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Found 87 papers, 35 papers with code
Methods for Numeracy-Preserving Word Embeddings
We propose a new methodology to assign and learn embeddings for numbers.
Dialogue Response Generation via Contrastive Latent Representation Learning
In this work, we aim to construct a robust sentence representation learning model, that is specifically designed for dialogue response generation, with Transformer-based encoder-decoder structure.
An Expert is Worth One Token: Synergizing Multiple Expert LLMs as Generalist via Expert Token Routing
We present Expert-Token-Routing, a unified generalist framework that facilitates seamless integration of multiple expert LLMs.
Open Continual Feature Selection via Granular-Ball Knowledge Transfer
To this end, the proposed CFS method combines the strengths of continual learning (CL) with granular-ball computing (GBC), which focuses on constructing a granular-ball knowledge base to detect unknown classes and facilitate the transfer of previously learned knowledge for further feature selection.
Empowering Large Language Model Agents through Action Learning
Large Language Model (LLM) Agents have recently garnered increasing interest yet they are limited in their ability to learn from trial and error, a key element of intelligent behavior.
LoraRetriever: Input-Aware LoRA Retrieval and Composition for Mixed Tasks in the Wild
Low-Rank Adaptation (LoRA) provides an effective yet efficient solution for fine-tuning large language models (LLM).
Similarity-based Neighbor Selection for Graph LLMs
Our research further underscores the significance of graph structure integration in LLM applications and identifies key factors for their success in node classification.
InfiAgent-DABench: Evaluating Agents on Data Analysis Tasks
In this paper, we introduce InfiAgent-DABench, the first benchmark specifically designed to evaluate LLM-based agents on data analysis tasks.
Multi-Granularity Representation Learning for Sketch-based Dynamic Face Image Retrieval
In specific scenarios, face sketch can be used to identify a person.
Multi-granularity Causal Structure Learning
Unveil, model, and comprehend the causal mechanisms underpinning natural phenomena stand as fundamental endeavors across myriad scientific disciplines.
Sim-GPT: Text Similarity via GPT Annotated Data
Due to the lack of a large collection of high-quality labeled sentence pairs with textual similarity scores, existing approaches for Semantic Textual Similarity (STS) mostly rely on unsupervised techniques or training signals that are only partially correlated with textual similarity, e. g., NLI-based datasets.
Sentiment Analysis through LLM Negotiations
A standard paradigm for sentiment analysis is to rely on a singular LLM and makes the decision in a single round under the framework of in-context learning.
A Causal Disentangled Multi-Granularity Graph Classification Method
Graph data widely exists in real life, with large amounts of data and complex structures.
Long-Tailed Classification Based on Coarse-Grained Leading Forest and Multi-Center Loss
The deviation of a classification model is caused by both class-wise and attribute-wise imbalance.
Language Models As Semantic Indexers
Semantic identifier (ID) is an important concept in information retrieval that aims to preserve the semantics of objects such as documents and items inside their IDs.
Are Human-generated Demonstrations Necessary for In-context Learning?
In this paper, we raise the fundamental question that whether human-generated demonstrations are necessary for ICL.
Instruction Tuning for Large Language Models: A Survey
This paper surveys research works in the quickly advancing field of instruction tuning (IT), a crucial technique to enhance the capabilities and controllability of large language models (LLMs).
Pushing the Limits of ChatGPT on NLP Tasks
In this work, we propose a collection of general modules to address these issues, in an attempt to push the limits of ChatGPT on NLP tasks.
GBG++: A Fast and Stable Granular Ball Generation Method for Classification
Second, considering the influence of the sample size within the GB on the GB's quality, based on the GBG++ method, an improved GB-based $k$-nearest neighbors algorithm (GB$k$NN++) is presented, which can reduce misclassification at the class boundary.
TaDSE: Template-aware Dialogue Sentence Embeddings
We further introduce a novel analytic instrument of Semantic Compression method, for which we discover a correlation with uniformity and alignment.
Text Classification via Large Language Models
This is due to (1) the lack of reasoning ability in addressing complex linguistic phenomena (e. g., intensification, contrast, irony etc); (2) limited number of tokens allowed in in-context learning.
Towards Building the Federated GPT: Federated Instruction Tuning
This repository offers a foundational framework for exploring federated fine-tuning of LLMs using heterogeneous instructions across diverse categories.
