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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/nyu-dl/non-monotonic-self-terminating-lm

공식 코드

A Non-monotonic Self-terminating Language Model

https://github.com/nyu-mll/PRPN-Analysis

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Grammar Induction with Neural Language Models: An Unusual Replication

https://github.com/nyu-mll/wsc-formalizations

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Precise Task Formalization Matters in Winograd Schema Evaluations

https://github.com/nyuolab/MedMobile

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MedMobile: A mobile-sized language model with expert-level clinical capabilities

https://github.com/nyuolab/model_sensitivity

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Language Model Classifier Aligns Better with Physician Word Sensitivity than XGBoost on Readmission Prediction

https://github.com/nyushcs/moleculargpt

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MolecularGPT: Open Large Language Model (LLM) for Few-Shot Molecular Property Prediction

https://github.com/nyushcs/uniglm

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UniGLM: Training One Unified Language Model for Text-Attributed Graph Embedding

https://github.com/oaklight/mango

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MANGO: A Benchmark for Evaluating Mapping and Navigation Abilities of Large Language Models

https://github.com/oashua/mathagent

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Modeling Complex Mathematical Reasoning via Large Language Model based MathAgent

https://github.com/oddqueue/e-dpo

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KL Penalty Control via Perturbation for Direct Preference Optimization

https://github.com/ofa-sys/expertllama

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ExpertPrompting: Instructing Large Language Models to be Distinguished Experts

https://github.com/ofa-sys/ofa

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OFA: Unifying Architectures, Tasks, and Modalities Through a Simple Sequence-to-Sequence Learning Framework

https://github.com/ofirpress/PartialShuffle

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Partially Shuffling the Training Data to Improve Language Models

https://github.com/ofirpress/shortformer

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Shortformer: Better Language Modeling using Shorter Inputs

https://github.com/ohadrubin/epr

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Learning To Retrieve Prompts for In-Context Learning

https://github.com/ohadrubin/rpt

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Retrieval-Pretrained Transformer: Long-range Language Modeling with Self-retrieval

https://github.com/ola-omni/ola

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Ola: Pushing the Frontiers of Omni-Modal Language Model

https://github.com/olivialyt/clip-pcqa

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CLIP-PCQA: Exploring Subjective-Aligned Vision-Language Modeling for Point Cloud Quality Assessment

https://github.com/om-ai-lab/rs5m

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RS5M and GeoRSCLIP: A Large Scale Vision-Language Dataset and A Large Vision-Language Model for Remote Sensing

https://github.com/om-ai-lab/vlm-r1

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VLM-R1: A Stable and Generalizable R1-style Large Vision-Language Model

https://github.com/omarmomen14/structformer-babylm

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Increasing The Performance of Cognitively Inspired Data-Efficient Language Models via Implicit Structure Building

https://github.com/omron-sinicx/whereistheanswer

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Where is the answer? Investigating Positional Bias in Language Model Knowledge Extraction

https://github.com/oniani/covid-19-chatbot

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A Qualitative Evaluation of Language Models on Automatic Question-Answering for COVID-19

https://github.com/oo222bs/PVAE-BERT

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Language Model-Based Paired Variational Autoencoders for Robotic Language Learning

https://github.com/oodbag/rise

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A Sentence Speaks a Thousand Images: Domain Generalization through Distilling CLIP with Language Guidance

https://github.com/oomics/dnazen

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DNAZEN: Enhanced Gene Sequence Representations via Mixed Granularities of Coding Units

https://github.com/open-compass/CriticBench

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CriticEval: Evaluating Large Language Model as Critic

https://github.com/open-compass/criticeval

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CriticEval: Evaluating Large Language Model as Critic

https://github.com/open-mmlab/multimodal-gpt

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MultiModal-GPT: A Vision and Language Model for Dialogue with Humans

https://github.com/openai/LHOPT

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A Generalizable Approach to Learning Optimizers

https://github.com/openai/gpt-3

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Language Models are Few-Shot Learners

https://github.com/openai/human-eval

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Evaluating Large Language Models Trained on Code

https://github.com/openai/lean-gym

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Formal Mathematics Statement Curriculum Learning

https://github.com/openai/safety-rbr-code-and-data

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Rule Based Rewards for Language Model Safety

https://github.com/openai/whisper

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Adapting Large Language Model with Speech for Fully Formatted End-to-End Speech Recognition

https://github.com/openaudiolab/llast

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LLaST: Improved End-to-end Speech Translation System Leveraged by Large Language Models

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.