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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/RobvanGastel/meta-in-context-learning

General-Purpose In-Context Learning by Meta-Learning Transformers

https://github.com/Samyu0304/thought-propagation

Can Language Models Solve Graph Problems in Natural Language?

https://github.com/XGenerationLab/M-Schema

A Preview of XiYan-SQL: A Multi-Generator Ensemble Framework for Text-to-SQL

https://github.com/XGenerationLab/XiYan-DateResolver

A Preview of XiYan-SQL: A Multi-Generator Ensemble Framework for Text-to-SQL

https://github.com/Xiaoqi-Zhao-DLUT/GateNet-RGB-Saliency

Inspiring the Next Generation of Segment Anything Models: Comprehensively Evaluate SAM and SAM 2 with Diverse Prompts Towards Context-Dependent Concepts under Different Scenes

https://github.com/YuanJianhao508/RAG-Driver

RAG-Driver: Generalisable Driving Explanations with Retrieval-Augmented In-Context Learning in Multi-Modal Large Language Model

https://github.com/adelacvg/ns2vc

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

https://github.com/agentcoord/agentcoord

ExpertPrompting: Instructing Large Language Models to be Distinguished Experts

https://github.com/albertotestoni/ndq_visual_objects

Grounding Language Models to Images for Multimodal Inputs and Outputs

https://github.com/alibaba/thinking_bias

Label Words are Anchors: An Information Flow Perspective for Understanding In-Context Learning

https://github.com/allenai/visprog

Visual Programming: Compositional visual reasoning without training

https://github.com/asap7772/fewshot-preference-optimization

FSPO: Few-Shot Preference Optimization of Synthetic Preference Data in LLMs Elicits Effective Personalization to Real Users

https://github.com/automl/ifbo

In-Context Freeze-Thaw Bayesian Optimization for Hyperparameter Optimization

https://github.com/automl/tabpfn

TabPFN: A Transformer That Solves Small Tabular Classification Problems in a Second

https://github.com/batorskq/prl

Large Language Models Are Human-Level Prompt Engineers

https://github.com/behavioral-data/cognitive-reframing

Cognitive Reframing of Negative Thoughts through Human-Language Model Interaction

https://github.com/blinkdl/chatrwkv

RWKV-7 "Goose" with Expressive Dynamic State Evolution

https://github.com/buswayne/sysid-transformers-control

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

https://github.com/calvinmccarter/kditransform

TabPFN: A Transformer That Solves Small Tabular Classification Problems in a Second

https://github.com/cambridgeltl/multi3woz

DIALIGHT: Lightweight Multilingual Development and Evaluation of Task-Oriented Dialogue Systems with Large Language Models

https://github.com/carperai/openelm

Language Model Crossover: Variation through Few-Shot Prompting

https://github.com/chen700564/supercd

Learning In-context Learning for Named Entity Recognition

https://github.com/chiral-carbon/kg-for-science

Bring Your Own KG: Self-Supervised Program Synthesis for Zero-Shot KGQA

https://github.com/codelion/optillm/blob/main/optillm/leap.py

In-Context Principle Learning from Mistakes

https://github.com/cyqiq/multicot

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

https://github.com/distractor-generation/dg_survey

Automated Distractor and Feedback Generation for Math Multiple-choice Questions via In-context Learning

https://github.com/dmis-lab/temporalhead

Knowledge Circuits in Pretrained Transformers

https://github.com/doc-doc/NExT-OE

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

https://github.com/dsubuntu/scie

Can Language Models Perform Robust Reasoning in Chain-of-thought Prompting with Noisy Rationales?

https://github.com/egg-hu/lora-recycle

Unlocking Tuning-Free Few-Shot Adaptability in Visual Foundation Models by Recycling Pre-Tuned LoRAs

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

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

https://github.com/ericwtodd/function_vectors

Function Vectors in Large Language Models

https://github.com/facebookresearch/minihack

LLM Augmented Hierarchical Agents

https://github.com/forgi86/lru-reduction

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

https://github.com/forgi86/sysid-transformers-transfer

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

https://github.com/frederikkemarin/bend

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

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