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1. Robot Behavior-Tree-Based Task Generation with Large Language Models

   Cao, Yue; Lee, C.S. George (March 2023, CEUR workshop proceedings)

   Nowadays, the behavior tree is gaining popularity as a representation for robot tasks due to its modularity and reusability. Designing behavior-tree tasks manually is time-consuming for robot end-users, thus there is a need for investigating automatic behavior-tree-based task generation. Prior behavior-tree- based task generation approaches focus on fixed primitive tasks and lack generalizability to new task domains. To cope with this issue, we propose a novel behavior-tree-based task generation approach that utilizes state-of-the-art large language models. We propose a Phase-Step prompt design that enables a hierarchical-structured robot task generation and further integrate it with behavior-tree-embedding- based search to set up the appropriate prompt. In this way, we enable an automatic and cross-domain behavior-tree task generation. Our behavior-tree-based task generation approach does not require a set of pre-defined primitive tasks. End-users only need to describe an abstract desired task and our proposed approach can swiftly generate the corresponding behavior tree. A full-process case study is provided to demonstrate our proposed approach. An ablation study is conducted to evaluate the effectiveness of our Phase-Step prompts. Assessment on Phase-Step prompts and the limitation of large language models are presented and discussed. 

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2. Robot Behavior-Tree Task Generation with Large Language Models

   Cao, Yue; Lee, C.S. George (March 2023, CEUR workshop proceedings)

   Nowadays, the behavior tree is gaining popularity as a representation for robot tasks due to its modularity and reusability. Designing behavior-tree tasks manually is a time-consuming work for robot end- users, thus suggests a need for automatic behavior-tree task generation. Prior behavior-tree generation approaches focus on fixed primitive tasks and lack generalizability to new task domains. To cope with this issue, we propose a novel behavior-tree task generation approach with state-of-the-art large language models. We present a Phase-Step prompt design that enables hierarchical-structured robot task generation. We further integrate with behavior-tree-embedding-based search to set up the appropriate prompt. In such way, we enable automatic and cross-domain behavior-tree task generation. Our task generation approach does not require a set of pre-defined primitive tasks. End-user only needs to describe an abstract desired task and our approach can swiftly generate the corresponding behavior tree. Case studies are provided to demonstrate our approach. 

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3. In-Context Learning Unlocked for Diffusion Models

   Wang, Zhendong; Jiang, Yifan; Lu, Yadong; Shen, Yelong; He, Pengcheng; Chen, Weizhu; Wang, Zhangyang; Zhou, Mingyuan (December 2023, Neural Information Processing Systems)

   We present Prompt Diffusion, a framework for enabling in-context learning in diffusion-based generative models. Given a pair of task-specific example images, such as depth from/to image and scribble from/to image, and a text guidance, our model automatically understands the underlying task and performs the same task on a new query image following the text guidance. To achieve this, we propose a vision-language prompt that can model a wide range of vision-language tasks and a diffusion model that takes it as input. The diffusion model is trained jointly on six different tasks using these prompts. The resulting Prompt Diffusion model becomes the first diffusion-based vision-language foundation model capable of in-context learning. It demonstrates high-quality in-context generation for the trained tasks and effectively generalizes to new, unseen vision tasks using their respective prompts. Our model also shows compelling text-guided image editing results. Our framework aims to facilitate research into in-context learning for computer vision. We share our code and pre-trained models at https://github. com/Zhendong-Wang/Prompt-Diffusion. 

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4. Task Supportive and Personalized Human-Large Language Model Interaction: A User Study

   https://doi.org/10.1145/3627508.3638344  

   Wang, Ben; Liu, Jiqun; Karimnazarov, Jamshed; Thompson, Nicolas (March 2024, ACM)

   Large language model (LLM) applications, such as ChatGPT, are a powerful tool for online information-seeking (IS) and problem-solving tasks. However, users still face challenges initializing and refining prompts, and their cognitive barriers and biased perceptions further impede task completion. These issues reflect broader challenges identified within the fields of IS and interactive information retrieval (IIR). To address these, our approach integrates task context and user perceptions into human-ChatGPT interactions through prompt engineering. We developed a ChatGPT-like platform integrated with supportive functions, including perception articulation, prompt suggestion, and conversation explanation. Our findings of a user study demonstrate that the supportive functions help users manage expectations, reduce cognitive loads, better refine prompts, and increase user engagement. This research enhances our comprehension of designing proactive and user-centric systems with LLMs. It offers insights into evaluating human-LLM interactions and emphasizes potential challenges for under served users. 

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5. All You Need is One: Capsule Prompt Tuning with a Single Vector

   Yiyang, Liu; James_Chenhao, Liang; Heng, Fan; Wenhao, Yang; Yiming, Cui; Xiaotian, Han; Lifu, Huang; Dongfang, Liu; Qifan, Wang; Cheng, Han (October 2025, The Thirty-Ninth Annual Conference on Neural Information Processing Systems (NeurIPS, 2025))

   Prompt-based learning has emerged as a parameter-efficient finetuning (PEFT) approach to facilitate Large Language Model (LLM) adaptation to downstream tasks by conditioning generation with task-aware guidance. Despite its successes, current prompt-based learning methods heavily rely on laborious grid searching for optimal prompt length and typically require considerable number of prompts, introducing additional computational burden. Worse yet, our pioneer findings indicate that the task-aware prompt design is inherently limited by its absence of instance-aware information, leading to a subtle attention interplay with the input sequence. In contrast, simply incorporating instance-aware information as a part of the guidance can enhance the prompt-tuned model performance without additional fine-tuning. Moreover, we find an interesting phenomenon, namely “attention anchor,” that incorporates instance-aware tokens at the earliest position of the sequence, can successfully preserve strong attention to critical structural information, and exhibits more active attention interaction with all input tokens. In light of our observation, we introduce Capsule Prompt-Tuning (CaPT), an efficient and effective solution that leverages off-the-shelf, informative instance semantics into prompt-based learning. Our approach innovatively integrates both instance-aware and task-aware information in a nearly parameter-free manner (i.e., one single capsule prompt). Empirical results demonstrate that our method can exhibit superior performance across various language tasks (e.g., 84.03% average accuracy on T5-Large), serving as an “attention anchor,” while enjoying high parameter efficiency (e.g., 0.003% of model parameters on Llama3.2-1B). 

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