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Language Modeling
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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/sakanaai/ai-scientist
공식 코드The AI Scientist: Towards Fully Automated Open-Ended Scientific Discovery
https://github.com/sakharamg/kitlm
공식 코드KITLM: Domain-Specific Knowledge InTegration into Language Models for Question Answering
https://github.com/salesforce/CodeGen
공식 코드CodeGen: An Open Large Language Model for Code with Multi-Turn Program Synthesis
https://github.com/salesforce/DialogStudio
공식 코드DialogStudio: Towards Richest and Most Diverse Unified Dataset Collection for Conversational AI
https://github.com/salesforce/awd-lstm-lm
공식 코드https://github.com/salesforce/codegen2
공식 코드CodeGen2: Lessons for Training LLMs on Programming and Natural Languages
https://github.com/salesforce/ebm_calibration_nlu
공식 코드Joint Energy-based Model Training for Better Calibrated Natural Language Understanding Models
https://github.com/salesforce/fewshot_absa
공식 코드A Generative Language Model for Few-shot Aspect-Based Sentiment Analysis
https://github.com/salesforce/jaxformer
공식 코드CodeGen: An Open Large Language Model for Code with Multi-Turn Program Synthesis
https://github.com/salesforce/lavis
공식 코드https://github.com/salesforce/qvr-simpledlm
공식 코드Value Retrieval with Arbitrary Queries for Form-like Documents
https://github.com/salesforce/simpletod
공식 코드https://github.com/salesforceairesearch/answer-engine-eval
공식 코드Search Engines in an AI Era: The False Promise of Factual and Verifiable Source-Cited Responses
https://github.com/salesforceairesearch/faitheval
공식 코드https://github.com/salt-nlp/chain-of-thought-bias
공식 코드On Second Thought, Let's Not Think Step by Step! Bias and Toxicity in Zero-Shot Reasoning
https://github.com/salt-nlp/culturebank
공식 코드https://github.com/salt-nlp/dylan
공식 코드A Dynamic LLM-Powered Agent Network for Task-Oriented Agent Collaboration
https://github.com/saltychtao/prealign
공식 코드PreAlign: Boosting Cross-Lingual Transfer by Early Establishment of Multilingual Alignment
https://github.com/salute-developers/gigaam
공식 코드GigaAM: Efficient Self-Supervised Learner for Speech Recognition
https://github.com/samholt/DiscoPOP
공식 코드Discovering Preference Optimization Algorithms with and for Large Language Models
https://github.com/samholt/l2mac
공식 코드L2MAC: Large Language Model Automatic Computer for Extensive Code Generation
https://github.com/samuelarnesen/nyu-debate-modeling
공식 코드Training Language Models to Win Debates with Self-Play Improves Judge Accuracy
https://github.com/samuelsimko/crl-llm-defense
공식 코드Improving Large Language Model Safety with Contrastive Representation Learning
https://github.com/samuelstevens/bert-edits
공식 코드An Investigation of Language Model Interpretability via Sentence Editing
https://github.com/samuelstevens/sentence-editing-interpretability
공식 코드An Investigation of Language Model Interpretability via Sentence Editing
https://github.com/samuki/reverse-engineering-the-reader
공식 코드https://github.com/sanchaitahazra/t4text
공식 코드To Tell The Truth: Language of Deception and Language Models
https://github.com/sander102907/autoencoder_program_synthesis
공식 코드https://github.com/sangmichaelxie/doremi
공식 코드DoReMi: Optimizing Data Mixtures Speeds Up Language Model Pretraining
https://github.com/sangwu99/simplot
공식 코드SIMPLOT: Enhancing Chart Question Answering by Distilling Essentials
https://github.com/sannim3/predicitons_with_explanations
공식 코드https://github.com/sap-samples/emnlp2021-contrastive-refinement
공식 코드Towards Zero-shot Commonsense Reasoning with Self-supervised Refinement of Language Models
https://github.com/saprmarks/feature-circuits
공식 코드Sparse Feature Circuits: Discovering and Editing Interpretable Causal Graphs in Language Models
https://github.com/saprmarks/geometry-of-truth
공식 코드https://github.com/saptarshi059/generalization-hypothesis
공식 코드Exploring Language Model Generalization in Low-Resource Extractive QA
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.