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Found 38 papers, 13 papers with code
A Corpus for Commonsense Inference in Story Cloze Test
The SOTA models can achieve over 90% accuracy on predicting the last sentence.
Fantastic Questions and Where to Find Them: FairytaleQA – An Authentic Dataset for Narrative Comprehension
Through benchmarking with QG models, we show that the QG model trained on FairytaleQA is capable of asking high-quality and more diverse questions.
It is AI’s Turn to Ask Humans a Question: Question-Answer Pair Generation for Children’s Story Books
Existing question answering (QA) techniques are created mainly to answer questions asked by humans.
Designing AI Tools for Clinical Care Teams to Support Serious Illness Conversations with Older Adults in the Emergency Department
Serious illness conversations (SICs), discussions between clinical care teams and patients with serious, life-limiting illnesses about their values, goals, and care preferences, are critical for patient-centered care.
UXAgent: A System for Simulating Usability Testing of Web Design with LLM Agents
Usability testing is a fundamental research method that user experience (UX) researchers use to evaluate and iterate a web design, but\textbf{ how to evaluate and iterate the usability testing study design } itself?
AgentA/B: Automated and Scalable Web A/BTesting with Interactive LLM Agents
A/B testing experiment is a widely adopted method for evaluating UI/UX design decisions in modern web applications.
Towards a Design Guideline for RPA Evaluation: A Survey of Large Language Model-Based Role-Playing Agents
Role-Playing Agent (RPA) is an increasingly popular type of LLM Agent that simulates human-like behaviors in a variety of tasks.
UXAgent: An LLM Agent-Based Usability Testing Framework for Web Design
Usability testing is a fundamental yet challenging (e. g., inflexible to iterate the study design flaws and hard to recruit study participants) research method for user experience (UX) researchers to evaluate a web design.
RECOVER: Designing a Large Language Model-based Remote Patient Monitoring System for Postoperative Gastrointestinal Cancer Care
Cancer surgery is a key treatment for gastrointestinal (GI) cancers, a group of cancers that account for more than 35% of cancer-related deaths worldwide, but postoperative complications are unpredictable and can be life-threatening.
WatchGuardian: Enabling User-Defined Personalized Just-in-Time Intervention on Smartwatch
Our findings underscore the effectiveness of a customizable, AI-driven JITI system for individuals in need of behavioral intervention in personal undesirable actions.
"It Felt Like I Was Left in the Dark": Exploring Information Needs and Design Opportunities for Family Caregivers of Older Adult Patients in Critical Care Settings
Our project aims to explore the information needs of caregivers of ICU older adult patients, from which we can propose design opportunities to guide future AI systems.
Zero-shot Large Language Models for Long Clinical Text Summarization with Temporal Reasoning
Recent advancements in large language models (LLMs) have shown potential for transforming data processing in healthcare, particularly in understanding complex clinical narratives.
SepsisCalc: Integrating Clinical Calculators into Early Sepsis Prediction via Dynamic Temporal Graph Construction
Sepsis is an organ dysfunction caused by a deregulated immune response to an infection.
Vital Insight: Assisting Experts' Context-Driven Sensemaking of Multi-modal Personal Tracking Data Using Visualization and Human-In-The-Loop LLM Agents
Passive tracking methods, such as phone and wearable sensing, have become dominant in monitoring human behaviors in modern ubiquitous computing studies.
Secret Use of Large Language Model (LLM)
The advancements of Large Language Models (LLMs) have decentralized the responsibility for the transparency of AI usage.
SepsisLab: Early Sepsis Prediction with Uncertainty Quantification and Active Sensing
Clinicians and potential sepsis patients can benefit from the system in early prediction and diagnosis of sepsis.
"I Like Sunnie More Than I Expected!": Exploring User Expectation and Perception of an Anthropomorphic LLM-based Conversational Agent for Well-Being Support
The human-computer interaction (HCI) research community has a longstanding interest in exploring the mismatch between users' actual experiences and expectation toward new technologies, for instance, large language models (LLMs).
Human-Centered Privacy Research in the Age of Large Language Models
The emergence of large language models (LLMs), and their increased use in user-facing systems, has led to substantial privacy concerns.
Bergeron: Combating Adversarial Attacks through a Conscience-Based Alignment Framework
To help mitigate this issue, we introduce Bergeron: a framework designed to improve the robustness of LLMs against attacks without any additional parameter fine-tuning.