Granular-ball computing: an efficient, robust, and interpretable adaptive multi-granularity representation and computation method
This mechanism inherently possesses an adaptive multi-granularity description capacity, resulting in computational traits such as efficiency, robustness, and interpretability.
GPT-NER: Named Entity Recognition via Large Language Models
GPT-NER bridges the gap by transforming the sequence labeling task to a generation task that can be easily adapted by LLMs e. g., the task of finding location entities in the input text "Columbus is a city" is transformed to generate the text sequence "@@Columbus## is a city", where special tokens @@## marks the entity to extract.
Granular-ball Optimization Algorithm
The faster speed, higher approximation ability of optimal solution, no hyper-parameters, and simpler design of GBO make it an all-around replacement of most of the existing popular intelligent optimization algorithms.
Open World Classification with Adaptive Negative Samples
Open world classification is a task in natural language processing with key practical relevance and impact.
Research on Efficient Fuzzy Clustering Method Based on Local Fuzzy Granular balls
In recent years, the problem of fuzzy clustering has been widely concerned.
GBMST: An Efficient Minimum Spanning Tree Clustering Based on Granular-Ball Computing
We construct coarsegrained granular-balls, and then use granular-balls and MST to implement the clustering method based on "large-scale priority", which can greatly avoid the influence of outliers and accelerate the construction process of MST.
PK-ICR: Persona-Knowledge Interactive Context Retrieval for Grounded Dialogue
Identifying relevant persona or knowledge for conversational systems is critical to grounded dialogue response generation.
Sketch Less Face Image Retrieval: A New Challenge
In this study, we proposed a new task named sketch less face image retrieval (SLFIR), in which the retrieval was carried out at each stroke and aim to retrieve the target face photo using a partial sketch with as few strokes as possible (see Fig. 1).
A novel cluster internal evaluation index based on hyper-balls
It is crucial to evaluate the quality and determine the optimal number of clusters in cluster analysis.
WL-Align: Weisfeiler-Lehman Relabeling for Aligning Users across Networks via Regularized Representation Learning
Aligning users across networks using graph representation learning has been found effective where the alignment is accomplished in a low-dimensional embedding space.
GNN-SL: Sequence Labeling Based on Nearest Examples via GNN
To better handle long-tail cases in the sequence labeling (SL) task, in this work, we introduce graph neural networks sequence labeling (GNN-SL), which augments the vanilla SL model output with similar tagging examples retrieved from the whole training set.
Granular-Ball Fuzzy Set and Its Implementation in SVM
Most existing fuzzy set methods use points as their input, which is the finest granularity from the perspective of granular computing.
GBSVM: Granular-ball Support Vector Machine
Furthermore, a particle swarm optimization algorithm is designed to solve the dual model.
Ranking-Enhanced Unsupervised Sentence Representation Learning
In this work, we show that the semantic meaning of a sentence is also determined by nearest-neighbor sentences that are similar to the input sentence.
Semi-supervised Learning with Deterministic Labeling and Large Margin Projection
Optimal leading forest (OLF) has been observed to have the advantage of revealing the difference evolution along a path within a subtree.
Learning Personalized Representations using Graph Convolutional Network
Then we propose a graph convolutional network(GCN) based model, namely Personalized Dynamic Routing Feature Encoder(PDRFE), that generates personalized customer representations learned from the built graph.
Advanced Conditional Variational Autoencoders (A-CVAE): Towards interpreting open-domain conversation generation via disentangling latent feature representation
Particularly, the model integrates the macro-level guided-category knowledge and micro-level open-domain dialogue data for the training, leveraging the priori knowledge into the latent space, which enables the model to disentangle the latent variables within the mesoscopic scale.
GBC: An Efficient and Adaptive Clustering Algorithm Based on Granular-Ball
Existing clustering methods are based on a single granularity of information, such as the distance and density of each data.
Improving Downstream Task Performance by Treating Numbers as Entities
Numbers are essential components of text, like any other word tokens, from which natural language processing (NLP) models are built and deployed.
$k$NN-NER: Named Entity Recognition with Nearest Neighbor Search
Inspired by recent advances in retrieval augmented methods in NLP~\citep{khandelwal2019generalization, khandelwal2020nearest, meng2021gnn}, in this paper, we introduce a $k$ nearest neighbor NER ($k$NN-NER) framework, which augments the distribution of entity labels by assigning $k$ nearest neighbors retrieved from the training set.
