Search Results for author:
Found 19 papers, 10 papers with code
FlowAgent: Achieving Compliance and Flexibility for Workflow Agents
Experiments on three datasets demonstrate that FlowAgent not only adheres to workflows but also effectively manages OOW queries, highlighting its dual strengths in compliance and flexibility.
LUCY: Linguistic Understanding and Control Yielding Early Stage of Her
The film Her features Samantha, a sophisticated AI audio agent who is capable of understanding both linguistic and paralinguistic information in human speech and delivering real-time responses that are natural, informative and sensitive to emotional subtleties.
A MapReduce Approach to Effectively Utilize Long Context Information in Retrieval Augmented Language Models
While holding great promise for improving and facilitating healthcare, large language models (LLMs) struggle to produce up-to-date responses on evolving topics due to outdated knowledge or hallucination.
Deciphering genomic codes using advanced NLP techniques: a scoping review
The goal of this review is to assess data and model accessibility in the most recent literature, gaining a better understanding of the existing capabilities and constraints of these tools in processing genomic sequencing data.
Environment Scan of Generative AI Infrastructure for Clinical and Translational Science
This study reports a comprehensive environmental scan of the generative AI (GenAI) infrastructure in the national network for clinical and translational science across 36 institutions supported by the Clinical and Translational Science Award (CTSA) Program led by the National Center for Advancing Translational Sciences (NCATS) of the National Institutes of Health (NIH) at the United States.
Leveraging Open Knowledge for Advancing Task Expertise in Large Language Models
For the latter, we highlight the diversity of constituting experts and that of the fine-tuning instructions throughout the model and data selection process.
Unleashing the Power of Data Tsunami: A Comprehensive Survey on Data Assessment and Selection for Instruction Tuning of Language Models
To bridge this gap, we present a comprehensive review on existing literature of data assessment and selection especially for instruction tuning of LLMs.
T2V-CompBench: A Comprehensive Benchmark for Compositional Text-to-video Generation
In this work, we conduct the first systematic study on compositional text-to-video generation.
Sinkhorn Distance Minimization for Knowledge Distillation
We propose the Sinkhorn Knowledge Distillation (SinKD) that exploits the Sinkhorn distance to ensure a nuanced and precise assessment of the disparity between teacher and student distributions.
Towards Robust Text Retrieval with Progressive Learning
However, existing embedding models for text retrieval usually have three non-negligible limitations.
Devil in the Number: Towards Robust Multi-modality Data Filter
In order to appropriately filter multi-modality data sets on a web-scale, it becomes crucial to employ suitable filtering methods to boost performance and reduce training costs.
SoftCLIP: Softer Cross-modal Alignment Makes CLIP Stronger
During the preceding biennium, vision-language pre-training has achieved noteworthy success on several downstream tasks.
PyramidCLIP: Hierarchical Feature Alignment for Vision-language Model Pretraining
Large-scale vision-language pre-training has achieved promising results on downstream tasks.
ShadowGNN: Graph Projection Neural Network for Text-to-SQL Parser
Given a database schema, Text-to-SQL aims to translate a natural language question into the corresponding SQL query.
ReCU: Reviving the Dead Weights in Binary Neural Networks
We prove that reviving the "dead weights" by ReCU can result in a smaller quantization error.
SiMaN: Sign-to-Magnitude Network Binarization
In this paper, we show that our weight binarization provides an analytical solution by encoding high-magnitude weights into +1s, and 0s otherwise.
Answer-driven Deep Question Generation based on Reinforcement Learning
First, we propose an answer-aware initialization module with a gated connection layer which introduces both document and answer information to the decoder, thus helping to guide the choice of answer-focused question words.
Rotated Binary Neural Network
In this paper, for the first time, we explore the influence of angular bias on the quantization error and then introduce a Rotated Binary Neural Network (RBNN), which considers the angle alignment between the full-precision weight vector and its binarized version.
CREDIT: Coarse-to-Fine Sequence Generation for Dialogue State Tracking
In dialogue systems, a dialogue state tracker aims to accurately find a compact representation of the current dialogue status, based on the entire dialogue history.
