Search Results for author:
Found 22 papers, 8 papers with code
OpenPrompt: An Open-source Framework for Prompt-learning
Prompt-learning has become a new paradigm in modern natural language processing, which directly adapts pre-trained language models (PLMs) to $cloze$-style prediction, autoregressive modeling, or sequence to sequence generation, resulting in promising performances on various tasks.
Enhancing Chat Language Models by Scaling High-quality Instructional Conversations
Fine-tuning on instruction data has been widely validated as an effective practice for implementing chat language models like ChatGPT.
Delta Tuning: A Comprehensive Study of Parameter Efficient Methods for Pre-trained Language Models
This necessitates a new branch of research focusing on the parameter-efficient adaptation of PLMs, dubbed as delta tuning in this paper.
Few-NERD: A Few-Shot Named Entity Recognition Dataset
In this paper, we present Few-NERD, a large-scale human-annotated few-shot NER dataset with a hierarchy of 8 coarse-grained and 66 fine-grained entity types.
Ranked #5 on
Named Entity Recognition (NER)
on Few-NERD (SUP)
MAVEN-ERE: A Unified Large-scale Dataset for Event Coreference, Temporal, Causal, and Subevent Relation Extraction
It contains 103, 193 event coreference chains, 1, 216, 217 temporal relations, 57, 992 causal relations, and 15, 841 subevent relations, which is larger than existing datasets of all the ERE tasks by at least an order of magnitude.
Sparse Low-rank Adaptation of Pre-trained Language Models
Recognizing the need for more flexible adaptation, we extend the methodology of LoRA to an innovative approach we call sparse low-rank adaptation (SoRA) that enables dynamic adjustments to the intrinsic rank during the adaptation process.
A Read-and-Select Framework for Zero-shot Entity Linking
Zero-shot entity linking (EL) aims at aligning entity mentions to unseen entities to challenge the generalization ability.
Revisiting Sparse Retrieval for Few-shot Entity Linking
Entity linking aims to link ambiguous mentions to their corresponding entities in a knowledge base.
High-throughput relation extraction algorithm development associating knowledge articles and electronic health records
In this paper, we present Hi-RES, a framework for high-throughput relation extraction algorithm development.
A Charge-Density-Wave Topological Semimetal
We here demonstrate that the room-temperature phase of (TaSe$_4$)$_2$I is a Weyl semimetal with 24 pairs of Weyl nodes.
Band Gap
Materials Science
Strongly Correlated Electrons
Pressure-induced Superconductivity in dual-topological semimetal Pt2HgSe3
Recently monolayer jacutingaite (Pt2HgSe3), a naturally occurring exfoliable mineral, discovered in Brazil in 2008, has been theoretically predicted as a candidate quantum spin Hall system with a 0. 5 eV band gap, while the bulk form is one of only a few known dual-topological insulators which may host different surface states protected by symmetries.
Band Gap
Superconductivity
Materials Science
Prompt-Learning for Fine-Grained Entity Typing
In this work, we investigate the application of prompt-learning on fine-grained entity typing in fully supervised, few-shot and zero-shot scenarios.
Few-shot Learning with Big Prototypes
A big prototype could be effectively modeled by two sets of learnable parameters, one is the center of the hypersphere, which is an embedding with the same dimension of training examples.
BTPK-based interpretable method for NER tasks based on Talmudic Public Announcement Logic
In this paper, we propose a novel interpretable method, BTPK (Binary Talmudic Public Announcement Logic model), to help users understand the internal recognition logic of the name entity recognition tasks based on Talmudic Public Announcement Logic.
Few-shot Classification with Hypersphere Modeling of Prototypes
Moreover, it is more convenient to perform metric-based classification with hypersphere prototypes than statistical modeling, as we only need to calculate the distance from a data point to the surface of the hypersphere.
SCTracker: Multi-object tracking with shape and confidence constraints
Detection-based tracking is one of the main methods of multi-object tracking.
Exploring Lottery Prompts for Pre-trained Language Models
Consistently scaling pre-trained language models (PLMs) imposes substantial burdens on model adaptation, necessitating more efficient alternatives to conventional fine-tuning.
Enhancing Nucleus Segmentation with HARU-Net: A Hybrid Attention Based Residual U-Blocks Network
Additionally, we introduce a post-processing method that combines the target information and target contours to distinguish overlapping nuclei and generate an instance segmentation image.
A Voting-Stacking Ensemble of Inception Networks for Cervical Cytology Classification
To address this issue, we propose a Voting-Stacking ensemble strategy, which employs three Inception networks as base learners and integrates their outputs through a voting ensemble.
Privacy in Large Language Models: Attacks, Defenses and Future Directions
The advancement of large language models (LLMs) has significantly enhanced the ability to effectively tackle various downstream NLP tasks and unify these tasks into generative pipelines.
Mastering Text, Code and Math Simultaneously via Fusing Highly Specialized Language Models
Underlying data distributions of natural language, programming code, and mathematical symbols vary vastly, presenting a complex challenge for large language models (LLMs) that strive to achieve high performance across all three domains simultaneously.
Medical Visual Prompting (MVP): A Unified Framework for Versatile and High-Quality Medical Image Segmentation
This novel framework offers improved performance with fewer parameters and holds significant potential for accurate segmentation of lesion regions in various medical tasks, making it clinically valuable.
