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Continual Pretraining

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하위 태스크: 0
평가표: 3
보존된 평가 기록: 3

코드

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

https://github.com/01-ai/yi

공식 코드

Yi: Open Foundation Models by 01.AI

https://github.com/UIC-Liu-Lab/ContinualLM

공식 코드

Continual Pre-training of Language Models

https://github.com/X-F-Cui/Fictitious_Fact_Watermarks

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Robust Data Watermarking in Language Models by Injecting Fictitious Knowledge

https://github.com/andreacossu/continual-pretraining-nlp-vision

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Continual Pre-Training Mitigates Forgetting in Language and Vision

https://github.com/apple/ml-tic-lm

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TiC-LM: A Web-Scale Benchmark for Time-Continual LLM Pretraining

https://github.com/cisnlp/langsamp

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LangSAMP: Language-Script Aware Multilingual Pretraining

https://github.com/eques-inc/pharma-llm-eval

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A Japanese Language Model and Three New Evaluation Benchmarks for Pharmaceutical NLP

https://github.com/explainableml/fomo_in_flux

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A Practitioner's Guide to Continual Multimodal Pretraining

https://github.com/franxyao/long-context-data-engineering

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Data Engineering for Scaling Language Models to 128K Context

https://github.com/hiyouga/llama-factory

공식 코드

Autonomous Data Selection with Zero-shot Generative Classifiers for Mathematical Texts

https://github.com/ibm/tslm-discourse-markers

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Fortunately, Discourse Markers Can Enhance Language Models for Sentiment Analysis

https://github.com/ink-usc/entity-robustness

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On the Robustness of Reading Comprehension Models to Entity Renaming

https://github.com/kevinlight831/ctp

공식 코드

CTP: Towards Vision-Language Continual Pretraining via Compatible Momentum Contrast and Topology Preservation

https://github.com/kongds/mora

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MoRA: High-Rank Updating for Parameter-Efficient Fine-Tuning

https://github.com/leiboliu/hilat

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Hierarchical Label-wise Attention Transformer Model for Explainable ICD Coding

https://github.com/meshidenn/cai

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Unsupervised Domain Adaptation for Sparse Retrieval by Filling Vocabulary and Word Frequency Gaps

https://github.com/microsoft/rho

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Rho-1: Not All Tokens Are What You Need

https://github.com/mmendiet/gfm

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Towards Geospatial Foundation Models via Continual Pretraining

https://github.com/nailashfrni/mcq-leakage-detection-code

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Simulating Training Data Leakage in Multiple-Choice Benchmarks for LLM Evaluation

https://github.com/nightdessert/retrieval_head

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Retrieval Head Mechanistically Explains Long-Context Factuality

https://github.com/plrbear/pecop

공식 코드

PECoP: Parameter Efficient Continual Pretraining for Action Quality Assessment

https://github.com/pluslabnlp/econet

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ECONET: Effective Continual Pretraining of Language Models for Event Temporal Reasoning

https://github.com/qianlima-lab/awesome-lifelong-learning-methods-for-llm

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Towards Lifelong Learning of Large Language Models: A Survey

https://github.com/salesforceairesearch/findap

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Demystifying Domain-adaptive Post-training for Financial LLMs

https://github.com/uic-liu-lab/cpt

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Continual Training of Language Models for Few-Shot Learning

https://github.com/vano1205/efficientcl

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Efficient Contrastive Learning via Novel Data Augmentation and Curriculum Learning

https://github.com/wclsn/Alchemy

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Alchemy: Amplifying Theorem-Proving Capability through Symbolic Mutation

https://github.com/yanyongyu/af-adapter

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AF Adapter: Continual Pretraining for Building Chinese Biomedical Language Model

https://github.com/yifanzhang-pro/automathtext

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Autonomous Data Selection with Zero-shot Generative Classifiers for Mathematical Texts

https://github.com/zhu-xlab/softcon

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Multi-Label Guided Soft Contrastive Learning for Efficient Earth Observation Pretraining

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