Human Still Wins over LLM: An Empirical Study of Active Learning on Domain-Specific Annotation Tasks
Large Language Models (LLMs) have demonstrated considerable advances, and several claims have been made about their exceeding human performance.
More Samples or More Prompts? Exploring Effective In-Context Sampling for LLM Few-Shot Prompt Engineering
While most existing works on LLM prompting techniques focus only on how to select a better set of data samples inside one single prompt input (In-Context Learning or ICL), why can not we design and leverage multiple prompts together to further improve the LLM's performance?
StorySparkQA: Expert-Annotated QA Pairs with Real-World Knowledge for Children's Story-Based Learning
Interactive story reading is a common parent-child activity, where parents expect to teach both language skills and real-world knowledge beyond the story.
'Don't Get Too Technical with Me': A Discourse Structure-Based Framework for Science Journalism
Science journalism refers to the task of reporting technical findings of a scientific paper as a less technical news article to the general public audience.
"It's a Fair Game", or Is It? Examining How Users Navigate Disclosure Risks and Benefits When Using LLM-Based Conversational Agents
To bridge this gap, we analyzed sensitive disclosures in real-world ChatGPT conversations and conducted semi-structured interviews with 19 LLM-based CA users.
Rethinking Human-AI Collaboration in Complex Medical Decision Making: A Case Study in Sepsis Diagnosis
Today's AI systems for medical decision support often succeed on benchmark datasets in research papers but fail in real-world deployment.
Talk2Care: Facilitating Asynchronous Patient-Provider Communication with Large-Language-Model
(2) For health providers, we built an LLM-based dashboard to summarize and present important health information based on older adults' conversations with the VA. We further conducted two user studies with older adults and providers to evaluate the usability of the system.
Mental-LLM: Leveraging Large Language Models for Mental Health Prediction via Online Text Data
More importantly, our experiments show that instruction finetuning can significantly boost the performance of LLMs for all tasks simultaneously.
Beyond Labels: Empowering Human Annotators with Natural Language Explanations through a Novel Active-Learning Architecture
Our AL architecture leverages an explanation-generation model to produce explanations guided by human explanations, a prediction model that utilizes generated explanations toward prediction faithfully, and a novel data diversity-based AL sampling strategy that benefits from the explanation annotations.
Are Human Explanations Always Helpful? Towards Objective Evaluation of Human Natural Language Explanations
Human-annotated labels and explanations are critical for training explainable NLP models.
NECE: Narrative Event Chain Extraction Toolkit
To understand a narrative, it is essential to comprehend the temporal event flows, especially those associated with main characters; however, this can be challenging with lengthy and unstructured narrative texts.
GEMv2: Multilingual NLG Benchmarking in a Single Line of Code
This problem is especially pertinent in natural language generation which requires ever-improving suites of datasets, metrics, and human evaluation to make definitive claims.
Fantastic Questions and Where to Find Them: FairytaleQA -- An Authentic Dataset for Narrative Comprehension
Through benchmarking with QG models, we show that the QG model trained on FairytaleQA is capable of asking high-quality and more diverse questions.
Ranked #1 on
Question Generation
on FairytaleQA
Efficient Long Sequence Encoding via Synchronization
Pre-trained Transformer models have achieved successes in a wide range of NLP tasks, but are inefficient when dealing with long input sequences.
StoryBuddy: A Human-AI Collaborative Chatbot for Parent-Child Interactive Storytelling with Flexible Parental Involvement
Despite its benefits for children's skill development and parent-child bonding, many parents do not often engage in interactive storytelling by having story-related dialogues with their child due to limited availability or challenges in coming up with appropriate questions.
It is AI's Turn to Ask Humans a Question: Question-Answer Pair Generation for Children's Story Books
Existing question answering (QA) techniques are created mainly to answer questions asked by humans.
Narrative Question Answering with Cutting-Edge Open-Domain QA Techniques: A Comprehensive Study
Recent advancements in open-domain question answering (ODQA), i. e., finding answers from large open-domain corpus like Wikipedia, have led to human-level performance on many datasets.
Frustratingly Hard Evidence Retrieval for QA Over Books
A lot of progress has been made to improve question answering (QA) in recent years, but the special problem of QA over narrative book stories has not been explored in-depth.
Trust in AutoML: Exploring Information Needs for Establishing Trust in Automated Machine Learning Systems
We explore trust in a relatively new area of data science: Automated Machine Learning (AutoML).
