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Found 44 papers, 21 papers with code
On the Transferability of Adversarial Attacks against Neural Text Classifier
Based on these studies, we propose a genetic algorithm to find an ensemble of models that can be used to induce adversarial examples to fool almost all existing models.
Improving Grammatical Error Correction Models with Purpose-Built Adversarial Examples
A sequence-to-sequence (seq2seq) learning with neural networks empirically shows to be an effective framework for grammatical error correction (GEC), which takes a sentence with errors as input and outputs the corrected one.
Towards Adversarially Robust Text Classifiers by Learning to Reweight Clean Examples
Most of the existing defense methods improve the adversarial robustness by making the models adapt to the training set augmented with some adversarial examples.
Tell Me What You Don't Know: Enhancing Refusal Capabilities of Role-Playing Agents via Representation Space Analysis and Editing
To investigate RPAs' performance when faced with different types of conflicting requests, we develop an evaluation benchmark that includes contextual knowledge conflicting requests, parametric knowledge conflicting requests, and non-conflicting requests to assess RPAs' ability to identify conflicts and refuse to answer appropriately without over-refusing.
Dual Advancement of Representation Learning and Clustering for Sparse and Noisy Images
Sparse and noisy images (SNIs), like those in spatial gene expression data, pose significant challenges for effective representation learning and clustering, which are essential for thorough data analysis and interpretation.
Searching for Best Practices in Retrieval-Augmented Generation
Retrieval-augmented generation (RAG) techniques have proven to be effective in integrating up-to-date information, mitigating hallucinations, and enhancing response quality, particularly in specialized domains.
Enhancing the Capability and Robustness of Large Language Models through Reinforcement Learning-Driven Query Refinement
This strategy improves the quality of the queries, empowering LLMs to generate more truthful, benign and useful responses.
Spiking Convolutional Neural Networks for Text Classification
Spiking neural networks (SNNs) offer a promising pathway to implement deep neural networks (DNNs) in a more energy-efficient manner since their neurons are sparsely activated and inferences are event-driven.
Towards Biologically Plausible Computing: A Comprehensive Comparison
Backpropagation is a cornerstone algorithm in training neural networks for supervised learning, which uses a gradient descent method to update network weights by minimizing the discrepancy between actual and desired outputs.
Promoting Data and Model Privacy in Federated Learning through Quantized LoRA
Conventional federated learning primarily aims to secure the privacy of data distributed across multiple edge devices, with the global model dispatched to edge devices for parameter updates during the learning process.
Advancing Spiking Neural Networks for Sequential Modeling with Central Pattern Generators
Spiking neural networks (SNNs) represent a promising approach to developing artificial neural networks that are both energy-efficient and biologically plausible.
Decoding Continuous Character-based Language from Non-invasive Brain Recordings
Deciphering natural language from brain activity through non-invasive devices remains a formidable challenge.
Advancing Parameter Efficiency in Fine-tuning via Representation Editing
Parameter Efficient Fine-Tuning (PEFT) techniques have drawn significant attention due to their ability to yield competitive results while updating only a small portion of the adjustable parameters.
Efficient and Effective Time-Series Forecasting with Spiking Neural Networks
In this paper, we propose a framework for SNNs in time-series forecasting tasks, leveraging the efficiency of spiking neurons in processing temporal information.
Rethinking Jailbreaking through the Lens of Representation Engineering
The recent surge in jailbreaking methods has revealed the vulnerability of Large Language Models (LLMs) to malicious inputs.
Aligning Large Language Models with Human Preferences through Representation Engineering
Aligning large language models (LLMs) with human preferences is crucial for enhancing their utility in terms of helpfulness, truthfulness, safety, harmlessness, and interestingness.
Tailoring Personality Traits in Large Language Models via Unsupervisedly-Built Personalized Lexicons
Users can utilize UBPL to adjust the probability vectors of predicted words in the decoding phase of LLMs, thus influencing the personality expression of LLMs.
Unraveling the Enigma of Double Descent: An In-depth Analysis through the Lens of Learned Feature Space
Double descent presents a counter-intuitive aspect within the machine learning domain, and researchers have observed its manifestation in various models and tasks.
SpikeCLIP: A Contrastive Language-Image Pretrained Spiking Neural Network
Spiking Neural Networks (SNNs) have emerged as a promising alternative to conventional Artificial Neural Networks (ANNs), demonstrating comparable performance in both visual and linguistic tasks while offering the advantage of improved energy efficiency.
SpikeBERT: A Language Spikformer Learned from BERT with Knowledge Distillation
Spiking neural networks (SNNs) offer a promising avenue to implement deep neural networks in a more energy-efficient way.
Spinal nerve segmentation method and dataset construction in endoscopic surgical scenarios
Endoscopic surgery is currently an important treatment method in the field of spinal surgery and avoiding damage to the spinal nerves through video guidance is a key challenge.
