Papers with Code 아카이브
Language Modelling
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A language model is a model of natural language. Language models are useful for a variety of tasks, including speech recognition, machine translation, natural language generation (generating more human-like text), optical character recognition, route optimization, handwriting recognition, grammar induction, and information retrieval.
Large language models (LLMs), currently their most advanced form, are predominantly based on transformers trained on larger datasets (frequently using words scraped from the public internet). They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.
Source: Wikipedia
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/columbia-nlp-lab/papillon
공식 코드PAPILLON: Privacy Preservation from Internet-based and Local Language Model Ensembles
https://github.com/comind-ml/comind
공식 코드Towards Community-Driven Agents for Machine Learning Engineering
https://github.com/comp-int-hum/orthography-embedding-clustering
공식 코드Examining Language Modeling Assumptions Using an Annotated Literary Dialect Corpus
https://github.com/comp1511unsw/dcc
공식 코드Dcc --help: Generating Context-Aware Compiler Error Explanations with Large Language Models
https://github.com/compai-lab/miccai-2024-junli
공식 코드Language Models Meet Anomaly Detection for Better Interpretability and Generalizability
https://github.com/composable-models/llm_multiagent_debate
공식 코드Improving Factuality and Reasoning in Language Models through Multiagent Debate
https://github.com/computationalmedia/semstyle
공식 코드SemStyle: Learning to Generate Stylised Image Captions using Unaligned Text
https://github.com/concyclics/logquantkv
공식 코드LogQuant: Log-Distributed 2-Bit Quantization of KV Cache with Superior Accuracy Preservation
https://github.com/confirm-solutions/dreamy
공식 코드https://github.com/conghan0808/deop
공식 코드Open-Vocabulary Semantic Segmentation with Decoupled One-Pass Network
https://github.com/constantjxyz/promptlink
공식 코드PromptLink: Leveraging Large Language Models for Cross-Source Biomedical Concept Linking
https://github.com/contextualai/gritlm
공식 코드https://github.com/contextualai/lens
공식 코드Towards Language Models That Can See: Computer Vision Through the LENS of Natural Language
https://github.com/controllability/jailbreak-evaluation
공식 코드https://github.com/conveyor-sys/conveyor
공식 코드Conveyor: Efficient Tool-aware LLM Serving with Tool Partial Execution
https://github.com/cooelf/SemBERT
공식 코드https://github.com/cooelf/instancereg
공식 코드Instance Regularization for Discriminative Language Model Pre-training
https://github.com/cookielee77/CLARE
공식 코드https://github.com/cooperative-ia/cooperativegpt
공식 코드https://github.com/cooperleong00/toxificationreversal
공식 코드https://github.com/copenlu/X-MAML
공식 코드https://github.com/copenlu/cite-worth
공식 코드CiteWorth: Cite-Worthiness Detection for Improved Scientific Document Understanding
https://github.com/copenlu/context-utilisation-for-rag
공식 코드A Reality Check on Context Utilisation for Retrieval-Augmented Generation
https://github.com/copenlu/fever-adversarial-attacks
공식 코드Generating Label Cohesive and Well-Formed Adversarial Claims
https://github.com/copenlu/llm-gender-bias-polit
공식 코드Quantifying Gender Bias Towards Politicians in Cross-Lingual Language Models
https://github.com/cordercorder/quant_eval
공식 코드A Comprehensive Evaluation of Quantization Strategies for Large Language Models
https://github.com/core-robotics-lab/cross_loss_influence_functions
공식 코드Cross-Loss Influence Functions to Explain Deep Network Representations
https://github.com/corl-team/rebased
공식 코드Linear Transformers with Learnable Kernel Functions are Better In-Context Models
https://github.com/cornell-zhang/allo
공식 코드Understanding the Potential of FPGA-Based Spatial Acceleration for Large Language Model Inference
https://github.com/cornell-zhang/dnn-quant-ocs
공식 코드Improving Neural Network Quantization without Retraining using Outlier Channel Splitting
https://github.com/cosmoquester/memoria
공식 코드Memoria: Resolving Fateful Forgetting Problem through Human-Inspired Memory Architecture
https://github.com/coswindywang/HdLM
공식 코드Making Language Model a Hierarchical Classifier and Generator
https://github.com/coteries/cedille-ai
공식 코드https://github.com/cpcp1998/permuteformer
공식 코드PermuteFormer: Efficient Relative Position Encoding for Long Sequences
https://github.com/cpjku/performance_embeddings_fire23
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.