https://github.com/trais-lab/llm-structured-data
공식 코드Can LLMs Effectively Leverage Graph Structural Information through Prompts, and Why?
Papers with Code 아카이브
Text Classification
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Text Classification is the task of assigning a sentence or document an appropriate category. The categories depend on the chosen dataset and can range from topics.
Text Classification problems include emotion classification, news classification, citation intent classification, among others. Benchmark datasets for evaluating text classification capabilities include GLUE, AGNews, among others.
In recent years, deep learning techniques like XLNet and RoBERTa have attained some of the biggest performance jumps for text classification problems.
<span style="color:grey; opacity: 0.6">( Image credit: Text Classification Algorithms: A Survey )</span>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/tripto03/ship_of_theseus_paraphrased_copus
공식 코드A Ship of Theseus: Curious Cases of Paraphrasing in LLM-Generated Texts
https://github.com/truas/LexicalChain_Builder
공식 코드Enhanced word embeddings using multi-semantic representation through lexical chains
https://github.com/trungtrinh44/N-VAE
공식 코드Nested Variational Autoencoder for Topic Modeling on Microtexts with Word Vectors
https://github.com/trust-nlp/LAMKIT
공식 코드Length-Aware Multi-Kernel Transformer for Long Document Classification
https://github.com/trustairlab/synthetic_artifact_auditing
공식 코드Synthetic Artifact Auditing: Tracing LLM-Generated Synthetic Data Usage in Downstream Applications
https://github.com/trusthlt/dp-bart-private-rewriting
공식 코드DP-BART for Privatized Text Rewriting under Local Differential Privacy
https://github.com/trusthlt/privacy-preserving-gcn
공식 코드Privacy-Preserving Graph Convolutional Networks for Text Classification
https://github.com/trustworthy-ml-lab/cb-llms
공식 코드https://github.com/tshi04/ACCE
공식 코드Corpus-level and Concept-based Explanations for Interpretable Document Classification
https://github.com/ttslr/strengthnet
공식 코드Accurate Emotion Strength Assessment for Seen and Unseen Speech Based on Data-Driven Deep Learning
https://github.com/tub-cv-group/conclugen
공식 코드Multi-Task Multi-Modal Self-Supervised Learning for Facial Expression Recognition
https://github.com/tunazislam/latent-themes-llms
공식 코드Discovering Latent Themes in Social Media Messaging: A Machine-in-the-Loop Approach Integrating LLMs
https://github.com/turing-online-safety-codebase/dodo-learning
공식 코드https://github.com/tushar-semwal/TransferLearning_CNN_TextClassification
공식 코드https://github.com/tushar117/Transformer-Models-for-Text-Coherence-Assessment
공식 코드https://github.com/tuvuumass/task-transferability
공식 코드https://github.com/twinkle0331/lgtm
공식 코드Tailoring Instructions to Student's Learning Levels Boosts Knowledge Distillation
https://github.com/tzf101/Bangla-Text-Augmentation-Framework
공식 코드https://github.com/uber/ludwig
공식 코드https://github.com/ucla-scai/Source-LDA
공식 코드Source-LDA: Enhancing probabilistic topic models using prior knowledge sources
https://github.com/ucla-seal/sibyl
공식 코드https://github.com/uclanlp/Fast-and-Robust-Text-Classification
공식 코드https://github.com/uclanlp/nlp-interpretation-faithfulness
공식 코드On the Sensitivity and Stability of Model Interpretations in NLP
https://github.com/uclanlp/robust-xlt
공식 코드Improving Zero-Shot Cross-Lingual Transfer Learning via Robust Training
https://github.com/ucsb-nlp-chang/promptboosting
공식 코드PromptBoosting: Black-Box Text Classification with Ten Forward Passes
https://github.com/ucsc-real/beyondimages
공식 코드Beyond Images: Label Noise Transition Matrix Estimation for Tasks with Lower-Quality Features
https://github.com/ucsc-real/disparate-ssl
공식 코드The Rich Get Richer: Disparate Impact of Semi-Supervised Learning
https://github.com/ucsc-real/est-t-mi
공식 코드Beyond Images: Label Noise Transition Matrix Estimation for Tasks with Lower-Quality Features
https://github.com/uds-lsv/bert-lnl
공식 코드Is BERT Robust to Label Noise? A Study on Learning with Noisy Labels in Text Classification
https://github.com/ukplab/adasent
공식 코드AdaSent: Efficient Domain-Adapted Sentence Embeddings for Few-Shot Classification
https://github.com/ukplab/care
공식 코드https://github.com/ukplab/lagonn
공식 코드Like a Good Nearest Neighbor: Practical Content Moderation and Text Classification
https://github.com/ukplab/nessie
공식 코드Annotation Error Detection: Analyzing the Past and Present for a More Coherent Future
https://github.com/ulf1/sentence-embedding-evaluation-german
공식 코드https://github.com/umanlp/socioadapt
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.