Search Results for author:
Found 46 papers, 18 papers with code
Multimodal RAG-driven Anomaly Detection and Classification in Laser Powder Bed Fusion using Large Language Models
This study presents a novel multimodal Retrieval-Augmented Generation-based framework that automates anomaly detection across various Additive Manufacturing processes leveraging retrieved information from literature, including images and descriptive text, rather than training datasets.
DeepSelective: Feature Gating and Representation Matching for Interpretable Clinical Prediction
The rapid accumulation of Electronic Health Records (EHRs) has transformed healthcare by providing valuable data that enhance clinical predictions and diagnoses.
My Precious Crash Data: Barriers and Opportunities in Encouraging Autonomous Driving Companies to Share Safety-Critical Data
Findings suggest two key, previously unknown barriers to data sharing: (1) Datasets inherently embed salient knowledge that is key to improving AV safety and are resource-intensive.
How Problematic Writer-AI Interactions (Rather than Problematic AI) Hinder Writers' Idea Generation
Through a case study, we demonstrate that the impact of genAI on students' idea development depends not only on the AI but also on the students and, crucially, their interactions in between.
Stepback: Enhanced Disentanglement for Voice Conversion via Multi-Task Learning
Voice conversion (VC) modifies voice characteristics while preserving linguistic content.
Assessing and Learning Alignment of Unimodal Vision and Language Models
Next, we introduce Swift Alignment of Image and Language (SAIL), a efficient transfer learning framework that aligns pretrained unimodal vision and language models for downstream vision-language tasks.
WavChat: A Survey of Spoken Dialogue Models
Recent advancements in spoken dialogue models, exemplified by systems like GPT-4o, have captured significant attention in the speech domain.
Thoughtful Adoption of NLP for Civic Participation: Understanding Differences Among Policymakers
We interviewed seven politicians (politically appointed officials as heads of government institutions) and thirteen public servants (career government employees who design and administrate policy interventions), inquiring how they choose whether and how to use NLP tools to support civic participation processes.
Diabetica: Adapting Large Language Model to Enhance Multiple Medical Tasks in Diabetes Care and Management
Generally, our introduced framework helps develop diabetes-specific LLMs and highlights their potential to enhance clinical practice and provide personalized, data-driven support for diabetes management across different end users.
WavTokenizer: an Efficient Acoustic Discrete Codec Tokenizer for Audio Language Modeling
Despite the reduced number of tokens, WavTokenizer achieves state-of-the-art reconstruction quality with outstanding UTMOS scores and inherently contains richer semantic information.
MSceneSpeech: A Multi-Scene Speech Dataset For Expressive Speech Synthesis
We introduce an open source high-quality Mandarin TTS dataset MSceneSpeech (Multiple Scene Speech Dataset), which is intended to provide resources for expressive speech synthesis.
Qwen2-Audio Technical Report
We introduce the latest progress of Qwen-Audio, a large-scale audio-language model called Qwen2-Audio, which is capable of accepting various audio signal inputs and performing audio analysis or direct textual responses with regard to speech instructions.
Decompose and Compare Consistency: Measuring VLMs' Answer Reliability via Task-Decomposition Consistency Comparison
Despite tremendous advancements, current state-of-the-art Vision-Language Models (VLMs) are still far from perfect.
CodeHalu: Investigating Code Hallucinations in LLMs via Execution-based Verification
By evaluating 17 popular LLMs using this benchmark, we reveal significant differences in their accuracy and reliability in code generation, offering detailed insights for further improving the code generation capabilities of LLMs.
A Piece of Theatre: Investigating How Teachers Design LLM Chatbots to Assist Adolescent Cyberbullying Education
In offering this tool, we explore teachers' distinctive needs when designing chatbots to assist their teaching, and how chatbot design tools might better support them.
AIR-Bench: Benchmarking Large Audio-Language Models via Generative Comprehension
By revealing the limitations of existing LALMs through evaluation results, AIR-Bench can provide insights into the direction of future research.
Exploring the Best Practices of Query Expansion with Large Language Models
Large Language Models (LLMs) are foundational in language technologies, particularly in information retrieval (IR).
Leveraging Generative AI for Clinical Evidence Summarization Needs to Ensure Trustworthiness
Evidence-based medicine promises to improve the quality of healthcare by empowering medical decisions and practices with the best available evidence.
Qwen-Audio: Advancing Universal Audio Understanding via Unified Large-Scale Audio-Language Models
Recently, instruction-following audio-language models have received broad attention for audio interaction with humans.
Ranked #1 on
Acoustic Scene Classification
on TUT Acoustic Scenes 2017
(using extra training data)
The Participatory Turn in AI Design: Theoretical Foundations and the Current State of Practice
Despite the growing consensus that stakeholders affected by AI systems should participate in their design, enormous variation and implicit disagreements exist among current approaches.