One-Stage Deep Edge Detection Based on Dense-Scale Feature Fusion and Pixel-Level Imbalance Learning
Edge detection, a basic task in the field of computer vision, is an important preprocessing operation for the recognition and understanding of a visual scene.
Multi-granularity Association Learning Framework for on-the-fly Fine-Grained Sketch-based Image Retrieval
We consider that there is a significant correlation among these incomplete sketches in the sketch drawing episode of each photo.
An Efficient and Adaptive Granular-ball Generation Method in Classification Problem
In addition, this paper first provides the mathematical models for the granular-ball covering.
A Unified Granular-ball Learning Model of Pawlak Rough Set and Neighborhood Rough Set
The granular-ball rough set can simultaneously represent Pawlak rough sets, and the neighborhood rough set, so as to realize the unified representation of the two.
An Efficient and Accurate Rough Set for Feature Selection, Classification and Knowledge Representation
This paper present a strong data mining method based on rough set, which can realize feature selection, classification and knowledge representation at the same time.
Faster Nearest Neighbor Machine Translation
The core idea of Faster $k$NN-MT is to use a hierarchical clustering strategy to approximate the distance between the query and a data point in the datastore, which is decomposed into two parts: the distance between the query and the center of the cluster that the data point belongs to, and the distance between the data point and the cluster center.
Towards Improving Embedding Based Models of Social Network Alignment via Pseudo Anchors
Social network alignment aims at aligning person identities across social networks.
Rethinking the Image Feature Biases Exhibited by Deep CNN Models
From the results, we conclude that (1) the combined effect of certain features is typically far more influential than any single feature; (2) in different tasks, neural models can perform different biases, that is, we can design a specific task to make a neural model biased toward a specific anticipated feature.
Interpreting Deep Learning Models in Natural Language Processing: A Review
Neural network models have achieved state-of-the-art performances in a wide range of natural language processing (NLP) tasks.
Deciding Whether to Ask Clarifying Questions in Large-Scale Spoken Language Understanding
A large-scale conversational agent can suffer from understanding user utterances with various ambiguities such as ASR ambiguity, intent ambiguity, and hypothesis ambiguity.
An MRC Framework for Semantic Role Labeling
We formalize predicate disambiguation as multiple-choice machine reading comprehension, where the descriptions of candidate senses of a given predicate are used as options to select the correct sense.
Ranked #1 on
Semantic Role Labeling
on CoNLL 2005
AUGNLG: Few-shot Natural Language Generation using Self-trained Data Augmentation
Natural Language Generation (NLG) is a key component in a task-oriented dialogue system, which converts the structured meaning representation (MR) to the natural language.
D-Unet: A Dual-encoder U-Net for Image Splicing Forgery Detection and Localization
The unfixed encoder autonomously learns the image fingerprints that differentiate between the tampered and non-tampered regions, whereas the fixed encoder intentionally provides the direction information that assists the learning and detection of the network.
Integrating Task Specific Information into Pretrained Language Models for Low Resource Fine Tuning
Pretrained Language Models (PLMs) have improved the performance of natural language understanding in recent years.
LRA: an accelerated rough set framework based on local redundancy of attribute for feature selection
Based on the theorem, we propose the LRA framework for accelerating rough set algorithms.
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.
Weakly supervised cross-domain alignment with optimal transport
Cross-domain alignment between image objects and text sequences is key to many visual-language tasks, and it poses a fundamental challenge to both computer vision and natural language processing.
Students Need More Attention: BERT-based AttentionModel for Small Data with Application to AutomaticPatient Message Triage
Small and imbalanced datasets commonly seen in healthcare represent a challenge when training classifiers based on deep learning models.
Improving Adversarial Text Generation by Modeling the Distant Future
Auto-regressive text generation models usually focus on local fluency, and may cause inconsistent semantic meaning in long text generation.
Ball k-means
The assigned cluster of the points in the stable area is not changed in the current iteration while the points in the annulus area will be adjusted within a few neighbor clusters in the current iteration.
POINTER: Constrained Progressive Text Generation via Insertion-based Generative Pre-training
Large-scale pre-trained language models, such as BERT and GPT-2, have achieved excellent performance in language representation learning and free-form text generation.
