Search Results for author:
Found 77 papers, 39 papers with code
MoleculeQA: A Dataset to Evaluate Factual Accuracy in Molecular Comprehension
Large language models are playing an increasingly significant role in molecular research, yet existing models often generate erroneous information, posing challenges to accurate molecular comprehension.
UltraWiki: Ultra-fine-grained Entity Set Expansion with Negative Seed Entities
To solve this issue, we first introduce negative seed entities in the inputs, which belong to the same fine-grained semantic class as the positive seed entities but differ in certain attributes.
Let LLMs Take on the Latest Challenges! A Chinese Dynamic Question Answering Benchmark
To promote the improvement of Chinese LLMs' ability to answer dynamic questions, in this paper, we introduce CDQA, a Chinese Dynamic QA benchmark containing question-answer pairs related to the latest news on the Chinese Internet.
Mitigating Catastrophic Forgetting in Multi-domain Chinese Spelling Correction by Multi-stage Knowledge Transfer Framework
Chinese Spelling Correction (CSC) aims to detect and correct spelling errors in given sentences.
Rethinking the Roles of Large Language Models in Chinese Grammatical Error Correction
To promote the CGEC field to better adapt to the era of LLMs, we rethink the roles of LLMs in the CGEC task so that they can be better utilized and explored in CGEC.
When LLMs Meet Cunning Questions: A Fallacy Understanding Benchmark for Large Language Models
In this paper, we challenge the reasoning and understanding abilities of LLMs by proposing a FaLlacy Understanding Benchmark (FLUB) containing cunning questions that are easy for humans to understand but difficult for models to grasp.
EcomGPT-CT: Continual Pre-training of E-commerce Large Language Models with Semi-structured Data
Experimental results demonstrate the effectiveness of continual pre-training of E-commerce LLMs and the efficacy of our devised data mixing strategy.
RLHF-V: Towards Trustworthy MLLMs via Behavior Alignment from Fine-grained Correctional Human Feedback
Multimodal Large Language Models (MLLMs) have recently demonstrated impressive capabilities in multimodal understanding, reasoning, and interaction.
Towards Real-World Writing Assistance: A Chinese Character Checking Benchmark with Faked and Misspelled Characters
To the best of our knowledge, Visual-C$^3$ is the first real-world visual and the largest human-crafted dataset for the Chinese character checking scenario.
MixEdit: Revisiting Data Augmentation and Beyond for Grammatical Error Correction
In this paper, we aim to clarify how data augmentation improves GEC models.
A Frustratingly Easy Plug-and-Play Detection-and-Reasoning Module for Chinese Spelling Check
In recent years, Chinese Spelling Check (CSC) has been greatly improved by designing task-specific pre-training methods or introducing auxiliary tasks, which mostly solve this task in an end-to-end fashion.
Reformulating Vision-Language Foundation Models and Datasets Towards Universal Multimodal Assistants
The capabilities of MLLMs depend on two crucial factors: the model architecture to facilitate the feature alignment of visual modules and large language models; the multimodal instruction tuning datasets for human instruction following.
Retrieval-Augmented Meta Learning for Low-Resource Text Classification
However, due to the limited training data in the meta-learning scenario and the inherent properties of parameterized neural networks, poor generalization performance has become a pressing problem that needs to be addressed.
Prompt Learning With Knowledge Memorizing Prototypes For Generalized Few-Shot Intent Detection
Previous GFSID methods rely on the episodic learning paradigm, which makes it hard to extend to a generalized setup as they do not explicitly learn the classification of seen categories and the knowledge of seen intents.
An Anchor Learning Approach for Citation Field Learning
CIFAL leverages the anchor learning, which is model-agnostic for any Pre-trained Language Model, to help capture citation patterns from the data of different citation styles.
MISSRec: Pre-training and Transferring Multi-modal Interest-aware Sequence Representation for Recommendation
We propose MISSRec, a multi-modal pre-training and transfer learning framework for SR. On the user side, we design a Transformer-based encoder-decoder model, where the contextual encoder learns to capture the sequence-level multi-modal user interests while a novel interest-aware decoder is developed to grasp item-modality-interest relations for better sequence representation.
LatEval: An Interactive LLMs Evaluation Benchmark with Incomplete Information from Lateral Thinking Puzzles
With the continuous evolution and refinement of LLMs, they are endowed with impressive logical reasoning or vertical thinking capabilities.
SeqGPT: An Out-of-the-box Large Language Model for Open Domain Sequence Understanding
However, LLMs are sometimes too footloose for natural language understanding (NLU) tasks which always have restricted output and input format.
EcomGPT: Instruction-tuning Large Language Models with Chain-of-Task Tasks for E-commerce
EcomInstruct scales up the data size and task diversity by constructing atomic tasks with E-commerce basic data types, such as product information, user reviews.
MESED: A Multi-modal Entity Set Expansion Dataset with Fine-grained Semantic Classes and Hard Negative Entities
The Entity Set Expansion (ESE) task aims to expand a handful of seed entities with new entities belonging to the same semantic class.
