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Language Modeling
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코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/ardianumam/partdistill
공식 코드PartDistill: 3D Shape Part Segmentation by Vision-Language Model Distillation
https://github.com/ari-holtzman/degen
공식 코드https://github.com/arianhosseini/negation-learning
공식 코드Understanding by Understanding Not: Modeling Negation in Language Models
https://github.com/aribrill/scaling-laws-paper
공식 코드https://github.com/ariepratama/syntax-driven-da
공식 코드Syntax-driven Data Augmentation for Named Entity Recognition
https://github.com/arkokoley/salm
공식 코드SALM: A Multi-Agent Framework for Language Model-Driven Social Network Simulation
https://github.com/armandstrickernlp/emo-tod
공식 코드A Unified Approach to Emotion Detection and Task-Oriented Dialogue Modeling
https://github.com/artetxem/monoses
공식 코드https://github.com/artetxem/vecmap
공식 코드https://github.com/arthurleom/dress-llm
공식 코드DRESSing Up LLM: Efficient Stylized Question-Answering via Style Subspace Editing
https://github.com/artificial-scientist-lab/scimuse
공식 코드https://github.com/arumaekawa/dilm
공식 코드DiLM: Distilling Dataset into Language Model for Text-level Dataset Distillation
https://github.com/arvid-pku/atoke
공식 코드History Matters: Temporal Knowledge Editing in Large Language Model
https://github.com/aryan-sajith/urv-data_quantity_vs_data_quality-research
공식 코드Is Training Data Quality or Quantity More Impactful to Small Language Model Performance?
https://github.com/aryopg/vaxformer
공식 코드Vaxformer: Antigenicity-controlled Transformer for Vaccine Design Against SARS-CoV-2
https://github.com/asFeng/Diffuser
공식 코드Diffuser: Efficient Transformers with Multi-hop Attention Diffusion for Long Sequences
https://github.com/asad14053/AdCare-VLM
공식 코드https://github.com/asafamr/SymPatternWSI
공식 코드Word Sense Induction with Neural biLM and Symmetric Patterns
https://github.com/asahi417/analogy-language-model
공식 코드BERT is to NLP what AlexNet is to CV: Can Pre-Trained Language Models Identify Analogies?
https://github.com/asahi417/lm-question-generation
공식 코드An Empirical Comparison of LM-based Question and Answer Generation Methods
https://github.com/asahi417/lm-vocab-trimmer
공식 코드An Efficient Multilingual Language Model Compression through Vocabulary Trimming
https://github.com/asahi417/relbert
공식 코드Distilling Relation Embeddings from Pre-trained Language Models
https://github.com/asahi417/tner
공식 코드T-NER: An All-Round Python Library for Transformer-based Named Entity Recognition
https://github.com/asappresearch/domain-private-transformers
공식 코드https://github.com/asappresearch/flop
공식 코드https://github.com/asappresearch/halugen
공식 코드https://github.com/asappresearch/josh-llm-simulation-training
공식 코드https://github.com/asappresearch/sru
공식 코드When Attention Meets Fast Recurrence: Training Language Models with Reduced Compute
https://github.com/asayeed/activebaby
공식 코드https://github.com/asgsaeid/cad_vqa
공식 코드How to Determine the Preferred Image Distribution of a Black-Box Vision-Language Model?
https://github.com/ash-ai-safety-hub/g3-nandi
공식 코드Comparing Optimization Targets for Contrast-Consistent Search
https://github.com/asiddhant/taskonomy-nlp
공식 코드Supervised Contextual Embeddings for Transfer Learning in Natural Language Processing Tasks
https://github.com/assassinws/llm-tamp
공식 코드LLM3:Large Language Model-based Task and Motion Planning with Motion Failure Reasoning
https://github.com/astra-vision/poda
공식 코드https://github.com/astra-vision/prolip
공식 코드Fine-Tuning CLIP's Last Visual Projector: A Few-Shot Cornucopia
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.