Mega-TTS 2: Boosting Prompting Mechanisms for Zero-Shot Speech Synthesis
However, the prompting mechanisms of zero-shot TTS still face challenges in the following aspects: 1) previous works of zero-shot TTS are typically trained with single-sentence prompts, which significantly restricts their performance when the data is relatively sufficient during the inference stage.
Mega-TTS: Zero-Shot Text-to-Speech at Scale with Intrinsic Inductive Bias
3) We further use a VQGAN-based acoustic model to generate the spectrogram and a latent code language model to fit the distribution of prosody, since prosody changes quickly over time in a sentence, and language models can capture both local and long-range dependencies.
Med-EASi: Finely Annotated Dataset and Models for Controllable Simplification of Medical Texts
Automatic medical text simplification can assist providers with patient-friendly communication and make medical texts more accessible, thereby improving health literacy.
Semi-Siamese Network for Robust Change Detection Across Different Domains with Applications to 3D Printing
In this work, we approach the defect detection problem using a novel Semi-Siamese deep learning model that directly compares a reference schematic of the desired print and a camera image of the achieved print.
Enhancing Multi-modal and Multi-hop Question Answering via Structured Knowledge and Unified Retrieval-Generation
Moreover, the pipelined approaches of retrieval and generation might result in poor generation performance when retrieval performance is low.
Regularized Data Programming with Automated Bayesian Prior Selection
The cost of manual data labeling can be a significant obstacle in supervised learning.
Chunk-aware Alignment and Lexical Constraint for Visual Entailment with Natural Language Explanations
CSI), a relation inferrer, and a Lexical Constraint-aware Generator (arr.
Dict-TTS: Learning to Pronounce with Prior Dictionary Knowledge for Text-to-Speech
This paper tackles the polyphone disambiguation problem from a concise and novel perspective: we propose Dict-TTS, a semantic-aware generative text-to-speech model with an online website dictionary (the existing prior information in the natural language).
CoAuthor: Designing a Human-AI Collaborative Writing Dataset for Exploring Language Model Capabilities
Large language models (LMs) offer unprecedented language generation capabilities and exciting opportunities for interaction design.
Stakeholder Participation in AI: Beyond "Add Diverse Stakeholders and Stir"
There is a growing consensus in HCI and AI research that the design of AI systems needs to engage and empower stakeholders who will be affected by AI.
Clinical Evidence Engine: Proof-of-Concept For A Clinical-Domain-Agnostic Decision Support Infrastructure
As a result, clinicians cannot easily or rapidly scrutinize the CDSS recommendation when facing a difficult diagnosis or treatment decision in practice.
Sentence-level Online Handwritten Chinese Character Recognition
Single online handwritten Chinese character recognition~(single OLHCCR) has achieved prominent performance.
FLOP: Federated Learning on Medical Datasets using Partial Networks
While different data-driven deep learning models have been developed to mitigate the diagnosis of COVID-19, the data itself is still scarce due to patient privacy concerns.
Fluctuation spectra of large random dynamical systems reveal hidden structure in ecological networks
Understanding the relationship between complexity and stability in large dynamical systems -- such as ecosystems -- remains a key open question in complexity theory which has inspired a rich body of work developed over more than fifty years.
Improving Text Generation with Student-Forcing Optimal Transport
An extension is further proposed to improve the OT learning, based on the structural and contextual information of the text sequences.
Attacking Recommender Systems with Augmented User Profiles
Recommendation Systems (RS) have become an essential part of many online services.
Faster On-Device Training Using New Federated Momentum Algorithm
Mobile crowdsensing has gained significant attention in recent years and has become a critical paradigm for emerging Internet of Things applications.
Graph-Driven Generative Models for Heterogeneous Multi-Task Learning
We propose a novel graph-driven generative model, that unifies multiple heterogeneous learning tasks into the same framework.
An End-to-End Generative Architecture for Paraphrase Generation
Generating high-quality paraphrases is a fundamental yet challenging natural language processing task.
Two Case Studies of Experience Prototyping Machine Learning Systems in the Wild
When writing with the prototype, however, authors shared that they need to "see where the sentence is going two paragraphs later" in order to decide whether the suggestion aligns with their writing; Some even considered adopting machine suggestions as plagiarism, therefore "is simply wrong".
Improving Textual Network Learning with Variational Homophilic Embeddings
This paper considers a novel variational formulation of network embeddings, with special focus on textual networks.
Ouroboros: On Accelerating Training of Transformer-Based Language Models
Model parallelism is required if a model is too large to fit in a single computing device.
Unremarkable AI: Fitting Intelligent Decision Support into Critical, Clinical Decision-Making Processes
This paper describes the design and field evaluation of a radically new form of DST.
Decoupled Parallel Backpropagation with Convergence Guarantee
The backward locking in backpropagation algorithm constrains us from updating network layers in parallel and fully leveraging the computing resources.
Neural Machine Translation with Pivot Languages
While recent neural machine translation approaches have delivered state-of-the-art performance for resource-rich language pairs, they suffer from the data scarcity problem for resource-scarce language pairs.
