Search Results for author:
Found 25 papers, 18 papers with code
Correlation-Aware Graph Convolutional Networks for Multi-Label Node Classification
Multi-label node classification is an important yet under-explored domain in graph mining as many real-world nodes belong to multiple categories rather than just a single one.
Towards Rehearsal-Free Multilingual ASR: A LoRA-based Case Study on Whisper
Pre-trained multilingual speech foundation models, like Whisper, have shown impressive performance across different languages.
fairBERTs: Erasing Sensitive Information Through Semantic and Fairness-aware Perturbations
Pre-trained language models (PLMs) have revolutionized both the natural language processing research and applications.
On the Role of Long-tail Knowledge in Retrieval Augmented Large Language Models
Retrieval augmented generation (RAG) exhibits outstanding performance in promoting the knowledge capabilities of large language models (LLMs) with retrieved documents related to user queries.
UniPSDA: Unsupervised Pseudo Semantic Data Augmentation for Zero-Shot Cross-Lingual Natural Language Understanding
Specifically, to retrieve the tokens with similar meanings for the semantic data augmentation across different languages, we propose a sequential clustering process in 3 stages: within a single language, across multiple languages of a language family, and across languages from multiple language families.
KEHRL: Learning Knowledge-Enhanced Language Representations with Hierarchical Reinforcement Learning
Knowledge-enhanced pre-trained language models (KEPLMs) leverage relation triples from knowledge graphs (KGs) and integrate these external data sources into language models via self-supervised learning.
NoisyGL: A Comprehensive Benchmark for Graph Neural Networks under Label Noise
However, due to variations in dataset selection, data splitting, and preprocessing techniques, the community currently lacks a comprehensive benchmark, which impedes deeper understanding and further development of GLN.
DAFNet: Dynamic Auxiliary Fusion for Sequential Model Editing in Large Language Models
(2) Considering that auxiliary parameters are required to store the knowledge for sequential editing, we construct a new dataset named \textbf{DAFSet}, fulfilling recent, popular, long-tail and robust properties to enhance the generality of sequential editing.
S-Eval: Automatic and Adaptive Test Generation for Benchmarking Safety Evaluation of Large Language Models
Large Language Models have gained considerable attention for their revolutionary capabilities.
Lifelong Knowledge Editing for LLMs with Retrieval-Augmented Continuous Prompt Learning
Model editing aims to correct outdated or erroneous knowledge in large language models (LLMs) without the need for costly retraining.
R4: Reinforced Retriever-Reorder-Responder for Retrieval-Augmented Large Language Models
The reordering learning process is divided into two steps according to the quality of the generated responses: document order adjustment and document representation enhancement.
TRELM: Towards Robust and Efficient Pre-training for Knowledge-Enhanced Language Models
KEPLMs are pre-trained models that utilize external knowledge to enhance language understanding.
General Phrase Debiaser: Debiasing Masked Language Models at a Multi-Token Level
The social biases and unwelcome stereotypes revealed by pretrained language models are becoming obstacles to their application.
From Adversarial Arms Race to Model-centric Evaluation: Motivating a Unified Automatic Robustness Evaluation Framework
In our experiments, we conduct a robustness evaluation of RoBERTa models to demonstrate the effectiveness of our evaluation framework, and further show the rationality of each component in the framework.
Large Language Models Can be Lazy Learners: Analyze Shortcuts in In-Context Learning
Large language models (LLMs) have recently shown great potential for in-context learning, where LLMs learn a new task simply by conditioning on a few input-label pairs (prompts).
Decoder Tuning: Efficient Language Understanding as Decoding
With the evergrowing sizes of pre-trained models (PTMs), it has been an emerging practice to only provide the inference APIs for users, namely model-as-a-service (MaaS) setting.
Text Editing as Imitation Game
Text editing, such as grammatical error correction, arises naturally from imperfect textual data.
Syntax-guided Localized Self-attention by Constituency Syntactic Distance
Recent works have revealed that Transformers are implicitly learning the syntactic information in its lower layers from data, albeit is highly dependent on the quality and scale of the training data.
Why Should Adversarial Perturbations be Imperceptible? Rethink the Research Paradigm in Adversarial NLP
We discuss the deficiencies in previous work and propose our suggestions that the research on the Security-oriented adversarial NLP (SoadNLP) should: (1) evaluate their methods on security tasks to demonstrate the real-world concerns; (2) consider real-world attackers' goals, instead of developing impractical methods.
Supervised Prototypical Contrastive Learning for Emotion Recognition in Conversation
Capturing emotions within a conversation plays an essential role in modern dialogue systems.
Ranked #4 on
Emotion Recognition in Conversation
on EmoryNLP
EmotionFlow: Capture the Dialogue Level Emotion Transitions
However, the spread impact of emotions in a conversation is rarely addressed in existing researches.
Ranked #19 on
Emotion Recognition in Conversation
on MELD
RMGN: A Regional Mask Guided Network for Parser-free Virtual Try-on
Virtual try-on(VTON) aims at fitting target clothes to reference person images, which is widely adopted in e-commerce. Existing VTON approaches can be narrowly categorized into Parser-Based(PB) and Parser-Free(PF) by whether relying on the parser information to mask the persons' clothes and synthesize try-on images.
Prototypical Verbalizer for Prompt-based Few-shot Tuning
However, manual verbalizers heavily depend on domain-specific prior knowledge and human efforts, while finding appropriate label words automatically still remains challenging. In this work, we propose the prototypical verbalizer (ProtoVerb) which is built directly from training data.
SpanMlt: A Span-based Multi-Task Learning Framework for Pair-wise Aspect and Opinion Terms Extraction
To this end, this paper proposes an end-to-end method to solve the task of Pair-wise Aspect and Opinion Terms Extraction (PAOTE).
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
+3
Automated Strabismus Detection for Telemedicine Applications
Telemedicine, which has great potential to alleviate the growing demand of the diagnosis of ophthalmologic diseases, is an effective method to achieve timely strabismus detection.
