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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/gokulkarthik/detr

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An Empirical Study Of Self-supervised Learning Approaches For Object Detection With Transformers

https://github.com/goliaro/specinfer-ae

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SpecInfer: Accelerating Generative Large Language Model Serving with Tree-based Speculative Inference and Verification

https://github.com/google-deepmind/asyncdiloco

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Asynchronous Local-SGD Training for Language Modeling

https://github.com/google-deepmind/language_modeling_is_compression

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Language Modeling Is Compression

https://github.com/google-deepmind/librispeech-long

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Long-Form Speech Generation with Spoken Language Models

https://github.com/google-deepmind/md4

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Simplified and Generalized Masked Diffusion for Discrete Data

https://github.com/google-deepmind/reward-ensembles

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Helping or Herding? Reward Model Ensembles Mitigate but do not Eliminate Reward Hacking

https://github.com/google-health/records-research

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Improved Hierarchical Patient Classification with Language Model Pretraining over Clinical Notes

https://github.com/google-research-datasets/KELM-corpus

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Knowledge Graph Based Synthetic Corpus Generation for Knowledge-Enhanced Language Model Pre-training

https://github.com/google-research-datasets/ToTTo

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Text-to-Text Pre-Training for Data-to-Text Tasks

https://github.com/google-research-datasets/altentities

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Resolving Indirect Referring Expressions for Entity Selection

https://github.com/google-research-datasets/dakshina

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Processing South Asian Languages Written in the Latin Script: the Dakshina Dataset

https://github.com/google-research-datasets/gsm-ic

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Large Language Models Can Be Easily Distracted by Irrelevant Context

https://github.com/google-research-datasets/screen_annotation

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ScreenAI: A Vision-Language Model for UI and Infographics Understanding

https://github.com/google-research-datasets/screen_qa

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ScreenAI: A Vision-Language Model for UI and Infographics Understanding

https://github.com/google-research-datasets/swim-ir

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Leveraging LLMs for Synthesizing Training Data Across Many Languages in Multilingual Dense Retrieval

https://github.com/google-research-datasets/xsum_hallucination_annotations

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On Faithfulness and Factuality in Abstractive Summarization

https://github.com/google-research/big_vision

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UViM: A Unified Modeling Approach for Vision with Learned Guiding Codes

https://github.com/google-research/bleurt

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Learning Compact Metrics for MT

https://github.com/google-research/cascades

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Language Model Cascades

https://github.com/google-research/causallm_icl

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CausalLM is not optimal for in-context learning

https://github.com/google-research/dataset_grouper

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Towards Federated Foundation Models: Scalable Dataset Pipelines for Group-Structured Learning

https://github.com/google-research/deduplicate-text-datasets

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Deduplicating Training Data Makes Language Models Better

https://github.com/google-research/electra

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ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators

https://github.com/google-research/federated

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Multi-Epoch Matrix Factorization Mechanisms for Private Machine Learning

https://github.com/google-research/flan

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Finetuned Language Models Are Zero-Shot Learners

https://github.com/google-research/google-research

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Toward Better Storylines with Sentence-Level Language Models

https://github.com/google-research/google-research/tree/master/cfq_pt_vs_sa

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Compositional Generalization in Semantic Parsing: Pre-training vs. Specialized Architectures

https://github.com/google-research/google-research/tree/master/felix

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Felix: Flexible Text Editing Through Tagging and Insertion

https://github.com/google-research/google-research/tree/master/perso_arabic_norm

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Graphemic Normalization of the Perso-Arabic Script

https://github.com/google-research/google-research/tree/master/primer

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Primer: Searching for Efficient Transformers for Language Modeling

https://github.com/google-research/google-research/tree/master/scalable_shampoo/jax

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Scalable Second Order Optimization for Deep Learning

https://github.com/google-research/google-research/tree/master/sm3

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Memory-Efficient Adaptive Optimization

https://github.com/google-research/google-research/tree/master/smith

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Beyond 512 Tokens: Siamese Multi-depth Transformer-based Hierarchical Encoder for Long-Form Document Matching

https://github.com/google-research/google-research/tree/master/structformer

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StructFormer: Joint Unsupervised Induction of Dependency and Constituency Structure from Masked Language Modeling

https://github.com/google-research/google-research/tree/master/t5_closed_book_qa

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How Much Knowledge Can You Pack Into the Parameters of a Language Model?

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