https://github.com/computationalprivacy/document-level-membership-inference
공식 코드Did the Neurons Read your Book? Document-level Membership Inference for Large Language Models
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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/conglei2xu/global-context-mechanism
공식 코드Supplementary Features of BiLSTM for Enhanced Sequence Labeling
https://github.com/contextscout/gcn_ner
공식 코드https://github.com/cooplo/multimodal-pde
공식 코드Explain Like I'm Five: Using LLMs to Improve PDE Surrogate Models with Text
https://github.com/copenlu/cite-worth
공식 코드CiteWorth: Cite-Worthiness Detection for Improved Scientific Document Understanding
https://github.com/copenlu/sufficient_facts
공식 코드https://github.com/cosmmb/DCNN
공식 코드Dependency-based Convolutional Neural Networks for Sentence Embedding
https://github.com/cpaaax/utged
공식 코드Topic-Guided Self-Introduction Generation for Social Media Users
https://github.com/cr-copec/cr-copec
공식 코드CR-COPEC: Causal Rationale of Corporate Performance Changes to Learn from Financial Reports
https://github.com/craffel/mad
공식 코드Online and Linear-Time Attention by Enforcing Monotonic Alignments
https://github.com/crodriguezo/TMLGA
공식 코드https://github.com/crodriguezo/dori
공식 코드DORi: Discovering Object Relationship for Moment Localization of a Natural-Language Query in Video
https://github.com/cs329yangzhong/Document-Simplification-Deletion
공식 코드Discourse Level Factors for Sentence Deletion in Text Simplification
https://github.com/csebuetnlp/banglanmt
공식 코드https://github.com/csitfun/ConTRoL-dataset
공식 코드Natural Language Inference in Context -- Investigating Contextual Reasoning over Long Texts
https://github.com/csjianyang/ganlm
공식 코드GanLM: Encoder-Decoder Pre-training with an Auxiliary Discriminator
https://github.com/cslydia/Hire-NER
공식 코드Hierarchical Contextualized Representation for Named Entity Recognition
https://github.com/csu-signal/axomiyaberta
공식 코드AxomiyaBERTa: A Phonologically-aware Transformer Model for Assamese
https://github.com/ct123098/factoid-wiki
공식 코드Dense X Retrieval: What Retrieval Granularity Should We Use?
https://github.com/cuhksz-nlp/SAPar
공식 코드https://github.com/cuijin-23/ecan
공식 코드https://github.com/cuiniki/llms-are-not-scorers
공식 코드LLMs Are Not Scorers: Rethinking MT Evaluation with Generation-Based Methods
https://github.com/curai/curai-research
공식 코드MEDCOD: A Medically-Accurate, Emotive, Diverse, and Controllable Dialog System
https://github.com/cvikasreddy/skt
공식 코드https://github.com/cwbae10-purdue/cti-eacl24
공식 코드Threat Behavior Textual Search by Attention Graph Isomorphism
https://github.com/cwszz/xpr
공식 코드https://github.com/cyberagentailab/faithcamera
공식 코드FaithCAMERA: Construction of a Faithful Dataset for Ad Text Generation
https://github.com/cycraft-corp/CmdCaliper
공식 코드CmdCaliper: A Semantic-Aware Command-Line Embedding Model and Dataset for Security Research
https://github.com/cyr19/syntaxchange
공식 코드Syntactic Language Change in English and German: Metrics, Parsers, and Convergences
https://github.com/cytai/srlood
공식 코드https://github.com/cyx617/Text-Classification-MSFF-QDConv-Model
공식 코드Multi-Scale Feature Fusion Quantum Depthwise Convolutional Neural Networks for Text Classification
https://github.com/czyssrs/GloREPlus
공식 코드Global Textual Relation Embedding for Relational Understanding
https://github.com/d-ailin/clip-guided-decoding
공식 코드https://github.com/dadelani/sib-200
공식 코드https://github.com/daiquocnguyen/EventPrediction
공식 코드https://github.com/dair-iitd/PoolingAnalysis
공식 코드Why and when should you pool? Analyzing Pooling in Recurrent Architectures
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.