On the (In)Effectiveness of Large Language Models for Chinese Text Correction
Recently, the development and progress of Large Language Models (LLMs) have amazed the entire Artificial Intelligence community.
Correct Like Humans: Progressive Learning Framework for Chinese Text Error Correction
During the training process, ProTEC guides the model to learn text error correction by incorporating these sub-tasks into a progressive paradigm.
AOG-LSTM: An adaptive attention neural network for visual storytelling
Moreover, the existing method of alleviating error accumulation based on replacing reference words does not take into account the different effects of each word.
Ranked #20 on
Visual Storytelling
on VIST
Bidirectional End-to-End Learning of Retriever-Reader Paradigm for Entity Linking
Entity Linking (EL) is a fundamental task for Information Extraction and Knowledge Graphs.
Mining Interest Trends and Adaptively Assigning SampleWeight for Session-based Recommendation
Session-based Recommendation (SR) aims to predict users' next click based on their behavior within a short period, which is crucial for online platforms.
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.
CLEME: Debiasing Multi-reference Evaluation for Grammatical Error Correction
Evaluating the performance of Grammatical Error Correction (GEC) systems is a challenging task due to its subjectivity.
AR-Diffusion: Auto-Regressive Diffusion Model for Text Generation
Diffusion models have gained significant attention in the realm of image generation due to their exceptional performance.
Knowledge Soft Integration for Multimodal Recommendation
To address this issue, we propose using knowledge soft integration to balance the utilization of multimodal features and the curse of knowledge problem it brings about.
From Retrieval to Generation: Efficient and Effective Entity Set Expansion
For efficiency, expansion time consumed by GenExpan is independent of entity vocabulary and corpus size, and GenExpan achieves an average 600% speedup compared to strong baselines.
MoocRadar: A Fine-grained and Multi-aspect Knowledge Repository for Improving Cognitive Student Modeling in MOOCs
Student modeling, the task of inferring a student's learning characteristics through their interactions with coursework, is a fundamental issue in intelligent education.
Click-aware Structure Transfer with Sample Weight Assignment for Post-Click Conversion Rate Estimation
To tackle this issue, we argue that a trade-off should be achieved between the introduction of large amounts of auxiliary information and the protection of valuable information related to CVR.
Knowledge-augmented Few-shot Visual Relation Detection
Inspired by this, we devise a knowledge-augmented, few-shot VRD framework leveraging both textual knowledge and visual relation knowledge to improve the generalization ability of few-shot VRD.
Vision, Deduction and Alignment: An Empirical Study on Multi-modal Knowledge Graph Alignment
Entity alignment (EA) for knowledge graphs (KGs) plays a critical role in knowledge engineering.
Establishing a stronger baseline for lightweight contrastive models
A common practice to address this problem is to introduce a pretrained contrastive teacher model and train the lightweight networks with distillation signals generated by the teacher.
Visually Grounded Commonsense Knowledge Acquisition
In this work, we present CLEVER, which formulates CKE as a distantly supervised multi-instance learning problem, where models learn to summarize commonsense relations from a bag of images about an entity pair without any human annotation on image instances.
Embracing Ambiguity: Improving Similarity-oriented Tasks with Contextual Synonym Knowledge
Extensive experiments demonstrate that PICSO can dramatically outperform the original PLMs and the other knowledge and synonym injection models on four different similarity-oriented tasks.
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.
Active Relation Discovery: Towards General and Label-aware Open Relation Extraction
Open Relation Extraction (OpenRE) aims to discover novel relations from open domains.
Towards Attribute-Entangled Controllable Text Generation: A Pilot Study of Blessing Generation
Facing this dilemma, we focus on a novel CTG scenario, i. e., blessing generation which is challenging because high-quality blessing texts require CTG models to comprehensively consider the entanglement between multiple attributes (e. g., objects and occasions).
A Curriculum Learning Approach for Multi-domain Text Classification Using Keyword weight Ranking
We noted that the distinctness of the domain-specific features is different, so in this paper, we propose to use a curriculum learning strategy based on keyword weight ranking to improve the performance of multi-domain text classification models.
Focus Is What You Need For Chinese Grammatical Error Correction
Chinese Grammatical Error Correction (CGEC) aims to automatically detect and correct grammatical errors contained in Chinese text.
CLOWER: A Pre-trained Language Model with Contrastive Learning over Word and Character Representations
However, most current models use Chinese characters as inputs and are not able to encode semantic information contained in Chinese words.
Automatic Context Pattern Generation for Entity Set Expansion
In addition, we propose the GAPA, a novel ESE framework that leverages the aforementioned GenerAted PAtterns to expand target entities.
Contextual Similarity is More Valuable than Character Similarity: An Empirical Study for Chinese Spell Checking
Chinese Spell Checking (CSC) task aims to detect and correct Chinese spelling errors.
Contrastive Learning with Hard Negative Entities for Entity Set Expansion
In addition, we propose the ProbExpan, a novel probabilistic ESE framework utilizing the entity representation obtained by the aforementioned language model to expand entities.
