https://github.com/Stareru/NeighboringDistributionDivergence
공식 코드Contextualized Semantic Distance between Highly Overlapped Texts
Papers with Code 아카이브
Text Compression
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코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/WUHU-G/RCC_Transformer
공식 코드Recurrent Context Compression: Efficiently Expanding the Context Window of LLM
https://github.com/adithya7/scaling-mds
공식 코드Scaling Multi-Document Event Summarization: Evaluating Compression vs. Full-Text Approaches
https://github.com/alipay/l3tc-leveraging-rwkv-for-learned-lossless-low-complexity-text-compression
공식 코드L3TC: Leveraging RWKV for Learned Lossless Low-Complexity Text Compression
https://github.com/davidcox143/rnn-text-compress
공식 코드Syntactically Informed Text Compression with Recurrent Neural Networks
https://github.com/fazalmittu/finezip
공식 코드FineZip : Pushing the Limits of Large Language Models for Practical Lossless Text Compression
https://github.com/flipz357/npc_gzip_exp
공식 코드https://github.com/kid-22/llm4ir-bias
공식 코드https://github.com/kid-22/source-bias
공식 코드https://github.com/luckybian/isy5001
공식 코드https://github.com/microsoft/LLMLingua
공식 코드LLMLingua-2: Data Distillation for Efficient and Faithful Task-Agnostic Prompt Compression
https://github.com/pchizhov/picky_bpe
공식 코드BPE Gets Picky: Efficient Vocabulary Refinement During Tokenizer Training
https://github.com/swathi-shree-narashiman/alphazip
공식 코드https://github.com/vcskaushik/LLMzip
공식 코드LLMZip: Lossless Text Compression using Large Language Models
https://github.com/znlp/tokalign
공식 코드TokAlign: Efficient Vocabulary Adaptation via Token Alignment
https://github.com/8sukanya8/occav
https://github.com/Stonesjtu/Pytorch-NCE
A Batch Noise Contrastive Estimation Approach for Training Large Vocabulary Language Models
https://github.com/erika-n/GPTzip
LLMZip: Lossless Text Compression using Large Language Models
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.