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.
Syntax-Infused Transformer and BERT models for Machine Translation and Natural Language Understanding
Attention-based models have shown significant improvement over traditional algorithms in several NLP tasks.
An End-to-End Generative Architecture for Paraphrase Generation
Generating high-quality paraphrases is a fundamental yet challenging natural language processing task.
Zero-Shot Recognition via Optimal Transport
{Specifically, we build a conditional generative model to generate features from seen-class attributes, and establish an optimal transport between the distribution of the generated features and that of the real features.}
Kernel-Based Approaches for Sequence Modeling: Connections to Neural Methods
We investigate time-dependent data analysis from the perspective of recurrent kernel machines, from which models with hidden units and gated memory cells arise naturally.
Improving Textual Network Learning with Variational Homophilic Embeddings
This paper considers a novel variational formulation of network embeddings, with special focus on textual networks.
Learning Word Embeddings with Domain Awareness
Word embeddings are traditionally trained on a large corpus in an unsupervised setting, with no specific design for incorporating domain knowledge.
Improving Textual Network Embedding with Global Attention via Optimal Transport
Constituting highly informative network embeddings is an important tool for network analysis.
Topic-Guided Variational Auto-Encoder for Text Generation
We propose a topic-guided variational auto-encoder (TGVAE) model for text generation.
Discriminative Clustering for Robust Unsupervised Domain Adaptation
Unsupervised domain adaptation seeks to learn an invariant and discriminative representation for an unlabeled target domain by leveraging the information of a labeled source dataset.
Topic-Guided Variational Autoencoders for Text Generation
We propose a topic-guided variational autoencoder (TGVAE) model for text generation.
Adversarial Learning of a Sampler Based on an Unnormalized Distribution
We investigate adversarial learning in the case when only an unnormalized form of the density can be accessed, rather than samples.
Generative Adversarial Network Training is a Continual Learning Problem
We hypothesize that this is at least in part due to the evolution of the generator distribution and the catastrophic forgetting tendency of neural networks, which leads to the discriminator losing the ability to remember synthesized samples from previous instantiations of the generator.
Sequence Generation with Guider Network
Sequence generation with reinforcement learning (RL) has received significant attention recently.
JointGAN: Multi-Domain Joint Distribution Learning with Generative Adversarial Nets
Distinct from most existing approaches, that only learn conditional distributions, the proposed model aims to learn a joint distribution of multiple random variables (domains).
Baseline Needs More Love: On Simple Word-Embedding-Based Models and Associated Pooling Mechanisms
Many deep learning architectures have been proposed to model the compositionality in text sequences, requiring a substantial number of parameters and expensive computations.
Ranked #1 on
Named Entity Recognition (NER)
on CoNLL 2000
NASH: Toward End-to-End Neural Architecture for Generative Semantic Hashing
Semantic hashing has become a powerful paradigm for fast similarity search in many information retrieval systems.
Joint Embedding of Words and Labels for Text Classification
Word embeddings are effective intermediate representations for capturing semantic regularities between words, when learning the representations of text sequences.
Ranked #11 on
Text Classification
on DBpedia
On the Use of Word Embeddings Alone to Represent Natural Language Sequences
In this paper, we conduct an extensive comparative study between Simple Word Embeddings-based Models (SWEMs), with no compositional parameters, relative to employing word embeddings within RNN/CNN-based models.
Non-iterative Label Propagation in Optimal Leading Forest
We propose a sound assumption, arguing that: the neighboring data points are not in peer-to-peer relation, but in a partial-ordered relation induced by the local density and distance between the data; and the label of a center can be regarded as the contribution of its followers.
Deconvolutional Paragraph Representation Learning
Learning latent representations from long text sequences is an important first step in many natural language processing applications.
Self-training semi-supervised classification based on density peaks of data
Having a multitude of unlabeled data and few labeled ones is a common problem in many practical ap- plications.
Heuristic algorithms for finding distribution reducts in probabilistic rough set model
This is partly due to the fact that there are no monotonic fitness functions that are used to design heuristic attribute reduction algorithms in probabilistic rough set model.
Leading Tree in DPCLUS and Its Impact on Building Hierarchies
There are two major advantages with the LT: One is dramatically reducing the running time of assigning noncenter data points to their cluster ID, because the assigning process is turned into just disconnecting the links from each center to its parent.
