Papers with Code 아카이브

In-Context Learning

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

전체 기법전체 데이터셋

하위 태스크

0

평가표

0

보존된 평가 기록

0

코드

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

https://github.com/manishacharya60/rag-optimization

공식 코드

Optimizing Code Runtime Performance through Context-Aware Retrieval-Augmented Generation

https://github.com/mansheej/icl-task-diversity

공식 코드

Pretraining task diversity and the emergence of non-Bayesian in-context learning for regression

https://github.com/marc1198/chat-hsr

공식 코드

LLM-Empowered Embodied Agent for Memory-Augmented Task Planning in Household Robotics

https://github.com/margotyjx/fmint

공식 코드

FMint: Bridging Human Designed and Data Pretrained Models for Differential Equation Foundation Model

https://github.com/mariannerakic/tyche

공식 코드

Tyche: Stochastic In-Context Learning for Medical Image Segmentation

https://github.com/mario-sanz/cicl

공식 코드

Corrective In-Context Learning: Evaluating Self-Correction in Large Language Models

https://github.com/martin-wey/peft-llm-code

공식 코드

Exploring Parameter-Efficient Fine-Tuning Techniques for Code Generation with Large Language Models

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

공식 코드

Enhanced Transformer architecture for in-context learning of dynamical systems

https://github.com/mattyoon/reasoning-models-confidence

공식 코드

Reasoning Models Better Express Their Confidence

https://github.com/mayukaj/llm-boost

공식 코드

Transformers Boost the Performance of Decision Trees on Tabular Data across Sample Sizes

https://github.com/mbzuai-nlp/programfc

공식 코드

Fact-Checking Complex Claims with Program-Guided Reasoning

https://github.com/mcgill-nlp/incontext-code-generation

공식 코드

Evaluating In-Context Learning of Libraries for Code Generation

https://github.com/mcgill-nlp/magnifico

공식 코드

MAGNIFICo: Evaluating the In-Context Learning Ability of Large Language Models to Generalize to Novel Interpretations

https://github.com/mdrpanwar/icl-bayesian-prism

공식 코드

In-Context Learning through the Bayesian Prism

https://github.com/mediatechnologycenter/absinth

공식 코드

German also Hallucinates! Inconsistency Detection in News Summaries with the Absinth Dataset

https://github.com/mehdimojarradi/Ensemble-SuperICL

공식 코드

Improving In-Context Learning with Small Language Model Ensembles

https://github.com/meszarosanna/rule_extrapolation

공식 코드

Rule Extrapolation in Language Models: A Study of Compositional Generalization on OOD Prompts

https://github.com/meta-inf/bayes_icl

공식 코드

Is In-Context Learning in Large Language Models Bayesian? A Martingale Perspective

https://github.com/meta-inf/suq-nlg

공식 코드

On Subjective Uncertainty Quantification and Calibration in Natural Language Generation

https://github.com/metaevo/awesome-metabbo

공식 코드

Toward Automated Algorithm Design: A Survey and Practical Guide to Meta-Black-Box-Optimization

https://github.com/michael-wzhu/PromptCBLUE

공식 코드

Overview of the PromptCBLUE Shared Task in CHIP2023

https://github.com/microsoft/KBLaM

공식 코드

KBLaM: Knowledge Base augmented Language Model

https://github.com/microsoft/LLMLingua

공식 코드

LLMLingua: Compressing Prompts for Accelerated Inference of Large Language Models

https://github.com/microsoft/LNewKnow

공식 코드

Uncovering inequalities in new knowledge learning by large language models across different languages

https://github.com/microsoft/clinical-self-verification

공식 코드

Self-Verification Improves Few-Shot Clinical Information Extraction

https://github.com/microsoft/dp-few-shot-generation

공식 코드

Privacy-Preserving In-Context Learning with Differentially Private Few-Shot Generation

https://github.com/microsoft/few_shot_adaptation_for_parsing_contextual_utterances_with_llms

공식 코드

Few-Shot Adaptation for Parsing Contextual Utterances with LLMs

https://github.com/microsoft/layoutgeneration

공식 코드

LayoutPrompter: Awaken the Design Ability of Large Language Models

https://github.com/microsoft/list

공식 코드

LiST: Lite Prompted Self-training Makes Parameter-Efficient Few-shot Learners

https://github.com/microsoft/lmops

공식 코드

Structured Prompting: Scaling In-Context Learning to 1,000 Examples

https://github.com/microsoft/simulated-trial-and-error

공식 코드

LLMs in the Imaginarium: Tool Learning through Simulated Trial and Error

https://github.com/microsoft/ticl

공식 코드

MotherNet: Fast Training and Inference via Hyper-Network Transformers

https://github.com/microsoft/unilm

공식 코드

Language Models are General-Purpose Interfaces

https://github.com/microsoft/unilm/blob/master/Diff-Transformer/

공식 코드

Differential Transformer

https://github.com/microsoft/unilm/tree/master/kosmos-2

공식 코드

Kosmos-2: Grounding Multimodal Large Language Models to the World

https://github.com/midiyazhu/micl

공식 코드

Logit Separability-Driven Samples and Multiple Class-Related Words Selection for Advancing In-Context Learning

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