Search Results for author:
Found 32 papers, 8 papers with code
Learn and Review: Enhancing Continual Named Entity Recognition via Reviewing Synthetic Samples
However, the existing method depends on the relevance between tasks and is prone to inter-type confusion. In this paper, we propose a novel two-stage framework Learn-and-Review (L&R) for continual NER under the type-incremental setting to alleviate the above issues. Specifically, for the learning stage, we distill the old knowledge from teacher to a student on the current dataset.
Continual Named Entity Recognition
named-entity-recognition
+2
A Transition-based Method for Complex Question Understanding
Existing works are based on end-to-end neural models which do not explicitly model the intermediate states and lack interpretability for the parsing process.
GUI Agents: A Survey
Graphical User Interface (GUI) agents, powered by Large Foundation Models, have emerged as a transformative approach to automating human-computer interaction.
Personalized Multimodal Large Language Models: A Survey
We propose an intuitive taxonomy for categorizing the techniques used to personalize MLLMs to individual users, and discuss the techniques accordingly.
OCEAN: Offline Chain-of-thought Evaluation and Alignment in Large Language Models
To overcome the reasoning heterogeneity and grounding problems, we leverage on-policy KG exploration and RL to model a KG policy that generates token-level likelihood distributions for LLM-generated chain-of-thought reasoning paths, simulating KG reasoning preference.
A Survey of Small Language Models
We propose a novel taxonomy for categorizing the methods used to optimize SLMs, including model compression, pruning, and quantization techniques.
Knowledge-Aware Query Expansion with Large Language Models for Textual and Relational Retrieval
Large language models (LLMs) have been used to generate query expansions augmenting original queries for improving information search.
Federated Large Language Models: Current Progress and Future Directions
Large language models are rapidly gaining popularity and have been widely adopted in real-world applications.
Visual Prompting in Multimodal Large Language Models: A Survey
This paper presents the first comprehensive survey on visual prompting methods in MLLMs, focusing on visual prompting, prompt generation, compositional reasoning, and prompt learning.
GINN-KAN: Interpretability pipelining with applications in Physics Informed Neural Networks
To this end, we first evaluate several architectures that promise such interpretability, with a particular focus on two recent models selected for their potential to incorporate interpretability into standard neural network architectures while still leveraging backpropagation: the Growing Interpretable Neural Network (GINN) and Kolmogorov Arnold Networks (KAN).
Data-Driven Parametrization of Molecular Mechanics Force Fields for Expansive Chemical Space Coverage
A force field is a critical component in molecular dynamics simulations for computational drug discovery.
Shapley Value-based Contrastive Alignment for Multimodal Information Extraction
The rise of social media and the exponential growth of multimodal communication necessitates advanced techniques for Multimodal Information Extraction (MIE).
SoNIC: Safe Social Navigation with Adaptive Conformal Inference and Constrained Reinforcement Learning
Reinforcement Learning (RL) has enabled social robots to generate trajectories without human-designed rules or interventions, which makes it more effective than hard-coded systems for generalizing to complex real-world scenarios.
Advancing Retail Data Science: Comprehensive Evaluation of Synthetic Data
This paper introduces a comprehensive framework for assessing synthetic retail data, focusing on fidelity, utility, and privacy.
Simulation-Based Benchmarking of Reinforcement Learning Agents for Personalized Retail Promotions
The development of open benchmarking platforms could greatly accelerate the adoption of AI agents in retail.
Beyond Chain-of-Thought: A Survey of Chain-of-X Paradigms for LLMs
Chain-of-Thought (CoT) has been a widely adopted prompting method, eliciting impressive reasoning abilities of Large Language Models (LLMs).
Embedding-Informed Adaptive Retrieval-Augmented Generation of Large Language Models
However, it was observed by previous works that retrieval is not always helpful, especially when the LLM is already knowledgeable on the query to answer.
Hallucination Diversity-Aware Active Learning for Text Summarization
Large Language Models (LLMs) have shown propensity to generate hallucinated outputs, i. e., texts that are factually incorrect or unsupported.
Energy-based Model for Accurate Shapley Value Estimation in Interpretable Deep Learning Predictive Modeling
Most existing accelerated estimation methods have to compromise on estimation accuracy with efficiency.
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
Which LLM to Play? Convergence-Aware Online Model Selection with Time-Increasing Bandits
In this paper, we propose a time-increasing bandit algorithm TI-UCB, which effectively predicts the increase of model performances due to finetuning and efficiently balances exploration and exploitation in model selection.
RetailSynth: Synthetic Data Generation for Retail AI Systems Evaluation
Significant research effort has been devoted in recent years to developing personalized pricing, promotions, and product recommendation algorithms that can leverage rich customer data to learn and earn.
M2HGCL: Multi-Scale Meta-Path Integrated Heterogeneous Graph Contrastive Learning
Orthogonal to homogeneous graphs, the types of nodes and edges in heterogeneous graphs are diverse so that specialized graph contrastive learning methods are required.
Contrastive Bootstrapping for Label Refinement
Traditional text classification typically categorizes texts into pre-defined coarse-grained classes, from which the produced models cannot handle the real-world scenario where finer categories emerge periodically for accurate services.
The Closeness of In-Context Learning and Weight Shifting for Softmax Regression
Large language models (LLMs) are known for their exceptional performance in natural language processing, making them highly effective in many human life-related or even job-related tasks.
DocRED-FE: A Document-Level Fine-Grained Entity And Relation Extraction Dataset
Joint entity and relation extraction (JERE) is one of the most important tasks in information extraction.
Causal Analysis of Syntactic Agreement Neurons in Multilingual Language Models
However, much of this analysis has focused on monolingual models, and analyses of multilingual models have employed correlational methods that are confounded by the choice of probing tasks.
Low Resource Style Transfer via Domain Adaptive Meta Learning
Text style transfer (TST) without parallel data has achieved some practical success.
Exploring Text-transformers in AAAI 2021 Shared Task: COVID-19 Fake News Detection in English
In this paper, we describe our system for the AAAI 2021 shared task of COVID-19 Fake News Detection in English, where we achieved the 3rd position with the weighted F1 score of 0. 9859 on the test set.
Ranked #1 on
Fake News Detection
on Grover-Mega
Cluster Analysis of High-Dimensional scRNA Sequencing Data
With ongoing developments and innovations in single-cell RNA sequencing methods, advancements in sequencing performance could empower significant discoveries as well as new emerging possibilities to address biological and medical investigations.
Weighted total variation based convex clustering
Data clustering is a fundamental problem with a wide range of applications.
Efficient Clustering with Limited Distance Information
Given a point set S and an unknown metric d on S, we study the problem of efficiently partitioning S into k clusters while querying few distances between the points.
Head Pose Estimation Based on Multivariate Label Distribution
Accurate ground truth pose is essential to the training of most existing head pose estimation algorithms.
