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In-Context Learning

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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/google-deepmind/emergent_in_context_learning

Data Distributional Properties Drive Emergent In-Context Learning in Transformers

https://github.com/google-research/chain-of-table

Chain-of-Table: Evolving Tables in the Reasoning Chain for Table Understanding

https://github.com/hammoudhasan/diffclip

Differential Transformer

https://github.com/haoheliu/audioldm-training-finetuning

AudioLDM 2: Learning Holistic Audio Generation with Self-supervised Pretraining

https://github.com/hazyresearch/based

Can Mamba Learn How to Learn? A Comparative Study on In-Context Learning Tasks

https://github.com/hhhuang/cag

Long-context LLMs Struggle with Long In-context Learning

https://github.com/hkust-longgroup/comm

CoMM: A Coherent Interleaved Image-Text Dataset for Multimodal Understanding and Generation

https://github.com/huggingface/dataspeech

Natural language guidance of high-fidelity text-to-speech with synthetic annotations

https://github.com/huggingface/parler-tts

Natural language guidance of high-fidelity text-to-speech with synthetic annotations

https://github.com/jimmylihui/OpenGenome

HyenaDNA: Long-Range Genomic Sequence Modeling at Single Nucleotide Resolution

https://github.com/jimmylihui/genbench

HyenaDNA: Long-Range Genomic Sequence Modeling at Single Nucleotide Resolution

https://github.com/jonathanhus/back-to-school

A Benchmark for Learning to Translate a New Language from One Grammar Book

https://github.com/jubgjf/plmtuningcompetition

Black-Box Tuning for Language-Model-as-a-Service

https://github.com/judge0/judge0

ECCO: Can We Improve Model-Generated Code Efficiency Without Sacrificing Functional Correctness?

https://github.com/kakaobrain/kogpt

What Changes Can Large-scale Language Models Bring? Intensive Study on HyperCLOVA: Billions-scale Korean Generative Pretrained Transformers

https://github.com/kangmintong/c-rag

Learning to Retrieve In-Context Examples for Large Language Models

https://github.com/kareldo/biodex

In-Context Learning for Extreme Multi-Label Classification

https://github.com/knowlab/bi-weekly-paper-presentation

Textbooks Are All You Need II: phi-1.5 technical report

https://github.com/kohakublueleaf/lycoris

Few-Shot Parameter-Efficient Fine-Tuning is Better and Cheaper than In-Context Learning

https://github.com/kyegomez/PALI

PaLI-X: On Scaling up a Multilingual Vision and Language Model

https://github.com/layoutllm-t2i/layoutllm-t2i

LayoutLLM-T2I: Eliciting Layout Guidance from LLM for Text-to-Image Generation

https://github.com/liangyuwang/zo2

Fine-Tuning Language Models with Just Forward Passes

https://github.com/lichenghaobuaa/tokenlearning

ToolkenGPT: Augmenting Frozen Language Models with Massive Tools via Tool Embeddings

https://github.com/lifeiteng/vall-e

Neural Codec Language Models are Zero-Shot Text to Speech Synthesizers

https://github.com/lucidrains/naturalspeech2-pytorch

NaturalSpeech 2: Latent Diffusion Models are Natural and Zero-Shot Speech and Singing Synthesizers

https://github.com/lucidrains/voicebox-pytorch

Voicebox: Text-Guided Multilingual Universal Speech Generation at Scale

https://github.com/maomaocun/dllm-cache

Large Language Diffusion Models

https://github.com/masoudhashemi/AWMS

Tree of Problems: Improving structured problem solving with compositionality

https://github.com/mathisall/zo-adamu

Fine-Tuning Language Models with Just Forward Passes

https://github.com/mattrufolo/sysid-robust-transformer

From system models to class models: An in-context learning paradigm

https://github.com/microsoft/TUTA_table_understanding

SpreadsheetLLM: Encoding Spreadsheets for Large Language Models

https://github.com/microsoft/hitab

SpreadsheetLLM: Encoding Spreadsheets for Large Language Models

https://github.com/microsoft/nice-icl

NICE: To Optimize In-Context Examples or Not?

https://github.com/mindspore-ai/models/tree/master/official/nlp/pangu_alpha

PanGu-$α$: Large-scale Autoregressive Pretrained Chinese Language Models with Auto-parallel Computation

https://github.com/ml-gsai/llada

Large Language Diffusion Models

https://github.com/naver-ai/hypermix

What Changes Can Large-scale Language Models Bring? Intensive Study on HyperCLOVA: Billions-scale Korean Generative Pretrained Transformers

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