Improving Empathetic Dialogue Generation by Dynamically Infusing Commonsense Knowledge
To this end, we propose a novel approach for empathetic response generation, which incorporates an adaptive module for commonsense knowledge selection to ensure consistency between the generated empathetic responses and the speaker's situation.
Watermarking Pre-trained Language Models with Backdooring
Large pre-trained language models (PLMs) have proven to be a crucial component of modern natural language processing systems.
Improving the Adversarial Robustness of NLP Models by Information Bottleneck
Existing studies have demonstrated that adversarial examples can be directly attributed to the presence of non-robust features, which are highly predictive, but can be easily manipulated by adversaries to fool NLP models.
Searching for an Effective Defender: Benchmarking Defense against Adversarial Word Substitution
Recent studies have shown that deep neural networks are vulnerable to intentionally crafted adversarial examples, and various methods have been proposed to defend against adversarial word-substitution attacks for neural NLP models.
Defense against Synonym Substitution-based Adversarial Attacks via Dirichlet Neighborhood Ensemble
Although deep neural networks have achieved prominent performance on many NLP tasks, they are vulnerable to adversarial examples.
Exploration and Exploitation: Two Ways to Improve Chinese Spelling Correction Models
A sequence-to-sequence learning with neural networks has empirically proven to be an effective framework for Chinese Spelling Correction (CSC), which takes a sentence with some spelling errors as input and outputs the corrected one.
Certified Robustness to Text Adversarial Attacks by Randomized [MASK]
Recently, few certified defense methods have been developed to provably guarantee the robustness of a text classifier to adversarial synonym substitutions.
SparseGAN: Sparse Generative Adversarial Network for Text Generation
It is still a challenging task to learn a neural text generation model under the framework of generative adversarial networks (GANs) since the entire training process is not differentiable.
Alleviate Exposure Bias in Sequence Prediction \\ with Recurrent Neural Networks
The key idea is that at each time step, the network takes as input a ``bundle'' of similar words predicted at the previous step instead of a single ground truth.
TextFlint: Unified Multilingual Robustness Evaluation Toolkit for Natural Language Processing
To guarantee user acceptability, all the text transformations are linguistically based, and we provide a human evaluation for each one.
Unsupervised Word Segmentation with Bi-directional Neural Language Model
We present an unsupervised word segmentation model, in which the learning objective is to maximize the generation probability of a sentence given its all possible segmentation.
On the Transferability of Adversarial Attacksagainst Neural Text Classifier
Based on these studies, we propose a genetic algorithm to find an ensemble of models that can be used to induce adversarial examples to fool almost all existing models.
Cross-Lingual Dependency Parsing by POS-Guided Word Reordering
The words in each sentence of a source language corpus are rearranged to meet the word order in a target language under the guidance of a part-of-speech based language model (LM).
A deep learning-based approach for the automated surface inspection of copper clad laminate images
A machine vision-based surface quality inspection system is usually composed of two processes: image acquisition and automatic defect detection.
Unsupervised Summarization by Jointly Extracting Sentences and Keywords
We present RepRank, an unsupervised graph-based ranking model for extractive multi-document summarization in which the similarity between words, sentences, and word-to-sentence can be estimated by the distances between their vector representations in a unified vector space.
Improving Coreference Resolution by Leveraging Entity-Centric Features with Graph Neural Networks and Second-order Inference
One of the major challenges in coreference resolution is how to make use of entity-level features defined over clusters of mentions rather than mention pairs.
Evaluating and Enhancing the Robustness of Neural Network-based Dependency Parsing Models with Adversarial Examples
Despite achieving prominent performance on many important tasks, it has been reported that neural networks are vulnerable to adversarial examples.
Defense against Adversarial Attacks in NLP via Dirichlet Neighborhood Ensemble
Despite neural networks have achieved prominent performance on many natural language processing (NLP) tasks, they are vulnerable to adversarial examples.
Learning Structured Embeddings of Knowledge Graphs with Adversarial Learning Framework
A generative network (GN) takes two elements of a (subject, predicate, object) triple as input and generates the vector representation of the missing element.
Chinese Named Entity Recognition Augmented with Lexicon Memory
Inspired by a concept of content-addressable retrieval from cognitive science, we propose a novel fragment-based model augmented with a lexicon-based memory for Chinese NER, in which both the character-level and word-level features are combined to generate better feature representations for possible name candidates.
Chinese Named Entity Recognition
named-entity-recognition
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Generating Responses with a Specific Emotion in Dialog
It is desirable for dialog systems to have capability to express specific emotions during a conversation, which has a direct, quantifiable impact on improvement of their usability and user satisfaction.
Incremental Graph-based Neural Dependency Parsing
Very recently, some studies on neural dependency parsers have shown advantage over the traditional ones on a wide variety of languages.
