https://github.com/sakanaai/sudoku-bench
공식 코드Sudoku-Bench: Evaluating creative reasoning with Sudoku variants
Papers with Code 아카이브
Memorization
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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/sbdzdz/disco
공식 코드Infinite dSprites for Disentangled Continual Learning: Separating Memory Edits from Generalization
https://github.com/sbdzdz/idsprites
공식 코드Infinite dSprites for Disentangled Continual Learning: Separating Memory Edits from Generalization
https://github.com/scalable-model-editing/unified-model-editing
공식 코드https://github.com/schwartz-lab-nlp/data_contamination
공식 코드https://github.com/schwartz-lab-nlp/model_size_and_gender_bias
공식 코드Fewer Errors, but More Stereotypes? The Effect of Model Size on Gender Bias
https://github.com/sci-assess/sciassess
공식 코드SciAssess: Benchmarking LLM Proficiency in Scientific Literature Analysis
https://github.com/scottdyt/sgdl
공식 코드https://github.com/seclab-yonsei/amplifying-exposure
공식 코드Amplifying Training Data Exposure through Fine-Tuning with Pseudo-Labeled Memberships
https://github.com/selvamsriram/panorama-datagen
공식 코드PANORAMA: A synthetic PII-laced dataset for studying sensitive data memorization in LLMs
https://github.com/sflippl/compositional-generalization
공식 코드When does compositional structure yield compositional generalization? A kernel theory
https://github.com/shahriargolchin/dcq
공식 코드Data Contamination Quiz: A Tool to Detect and Estimate Contamination in Large Language Models
https://github.com/shangdatalab/deep-contam
공식 코드https://github.com/sharonlevy/Conspiracy-Theory-Memorization
공식 코드Investigating Memorization of Conspiracy Theories in Text Generation
https://github.com/shash42/evaluating-inexact-unlearning
공식 코드Towards Adversarial Evaluations for Inexact Machine Unlearning
https://github.com/shengliu66/ELR
공식 코드Early-Learning Regularization Prevents Memorization of Noisy Labels
https://github.com/shi-labs/forget-me-not
공식 코드Forget-Me-Not: Learning to Forget in Text-to-Image Diffusion Models
https://github.com/simone-bombari/spurious-features-memorization
공식 코드How Spurious Features Are Memorized: Precise Analysis for Random and NTK Features
https://github.com/sisinflab/llm-memoryinspector
공식 코드Do LLMs Memorize Recommendation Datasets? A Preliminary Study on MovieLens-1M
https://github.com/sixuli/ddpm_and_kde
공식 코드https://github.com/siyandong/src
공식 코드Visual Localization via Few-Shot Scene Region Classification
https://github.com/smahdavi4/attention-memorization
공식 코드Memorization Capacity of Multi-Head Attention in Transformers
https://github.com/smorad/graph-conv-memory
공식 코드https://github.com/snucml/largelossmatters
공식 코드Large Loss Matters in Weakly Supervised Multi-Label Classification
https://github.com/sobhan-moosavi/DCRNN
공식 코드https://github.com/somepago/dcr
공식 코드https://github.com/spoc-group/dataset-distillation-memorization
공식 코드Dataset distillation for memorized data: Soft labels can leak held-out teacher knowledge
https://github.com/sprintml/memorizationssl
공식 코드Memorization in Self-Supervised Learning Improves Downstream Generalization
https://github.com/ssg-research/sok-unintended-interactions
공식 코드SoK: Unintended Interactions among Machine Learning Defenses and Risks
https://github.com/stephen-smj/llamacare
공식 코드LlamaCare: A Large Medical Language Model for Enhancing Healthcare Knowledge Sharing
https://github.com/streethagore/forwardlocalgradient
공식 코드https://github.com/strong-ai-lab/logical-and-abstract-reasoning
공식 코드https://github.com/swing-research/universal-embeddings
공식 코드https://github.com/taewonpark/DAM
공식 코드Distributed Associative Memory Network with Memory Refreshing Loss
https://github.com/takashiishida/flooding
공식 코드Do We Need Zero Training Loss After Achieving Zero Training Error?
https://github.com/technion-cs-nlp/llm-arithmetic-heuristics
공식 코드Arithmetic Without Algorithms: Language Models Solve Math With a Bag of Heuristics
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.