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코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/b-faye/rpo
공식 코드https://github.com/ba1jun/model-selection-nlp
공식 코드https://github.com/baai-dcai/m3d
공식 코드M3D: Advancing 3D Medical Image Analysis with Multi-Modal Large Language Models
https://github.com/baichuan-inc/baichuan-audio
공식 코드Baichuan-Audio: A Unified Framework for End-to-End Speech Interaction
https://github.com/baiyuqi/agentic-society
공식 코드Agentic Society: Merging skeleton from real world and texture from Large Language Model
https://github.com/baizhige/eegunity
공식 코드EEGUnity: Open-Source Tool in Facilitating Unified EEG Datasets Towards Large-Scale EEG Model
https://github.com/banerjeepragyan/CAFIE
공식 코드All Should Be Equal in the Eyes of Language Models: Counterfactually Aware Fair Text Generation
https://github.com/bangawayoo/mb-lm-watermarking
공식 코드Advancing Beyond Identification: Multi-bit Watermark for Large Language Models
https://github.com/banjiuyufen/Recoverable-Compression
공식 코드Recoverable Compression: A Multimodal Vision Token Recovery Mechanism Guided by Text Information
https://github.com/banjiuyufen/recoverablecompression
공식 코드Recoverable Compression: A Multimodal Vision Token Recovery Mechanism Guided by Text Information
https://github.com/baochi0212/lavy
공식 코드https://github.com/baohaoliao/apiq
공식 코드https://github.com/baozuyi/MLMA
공식 코드Low-Resource Sequence Labeling via Unsupervised Multilingual Contextualized Representations
https://github.com/barfsma/FiSSA
공식 코드https://github.com/bartbussmann/batchtopk
공식 코드https://github.com/bartel-group/matgen_baselines
공식 코드Establishing baselines for generative discovery of inorganic crystals
https://github.com/basf/mamba-tabular
공식 코드On the Efficiency of NLP-Inspired Methods for Tabular Deep Learning
https://github.com/basnetsoyuj/improving-learned-index
공식 코드https://github.com/basusourya/mirostat
공식 코드Mirostat: A Neural Text Decoding Algorithm that Directly Controls Perplexity
https://github.com/batsresearch/ex2
공식 코드https://github.com/baudm/parseq
공식 코드Scene Text Recognition with Permuted Autoregressive Sequence Models
https://github.com/baylor-ai/halludetect
공식 코드Detecting Hallucinations in Large Language Model Generation: A Token Probability Approach
https://github.com/bbsbz/chemmllm
공식 코드https://github.com/bcdnlp/prd
공식 코드PRD: Peer Rank and Discussion Improve Large Language Model based Evaluations
https://github.com/bcgsc/GapPredict
공식 코드GapPredict: A Language Model for Resolving Gaps in Draft Genome Assemblies
https://github.com/bcmi220/erefdp
공식 코드https://github.com/bcwarner/audit-log-lm
공식 코드Autoregressive Language Models For Estimating the Entropy of Epic EHR Audit Logs
https://github.com/bdbc-kg-nlp/ngcse
공식 코드https://github.com/bdusell/nondeterministic-stack-rnn
공식 코드Learning Hierarchical Structures with Differentiable Nondeterministic Stacks
https://github.com/bdusell/stack-attention
공식 코드Stack Attention: Improving the Ability of Transformers to Model Hierarchical Patterns
https://github.com/beam-labs/balm
공식 코드Accurate Prediction of Antibody Function and Structure Using Bio-Inspired Antibody Language Model
https://github.com/bear96/gabm-epidemic
공식 코드https://github.com/beastyz/llm-verified-retrieval
공식 코드LLatrieval: LLM-Verified Retrieval for Verifiable Generation
https://github.com/beccabai/Data-centric_multimodal_LLM
공식 코드A Survey of Multimodal Large Language Model from A Data-centric Perspective
https://github.com/beccabai/multi-agent-data-selection
공식 코드Multi-Agent Collaborative Data Selection for Efficient LLM Pretraining
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.