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Language Modeling

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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/alibaba/EasyNLP

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Meta-KD: A Meta Knowledge Distillation Framework for Language Model Compression across Domains

https://github.com/alibaba/cloudeval-yaml

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CloudEval-YAML: A Practical Benchmark for Cloud Configuration Generation

https://github.com/alibaba/graphtranslator

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GraphTranslator: Aligning Graph Model to Large Language Model for Open-ended Tasks

https://github.com/alibaba/self-augmentation-strategy

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SAS: Self-Augmentation Strategy for Language Model Pre-training

https://github.com/alibaba/simcse-with-cards

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Improving Contrastive Learning of Sentence Embeddings with Case-Augmented Positives and Retrieved Negatives

https://github.com/alibabaresearch/advancedliteratemachinery

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Multi-Granularity Prediction for Scene Text Recognition

https://github.com/alibabaresearch/damo-convai

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SPACE-3: Unified Dialog Model Pre-training for Task-Oriented Dialog Understanding and Generation

https://github.com/alickzhu/player

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PLAYER*: Enhancing LLM-based Multi-Agent Communication and Interaction in Murder Mystery Games

https://github.com/alignmentresearch/gpt-4-novel-apis-attacks

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Exploiting Novel GPT-4 APIs

https://github.com/alinlab/ifseg

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IFSeg: Image-free Semantic Segmentation via Vision-Language Model

https://github.com/alipay/PainlessInferenceAcceleration

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Lookahead: An Inference Acceleration Framework for Large Language Model with Lossless Generation Accuracy

https://github.com/alipay/StructuredLM_RTDT

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R2D2: Recursive Transformer based on Differentiable Tree for Interpretable Hierarchical Language Modeling

https://github.com/alirezasalemi7/erag

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Evaluating Retrieval Quality in Retrieval-Augmented Generation

https://github.com/alisaadatv/esm2-missense-impact-analysis

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Fine-tuning the ESM2 protein language model to understand the functional impact of missense variants

https://github.com/alisafaya/mukayese

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Mukayese: Turkish NLP Strikes Back

https://github.com/alisafaya/neurocache

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Neurocache: Efficient Vector Retrieval for Long-range Language Modeling

https://github.com/alisawuffles/DExperts

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DExperts: Decoding-Time Controlled Text Generation with Experts and Anti-Experts

https://github.com/allenai/OLMoE

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OLMoE: Open Mixture-of-Experts Language Models

https://github.com/allenai/VILA

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VILA: Improving Structured Content Extraction from Scientific PDFs Using Visual Layout Groups

https://github.com/allenai/better-promptability

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Continued Pretraining for Better Zero- and Few-Shot Promptability

https://github.com/allenai/catwalk

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Catwalk: A Unified Language Model Evaluation Framework for Many Datasets

https://github.com/allenai/document-qa

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When to Fold'em: How to answer Unanswerable questions

https://github.com/allenai/dolma

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Dolma: an Open Corpus of Three Trillion Tokens for Language Model Pretraining Research

https://github.com/allenai/flex

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FLEX: Unifying Evaluation for Few-Shot NLP

https://github.com/allenai/genesys

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Language Modeling by Language Models

https://github.com/allenai/kb

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Knowledge Enhanced Contextual Word Representations

https://github.com/allenai/longformer

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Longformer: The Long-Document Transformer

https://github.com/allenai/olmo

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OLMo: Accelerating the Science of Language Models

https://github.com/allenai/olmocr

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olmOCR: Unlocking Trillions of Tokens in PDFs with Vision Language Models

https://github.com/allenai/open-instruct

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Tulu 3: Pushing Frontiers in Open Language Model Post-Training

https://github.com/allenai/reward-bench

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RewardBench: Evaluating Reward Models for Language Modeling

https://github.com/allenai/scibert

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SciBERT: A Pretrained Language Model for Scientific Text

https://github.com/allenai/scidocs

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SPECTER: Document-level Representation Learning using Citation-informed Transformers

https://github.com/allenai/sciriff

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SciRIFF: A Resource to Enhance Language Model Instruction-Following over Scientific Literature

https://github.com/allenai/sdi-detection

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SUPP.AI: Finding Evidence for Supplement-Drug Interactions

https://github.com/allenai/specter

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SPECTER: Document-level Representation Learning using Citation-informed Transformers

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.