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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/creatorscan/espnet

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EAT: Enhanced ASR-TTS for Self-supervised Speech Recognition

https://github.com/cristianopatricio/2-step-concept-based-skin-diagnosis

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A Two-Step Concept-Based Approach for Enhanced Interpretability and Trust in Skin Lesion Diagnosis

https://github.com/crowddynamicslab/cevlm

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CEV-LM: Controlled Edit Vector Language Model for Shaping Natural Language Generations

https://github.com/cruiseresearchgroup/MAPLE

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MAPLE: Mobile App Prediction Leveraging Large Language Model Embeddings

https://github.com/cs-giung/lipsum-ft

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Lipsum-FT: Robust Fine-Tuning of Zero-Shot Models Using Random Text Guidance

https://github.com/csbao/kip-privatization

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Keep It Private: Unsupervised Privatization of Online Text

https://github.com/csebuetnlp/banglabert

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BanglaBERT: Language Model Pretraining and Benchmarks for Low-Resource Language Understanding Evaluation in Bangla

https://github.com/csebuetnlp/banglanlg

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BanglaNLG and BanglaT5: Benchmarks and Resources for Evaluating Low-Resource Natural Language Generation in Bangla

https://github.com/cshaitao/sailer

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SAILER: Structure-aware Pre-trained Language Model for Legal Case Retrieval

https://github.com/cshizhe/HM3DAutoVLN

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Learning from Unlabeled 3D Environments for Vision-and-Language Navigation

https://github.com/cstevenson-uva/creAI-gpt3

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Putting GPT-3's Creativity to the (Alternative Uses) Test

https://github.com/cstevenson-uva/kidsarc

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Do Large Language Models Solve ARC Visual Analogies Like People Do?

https://github.com/csu-signal/axomiyaberta

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AxomiyaBERTa: A Phonologically-aware Transformer Model for Assamese

https://github.com/cteant/space

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Generation Meets Verification: Accelerating Large Language Model Inference with Smart Parallel Auto-Correct Decoding

https://github.com/cuhealthybrains/mt-llm

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Large Language Model Can Transcribe Speech in Multi-Talker Scenarios with Versatile Instructions

https://github.com/cultural-csk/candle

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Extracting Cultural Commonsense Knowledge at Scale

https://github.com/curai/curai-research

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DERA: Enhancing Large Language Model Completions with Dialog-Enabled Resolving Agents

https://github.com/cure-lab/guardt2i

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GuardT2I: Defending Text-to-Image Models from Adversarial Prompts

https://github.com/cvi-szu/linly

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Dynamic data sampler for cross-language transfer learning in large language models

https://github.com/cvi-szu/mg-motionllm

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MG-MotionLLM: A Unified Framework for Motion Comprehension and Generation across Multiple Granularities

https://github.com/cvs-health/langfair

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An Actionable Framework for Assessing Bias and Fairness in Large Language Model Use Cases

https://github.com/cwang621/blsp-emo

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BLSP-Emo: Towards Empathetic Large Speech-Language Models

https://github.com/cxcscmu/ed-copilot

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ED-Copilot: Reduce Emergency Department Wait Time with Language Model Diagnostic Assistance

https://github.com/cxcscmu/llm-interviewer

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Understand User Opinions of Large Language Models via LLM-Powered In-the-Moment User Experience Interviews

https://github.com/cxcscmu/longembeddinganalysis

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Dwell in the Beginning: How Language Models Embed Long Documents for Dense Retrieval

https://github.com/cxcscmu/montessori-instruct

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Montessori-Instruct: Generate Influential Training Data Tailored for Student Learning

https://github.com/cxcscmu/ragviz

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RAGViz: Diagnose and Visualize Retrieval-Augmented Generation

https://github.com/cxysteven/scalingproteinlm

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Training Compute-Optimal Protein Language Models

https://github.com/cyberiada/cartography-for-compositionality

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Harnessing Dataset Cartography for Improved Compositional Generalization in Transformers

https://github.com/cylance/perturbed-sequence-model

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PROPS: Probabilistic personalization of black-box sequence models

https://github.com/cyzus/among-agents

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AMONGAGENTS: Evaluating Large Language Models in the Interactive Text-Based Social Deduction Game

https://github.com/czjdsg/lmbot

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LMBot: Distilling Graph Knowledge into Language Model for Graph-less Deployment in Twitter Bot Detection

https://github.com/czlwang/brainbert

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BrainBERT: Self-supervised representation learning for intracranial recordings

https://github.com/czyssrs/Few-Shot-NLG

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Few-Shot NLG with Pre-Trained Language Model

https://github.com/d-ailin/blind-faith-in-text

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Words or Vision: Do Vision-Language Models Have Blind Faith in Text?

https://github.com/d-gurgurov/Injecting-Commonsense-Knowledge-into-LLMs

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Adapting Multilingual LLMs to Low-Resource Languages with Knowledge Graphs via Adapters

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