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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/ubc-nlp/mos

공식 코드

Mixture-of-Supernets: Improving Weight-Sharing Supernet Training with Architecture-Routed Mixture-of-Experts

https://github.com/ubiquitouslearning/phonelm

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PhoneLM:an Efficient and Capable Small Language Model Family through Principled Pre-training

https://github.com/ubiquitouslearning/slm_survey

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Small Language Models: Survey, Measurements, and Insights

https://github.com/ucasligang/semmae

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SemMAE: Semantic-Guided Masking for Learning Masked Autoencoders

https://github.com/ucasyjz/VIP

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VIP: Versatile Image Outpainting Empowered by Multimodal Large Language Model

https://github.com/ucfnlp/control-over-copying

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Controlling the Amount of Verbatim Copying in Abstractive Summarization

https://github.com/uchi-jcl/cachegen

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CacheGen: KV Cache Compression and Streaming for Fast Large Language Model Serving

https://github.com/uchidalab/vision-conformer

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Vision Conformer: Incorporating Convolutions into Vision Transformer Layers

https://github.com/ucla-trustworthy-ai-lab/autodiffusion

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AutoDiff: combining Auto-encoder and Diffusion model for tabular data synthesizing

https://github.com/uclaml/sppo

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Self-Play Preference Optimization for Language Model Alignment

https://github.com/uclanlp/NamedEntityLanguageModel

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Building Language Models for Text with Named Entities

https://github.com/uclanlp/diverse-factual

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The Factuality Tax of Diversity-Intervened Text-to-Image Generation: Benchmark and Fact-Augmented Intervention

https://github.com/uclanlp/socialbias-dataset-construction-biases

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The Tail Wagging the Dog: Dataset Construction Biases of Social Bias Benchmarks

https://github.com/uclmr/adversarial-nli

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Adversarially Regularising Neural NLI Models to Integrate Logical Background Knowledge

https://github.com/uclmr/pycodesuggest

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Learning Python Code Suggestion with a Sparse Pointer Network

https://github.com/ucsd-atp/propl

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Learn from Failure: Fine-Tuning LLMs with Trial-and-Error Data for Intuitionistic Propositional Logic Proving

https://github.com/ucsd-language-and-cognition-lab/nlm-fb

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Do Large Language Models know what humans know?

https://github.com/uditarora/ood-text-emnlp

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Types of Out-of-Distribution Texts and How to Detect Them

https://github.com/uds-lsv/hidden-space-adapters

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The Hidden Space of Transformer Language Adapters

https://github.com/uds-lsv/stereokg

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StereoKG: Data-Driven Knowledge Graph Construction for Cultural Knowledge and Stereotypes

https://github.com/uestc-gqj/tkre

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Bridging Generative and Discriminative Learning: Few-Shot Relation Extraction via Two-Stage Knowledge-Guided Pre-training

https://github.com/uf-hobi-informatics-lab/gatortrongpt

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A Study of Generative Large Language Model for Medical Research and Healthcare

https://github.com/ufal/augpt

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AuGPT: Auxiliary Tasks and Data Augmentation for End-To-End Dialogue with Pre-Trained Language Models

https://github.com/ufal/crac2023-corpipe

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ÚFAL CorPipe at CRAC 2023: Larger Context Improves Multilingual Coreference Resolution

https://github.com/ufal/multilexnorm2021

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ÚFAL at MultiLexNorm 2021: Improving Multilingual Lexical Normalization by Fine-tuning ByT5

https://github.com/uic-liu-lab/dga

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Adapting a Language Model While Preserving its General Knowledge

https://github.com/uicdice/imm-logistic-regression

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Induced Model Matching: How Restricted Models Can Help Larger Ones

https://github.com/uiuc-kang-lab/adaptiveattackagent

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Adaptive Attacks Break Defenses Against Indirect Prompt Injection Attacks on LLM Agents

https://github.com/uiuc-kang-lab/cve-bench

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CVE-Bench: A Benchmark for AI Agents' Ability to Exploit Real-World Web Application Vulnerabilities

https://github.com/uiuc-kang-lab/injecagent

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InjecAgent: Benchmarking Indirect Prompt Injections in Tool-Integrated Large Language Model Agents

https://github.com/uiuc-sst/asr24

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Fast transcription of speech in low-resource languages

https://github.com/ukplab/arxiv2022-context-injection-stance

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Contextual information integration for stance detection via cross-attention

https://github.com/ukplab/arxiv2025-misleading-visualizations

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Protecting multimodal large language models against misleading visualizations

https://github.com/ukplab/codeclarqa

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Python Code Generation by Asking Clarification Questions

https://github.com/ukplab/emnlp2021-prompt-ft-heuristics

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Avoiding Inference Heuristics in Few-shot Prompt-based Finetuning

https://github.com/ukplab/iclr2025-psa

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Differentially Private Steering for Large Language Model Alignment

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