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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/mandar-sharma/laying-anchors

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Laying Anchors: Semantically Priming Numerals in Language Modeling

https://github.com/mandar-sharma/overcomingbarriers

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Overcoming Barriers to Skill Injection in Language Modeling: Case Study in Arithmetic

https://github.com/mandyyyyii/scibench

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SciBench: Evaluating College-Level Scientific Problem-Solving Abilities of Large Language Models

https://github.com/manga-uofa/prompt-edit

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Prompt-Based Editing for Text Style Transfer

https://github.com/manon-reusens/native_en_bias

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Native Design Bias: Studying the Impact of English Nativeness on Language Model Performance

https://github.com/manueleberhardinger/ec-rl

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Learning of Generalizable and Interpretable Knowledge in Grid-Based Reinforcement Learning Environments

https://github.com/manuelfay/llm-data-hub

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CroissantLLM: A Truly Bilingual French-English Language Model

https://github.com/maple-research-lab/slot

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SLOT: Sample-specific Language Model Optimization at Test-time

https://github.com/marcelbinz/Llama-3.1-Centaur-70B

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Centaur: a foundation model of human cognition

https://github.com/marcoguerini/CONAN

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Human-in-the-Loop for Data Collection: a Multi-Target Counter Narrative Dataset to Fight Online Hate Speech

https://github.com/marcopodda/fragment-based-dgm

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A Deep Generative Model for Fragment-Based Molecule Generation

https://github.com/marekrei/sequence-labeler

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Semi-supervised Multitask Learning for Sequence Labeling

https://github.com/marian-nmt/marian

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The Highs and Lows of Simple Lexical Domain Adaptation Approaches for Neural Machine Translation

https://github.com/mariavale/contextual_inform

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Measuring Contextual Informativeness in Child-Directed Text

https://github.com/maris205/dnahl

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DNAHLM -- DNA sequence and Human Language mixed large language Model

https://github.com/maroo-sky/fsd

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Feature Structure Distillation with Centered Kernel Alignment in BERT Transferring

https://github.com/marthaflinderslewis/clip-binding

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Does CLIP Bind Concepts? Probing Compositionality in Large Image Models

https://github.com/martiansideofthemoon/rankgen

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RankGen: Improving Text Generation with Large Ranking Models

https://github.com/mas-takayama/LLM-and-SCD/tree/main/codes

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Integrating Large Language Models in Causal Discovery: A Statistical Causal Approach

https://github.com/mas-takayama/llm-and-scd

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Integrating Large Language Models in Causal Discovery: A Statistical Causal Approach

https://github.com/masoud-monajati/BERTHop

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BERTHop: An Effective Vision-and-Language Model for Chest X-ray Disease Diagnosis

https://github.com/massabaali7/colmbo

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CoLMbo: Speaker Language Model for Descriptive Profiling

https://github.com/matchten/lora-models-for-saes

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Low-Rank Adapting Models for Sparse Autoencoders

https://github.com/matejklemen/slonspell

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Neural spell-checker: Beyond words with synthetic data generation

https://github.com/matejulcar/slot5-tools

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Sequence to sequence pretraining for a less-resourced Slovenian language

https://github.com/mateuszbaransanok/e-agitation

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Electoral Agitation Data Set: The Use Case of the Polish Election

https://github.com/mateuszgabor/pcdeq

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Positive concave deep equilibrium models

https://github.com/matnlp/smedbert

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SMedBERT: A Knowledge-Enhanced Pre-trained Language Model with Structured Semantics for Medical Text Mining

https://github.com/matthewcym/sflm

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Revisiting Self-Training for Few-Shot Learning of Language Model

https://github.com/matthewcym/voicebench

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Advancing Speech Language Models by Scaling Supervised Fine-Tuning with Over 60,000 Hours of Synthetic Speech Dialogue Data

https://github.com/matthewmatero/melt

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MeLT: Message-Level Transformer with Masked Document Representations as Pre-Training for Stance Detection

https://github.com/maudzung/SFA3D

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Generalized Robot 3D Vision-Language Model with Fast Rendering and Pre-Training Vision-Language Alignment

https://github.com/mawdoo3/Multi-dialect-Arabic-BERT

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Multi-Dialect Arabic BERT for Country-Level Dialect Identification

https://github.com/maximecb/gym-miniworld

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Pre-trained Word Embeddings for Goal-conditional Transfer Learning in Reinforcement Learning

https://github.com/maximedb/full

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Open-Domain Dialog Evaluation using Follow-Ups Likelihood

https://github.com/maxischuh/twinbooster

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TwinBooster: Synergising Large Language Models with Barlow Twins and Gradient Boosting for Enhanced Molecular Property Prediction

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