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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/minnesotanlp/taddex
공식 코드https://github.com/mismayil/creative-story-gen
공식 코드Evaluating Creative Short Story Generation in Humans and Large Language Models
https://github.com/misonuma/recursum
공식 코드https://github.com/misonuma/strsum
공식 코드https://github.com/mitjanikolaus/compositional-image-captioning
공식 코드https://github.com/mitre-attack/tram
공식 코드Cyber-Attack Technique Classification Using Two-Stage Trained Large Language Models
https://github.com/miulab/factalign
공식 코드FactAlign: Long-form Factuality Alignment of Large Language Models
https://github.com/miyyer/scpn
공식 코드Adversarial Example Generation with Syntactically Controlled Paraphrase Networks
https://github.com/mkeet/CLaRO
공식 코드CLaRO: a Data-driven CNL for Specifying Competency Questions
https://github.com/mlai-bonn/simple-german-corpus
공식 코드https://github.com/mlepori1/Picking_BERTs_Brain
공식 코드https://github.com/mlepori1/circuit_probing
공식 코드Uncovering Intermediate Variables in Transformers using Circuit Probing
https://github.com/modelscope/AdaSeq/tree/master/examples/RaNER
공식 코드Improving Named Entity Recognition by External Context Retrieving and Cooperative Learning
https://github.com/modelscope/adaseq
공식 코드Improving Named Entity Recognition by External Context Retrieving and Cooperative Learning
https://github.com/mohna0310/zssc
공식 코드Zero-shot Approach to Overcome Perturbation Sensitivity of Prompts
https://github.com/molichenxi/haesum
공식 코드Hierarchical Attention Graph for Scientific Document Summarization in Global and Local Level
https://github.com/moranyanuka/icc_code
공식 코드ICC: Quantifying Image Caption Concreteness for Multimodal Dataset Curation
https://github.com/morenolaquatra/flanec
공식 코드https://github.com/morningmoni/FAR
공식 코드https://github.com/morphology-probing/morph-call
공식 코드Morph Call: Probing Morphosyntactic Content of Multilingual Transformers
https://github.com/motasemajlouni/cescore
공식 코드CEScore: Simple and Efficient Confidence Estimation Model for Evaluating Split and Rephrase
https://github.com/mounicam/bisect
공식 코드BiSECT: Learning to Split and Rephrase Sentences with Bitexts
https://github.com/mpoemsl/circe
공식 코드CIRCE at SemEval-2020 Task 1: Ensembling Context-Free and Context-Dependent Word Representations
https://github.com/mrjleo/ranking-models
공식 코드Exploiting Sentence-Level Representations for Passage Ranking
https://github.com/mrlamm/textual-analogy-parsing
공식 코드Textual Analogy Parsing: What's Shared and What's Compared among Analogous Facts
https://github.com/mrlibw/word-level-story-visualization
공식 코드https://github.com/mrpeerat/sct
공식 코드An Efficient Self-Supervised Cross-View Training For Sentence Embedding
https://github.com/msadat3/mscinli
공식 코드MSciNLI: A Diverse Benchmark for Scientific Natural Language Inference
https://github.com/msadat3/scinli
공식 코드SciNLI: A Corpus for Natural Language Inference on Scientific Text
https://github.com/msadat3/ssl_for_nli
공식 코드https://github.com/mshaikh2/joimter_mlmi_2021
공식 코드Improving Joint Learning of Chest X-Ray and Radiology Report by Word Region Alignment
https://github.com/msmadi/Arabic-Dataset-for-Commonsense-Validationion
공식 코드Is this sentence valid? An Arabic Dataset for Commonsense Validation
https://github.com/mstrise/dep2label
공식 코드https://github.com/mswellhao/PacSum
공식 코드Sentence Centrality Revisited for Unsupervised Summarization
https://github.com/mt-upc/SegAugment
공식 코드SegAugment: Maximizing the Utility of Speech Translation Data with Segmentation-based Augmentations
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.