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Found 18 papers, 7 papers with code
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.
Beyond Believability: Accurate Human Behavior Simulation with Fine-Tuned LLMs
In this work, we focus on evaluating and improving LLM's objective ``accuracy'' rather than the subjective ``believability'' in the web action generation task, leveraging a large-scale, real-world dataset collected from online shopping human actions.
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.
Benchmarking LLMs' Judgments with No Gold Standard
We also present GRE-bench (Generating Review Evaluation Benchmark) which evaluates LLMs based on how well they can generate high-quality peer reviews for academic research papers.
VoxelTrack: Exploring Voxel Representation for 3D Point Cloud Object Tracking
Current LiDAR point cloud-based 3D single object tracking (SOT) methods typically rely on point-based representation network.
Eliciting Informative Text Evaluations with Large Language Models
We highlight the results that on the ICLR dataset, our mechanisms can differentiate three quality levels -- human-written reviews, GPT-4-generated reviews, and GPT-3. 5-generated reviews in terms of expected scores.
Professional Network Matters: Connections Empower Person-Job Fit
Online recruitment platforms typically employ Person-Job Fit models in the core service that automatically match suitable job seekers with appropriate job positions.
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?
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.
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.
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.
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.
A Framework of Transaction Packaging in High-throughput Blockchains
We develop a model of coordination and allocation of decentralized multi-sided markets, in which our theoretical analysis is promisingly optimizing the decentralized transaction packaging process at high-throughput blockchains or Web 3. 0 platforms.
Contextual embedding and model weighting by fusing domain knowledge on Biomedical Question Answering
Biomedical Question Answering aims to obtain an answer to the given question from the biomedical domain.
Ranked #1 on
Machine Reading Comprehension
on BIOMRC
Empirical Analysis of EIP-1559: Transaction Fees, Waiting Time, and Consensus Security
However, a systematic evaluation of the real-world impact of TFMs is still absent.
Equal Affection or Random Selection: the Quality of Subjective Feedback from a Group Perspective
With this assumption, we propose a new definition for uninformative feedback and correspondingly design a family of evaluation metrics, called f-variety, for group-level feedback which can 1) distinguish informative feedback and uninformative feedback (separation) even if their statistics are both uniform and 2) decrease as the ratio of uninformative respondents increases (monotonicity).
Computer Science and Game Theory