Sememe Prediction for BabelNet Synsets using Multilingual and Multimodal Information
In this paper, we utilize the multilingual synonyms, multilingual glosses and images in BabelNet for SPBS.
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.
Efficient Sub-structured Knowledge Distillation
Specifically, we transfer the knowledge from a teacher model to its student model by locally matching their predictions on all sub-structures, instead of the whole output space.
The Past Mistake is the Future Wisdom: Error-driven Contrastive Probability Optimization for Chinese Spell Checking
However, there exists a gap between the learned knowledge of PLMs and the goal of CSC task.
Are we ready for a new paradigm shift? A Survey on Visual Deep MLP
In the GPU era, the locally and globally weighted summations are the current mainstreams, represented by the convolution and self-attention mechanism, as well as MLP.
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.
A non-hierarchical attention network with modality dropout for textual response generation in multimodal dialogue systems
Existing text- and image-based multimodal dialogue systems use the traditional Hierarchical Recurrent Encoder-Decoder (HRED) framework, which has an utterance-level encoder to model utterance representation and a context-level encoder to model context representation.
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.
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.
Sentence Semantic Regression for Text Generation
In the phase of surface realization, a mixed-granularity sentence decoder is designed to generate text with better consistency by jointly incorporating the predicted sentence-level main idea as well as the preceding contextual token-level information.
CLINE: Contrastive Learning with Semantic Negative Examples for Natural Language Understanding
Recent works aimed to improve the robustness of pre-trained models mainly focus on adversarial training from perturbed examples with similar semantics, neglecting the utilization of different or even opposite semantics.
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)
Prototypical Representation Learning for Relation Extraction
This approach allows us to learn meaningful, interpretable prototypes for the final classification.
Learning Purified Feature Representations from Task-irrelevant Labels
Learning an empirically effective model with generalization using limited data is a challenging task for deep neural networks.
Generating Diversified Comments via Reader-Aware Topic Modeling and Saliency Detection
Automatic comment generation is a special and challenging task to verify the model ability on news content comprehension and language generation.
Learning to Augment for Data-Scarce Domain BERT Knowledge Distillation
Despite pre-trained language models such as BERT have achieved appealing performance in a wide range of natural language processing tasks, they are computationally expensive to be deployed in real-time applications.
Towards All-around Knowledge Transferring: Learning From Task-irrelevant Labels
We also provide a theoretical explanation of our method.
Facial Expression Recognition
Facial Expression Recognition (FER)
+2
Consistency and Coherency Enhanced Story Generation
In terms of consistency, on one hand, GPT2 cannot guarantee the consistency of the plots explicitly.
Coupling Distant Annotation and Adversarial Training for Cross-Domain Chinese Word Segmentation
In order to simultaneously alleviate these two issues, this paper proposes to couple distant annotation and adversarial training for cross-domain CWS.
Blacklight: Scalable Defense for Neural Networks against Query-Based Black-Box Attacks
In particular, query-based black-box attacks do not require knowledge of the deep learning model, but can compute adversarial examples over the network by submitting queries and inspecting returns.
The World is Not Binary: Learning to Rank with Grayscale Data for Dialogue Response Selection
Despite that response selection is naturally a learning-to-rank problem, most prior works take a point-wise view and train binary classifiers for this task: each response candidate is labeled either relevant (one) or irrelevant (zero).
Ranked #10 on
Conversational Response Selection
on E-commerce
Fawkes: Protecting Privacy against Unauthorized Deep Learning Models
In this paper, we propose Fawkes, a system that helps individuals inoculate their images against unauthorized facial recognition models.
Event Detection with Trigger-Aware Lattice Neural Network
To ad- dress the two issues simultaneously, we pro- pose the Trigger-aware Lattice Neural Net- work (TLNN).
CARL: Aggregated Search with Context-Aware Module Embedding Learning
To model and utilize the context information for aggregated search, we propose a model with context attention and representation learning (CARL).
Chinese Relation Extraction with Multi-Grained Information and External Linguistic Knowledge
Chinese relation extraction is conducted using neural networks with either character-based or word-based inputs, and most existing methods typically suffer from segmentation errors and ambiguity of polysemy.
Regula Sub-rosa: Latent Backdoor Attacks on Deep Neural Networks
Recent work has proposed the concept of backdoor attacks on deep neural networks (DNNs), where misbehaviors are hidden inside "normal" models, only to be triggered by very specific inputs.
Gotta Catch 'Em All: Using Honeypots to Catch Adversarial Attacks on Neural Networks
Attackers' optimization algorithms gravitate towards trapdoors, leading them to produce attacks similar to trapdoors in the feature space.
Addressing Training Bias via Automated Image Annotation
Build accurate DNN models requires training on large labeled, context specific datasets, especially those matching the target scenario.
ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design
Datasets, Transforms and Models specific to Computer Vision
Ranked #873 on
Image Classification
on ImageNet
Automated Crowdturfing Attacks and Defenses in Online Review Systems
Malicious crowdsourcing forums are gaining traction as sources of spreading misinformation online, but are limited by the costs of hiring and managing human workers.
Cryptography and Security Social and Information Networks
