation and Language · Computer Science 2023-06-01 Bei Li , Rui Wang , Junliang Guo , Kaitao Song , Xu Tan , Hany Hassan , Arul Menezes , Tong Xiao , Jiang Bian , JingBo Zhu

Language Models (LMs) have proven to be useful in various downstream applications, such as summarisation, translation, question answering and text classification. LMs are becoming increasingly important tools in Artificial Intelligence,…

Large language models (LLMs) have displayed an impressive ability to harness natural language to perform complex tasks. In this work, we explore whether we can leverage this learned ability to find and explain patterns in data.…

Machine Learning · Computer Science 2023-01-30 Chandan Singh , John X. Morris , Jyoti Aneja , Alexander M. Rush , Jianfeng Gao

In today's digitally driven world, dialogue systems play a pivotal role in enhancing user interactions, from customer service to virtual assistants. In these dialogues, it is important to identify user's goals automatically to resolve their…

Computation and Language · Computer Science 2024-11-19 Juan A. Rodriguez , Nicholas Botzer , David Vazquez , Christopher Pal , Marco Pedersoli , Issam Laradji

Large language models (LLMs) have shown remarkable performance on many different Natural Language Processing (NLP) tasks. Prompt engineering plays a key role in adding more to the already existing abilities of LLMs to achieve significant…

Computation and Language · Computer Science 2024-07-25 Shubham Vatsal , Harsh Dubey

This paper surveys and organizes research works in a new paradigm in natural language processing, which we dub "prompt-based learning". Unlike traditional supervised learning, which trains a model to take in an input x and predict an output…

Computation and Language · Computer Science 2021-07-30 Pengfei Liu , Weizhe Yuan , Jinlan Fu , Zhengbao Jiang , Hiroaki Hayashi , Graham Neubig

Text-to-image generation model is able to generate images across a diverse range of subjects and styles based on a single prompt. Recent works have proposed a variety of interaction methods that help users understand the capabilities of…

Human-Computer Interaction · Computer Science 2023-07-19 Seungho Baek , Hyerin Im , Jiseung Ryu , Juhyeong Park , Takyeon Lee

AI tasks encompass a wide range of domains and fields. While numerous AI models have been designed for specific tasks and applications, they often require considerable human efforts in finding the right model architecture, optimization…

Computation and Language · Computer Science 2023-05-05 Shujian Zhang , Chengyue Gong , Lemeng Wu , Xingchao Liu , Mingyuan Zhou

With their remarkable ability to generate code, large language models (LLMs) are a transformative technology for computing education practice. They have created an urgent need for educators to rethink pedagogical approaches and teaching…

Human-Computer Interaction · Computer Science 2023-08-01 Paul Denny , Juho Leinonen , James Prather , Andrew Luxton-Reilly , Thezyrie Amarouche , Brett A. Becker , Brent N. Reeves

Recently, pretrained language models (PLMs) have had exceptional success in language generation. To leverage the rich knowledge encoded by PLMs, a simple yet powerful paradigm is to use prompts in the form of either discrete tokens or…

Computation and Language · Computer Science 2022-10-04 Tianyi Tang , Junyi Li , Wayne Xin Zhao , Ji-Rong Wen

Recent developments in large language models (LLM) and generative AI have unleashed the astonishing capabilities of text-to-image generation systems to synthesize high-quality images that are faithful to a given reference text, known as a…

Human-Computer Interaction · Computer Science 2023-03-17 Yutong Xie , Zhaoying Pan , Jinge Ma , Luo Jie , Qiaozhu Mei

Text-to-image generation models are powerful but difficult to use. Users craft specific prompts to get better images, though the images can be repetitive. This paper proposes a Prompt Expansion framework that helps users generate…

Computer Vision and Pattern Recognition · Computer Science 2023-12-29 Siddhartha Datta , Alexander Ku , Deepak Ramachandran , Peter Anderson

Recent virtual try-on approaches have advanced by finetuning pre-trained text-to-image diffusion models to leverage their powerful generative ability. However, the use of text prompts in virtual try-on remains underexplored. This paper…

Computer Vision and Pattern Recognition · Computer Science 2025-08-08 Jeongho Kim , Hoiyeong Jin , Sunghyun Park , Jaegul Choo

A key component of modern conversational systems is the Dialogue State Tracker (or DST), which models a user's goals and needs. Toward building more robust and reliable DSTs, we introduce a prompt-based learning approach to automatically…

Computation and Language · Computer Science 2023-06-08 Xiangjue Dong , Yun He , Ziwei Zhu , James Caverlee

High-quality prompts are crucial for Large Language Models (LLMs) to achieve exceptional performance. However, manually crafting effective prompts is labor-intensive and demands significant domain expertise, limiting its scalability.…

Computation and Language · Computer Science 2025-08-26 Zheng Dong , Luming Shang , Gabriela Olinto

The quality of the prompts provided to text-to-image diffusion models determines how faithful the generated content is to the user's intent, often requiring `prompt engineering'. To harness visual concepts from target images without prompt…

Computer Vision and Pattern Recognition · Computer Science 2023-12-20 Shweta Mahajan , Tanzila Rahman , Kwang Moo Yi , Leonid Sigal

This case study investigates the task of job classification in a real-world setting, where the goal is to determine whether an English-language job posting is appropriate for a graduate or entry-level position. We explore multiple…

Computation and Language · Computer Science 2023-04-19 Benjamin Clavié , Alexandru Ciceu , Frederick Naylor , Guillaume Soulié , Thomas Brightwell

Large language models (LLMs) have gained considerable attention for Artificial Intelligence Generated Content (AIGC), particularly with the emergence of ChatGPT. However, the direct adaptation of continuous speech to LLMs that process…

Audio and Speech Processing · Electrical Eng. & Systems 2023-08-28 Haibin Wu , Kai-Wei Chang , Yuan-Kuei Wu , Hung-yi Lee

Popular prompt strategies like Chain-of-Thought Prompting can dramatically improve the reasoning abilities of Large Language Models (LLMs) in various domains. However, such hand-crafted prompt-strategies are often sub-optimal. In this…

Computation and Language · Computer Science 2023-10-02 Chrisantha Fernando , Dylan Banarse , Henryk Michalewski , Simon Osindero , Tim Rocktäschel

The answering quality of an aligned large language model (LLM) can be drastically improved if treated with proper crafting of prompts. In this paper, we propose ExpertPrompting to elicit the potential of LLMs to answer as distinguished…

Computation and Language · Computer Science 2025-03-06 Benfeng Xu , An Yang , Junyang Lin , Quan Wang , Chang Zhou , Yongdong Zhang , Zhendong Mao