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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/aryamanarora/bayesian-laws-icl

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Bayesian scaling laws for in-context learning

https://github.com/asaakyan/explain-st

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ICLEF: In-Context Learning with Expert Feedback for Explainable Style Transfer

https://github.com/asaakyan/socnormnli

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Sociocultural Norm Similarities and Differences via Situational Alignment and Explainable Textual Entailment

https://github.com/asacooperstickland/situational-awareness-evals

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Taken out of context: On measuring situational awareness in LLMs

https://github.com/asgsaeid/cad_vqa

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How to Determine the Preferred Image Distribution of a Black-Box Vision-Language Model?

https://github.com/athenarc/citationintentopenllm

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Can LLMs Predict Citation Intent? An Experimental Analysis of In-context Learning and Fine-tuning on Open LLMs

https://github.com/atomslab/llmsforsr

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In Context Learning and Reasoning for Symbolic Regression with Large Language Models

https://github.com/atutej/RARe

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RARe: Retrieval Augmented Retrieval with In-Context Examples

https://github.com/automl/is_mamba_capable_of_icl

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Is Mamba Capable of In-Context Learning?

https://github.com/automl/oswm

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One-shot World Models Using a Transformer Trained on a Synthetic Prior

https://github.com/automl/pfns4bo

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PFNs4BO: In-Context Learning for Bayesian Optimization

https://github.com/automl/transformerscandobayesianinference

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Transformers Can Do Bayesian Inference

https://github.com/azreasoners/gpt-asp-rules

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Leveraging Large Language Models to Generate Answer Set Programs

https://github.com/baaivision/emu

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Generative Multimodal Models are In-Context Learners

https://github.com/baaivision/painter

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Images Speak in Images: A Generalist Painter for In-Context Visual Learning

https://github.com/barneslab/pallm

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PALLM: Evaluating and Enhancing PALLiative Care Conversations with Large Language Models

https://github.com/bat-reality/ddrcu

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Dynamic Demonstration Retrieval and Cognitive Understanding for Emotional Support Conversation

https://github.com/bdbc-kg-nlp/ngcse

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Narrowing the Gap between Supervised and Unsupervised Sentence Representation Learning with Large Language Model

https://github.com/behappyplz/ds2-absa

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DS$^2$-ABSA: Dual-Stream Data Synthesis with Label Refinement for Few-Shot Aspect-Based Sentiment Analysis

https://github.com/behavioral-data/bolt

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A Computational Framework for Behavioral Assessment of LLM Therapists

https://github.com/behavioral-data/cognitive-reframing-of-negative-thoughts

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Cognitive Reframing of Negative Thoughts through Human-Language Model Interaction

https://github.com/beichenzbc/booststep

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BoostStep: Boosting mathematical capability of Large Language Models via improved single-step reasoning

https://github.com/benfengxu/knnprompting

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$k$NN Prompting: Beyond-Context Learning with Calibration-Free Nearest Neighbor Inference

https://github.com/berlino/grammar-prompting

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Grammar Prompting for Domain-Specific Language Generation with Large Language Models

https://github.com/berlino/seq_icl

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In-Context Language Learning: Architectures and Algorithms

https://github.com/bernard-yang/simamr

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Emphasising Structured Information: Integrating Abstract Meaning Representation into LLMs for Enhanced Open-Domain Dialogue Evaluation

https://github.com/beyondguo/llm-tuning

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Sample Design Engineering: An Empirical Study of What Makes Good Downstream Fine-Tuning Samples for LLMs

https://github.com/bflashcp3f/deer

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Label-Guided In-Context Learning for Named Entity Recognition

https://github.com/bgavran/category_theory_machine_learning

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Fundamental Components of Deep Learning: A category-theoretic approach

https://github.com/bibisbar/LLaVA-Steering

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LLaVA Steering: Visual Instruction Tuning with 500x Fewer Parameters through Modality Linear Representation-Steering

https://github.com/bigai-ai/ICE

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In-Context Editing: Learning Knowledge from Self-Induced Distributions

https://github.com/bigai-nlco/docgnre

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Semi-automatic Data Enhancement for Document-Level Relation Extraction with Distant Supervision from Large Language Models

https://github.com/bigai-nlco/loogle

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LooGLE: Can Long-Context Language Models Understand Long Contexts?

https://github.com/bigheiniu/mend

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MEND: Meta dEmonstratioN Distillation for Efficient and Effective In-Context Learning

https://github.com/bithlp/homebench

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HomeBench: Evaluating LLMs in Smart Homes with Valid and Invalid Instructions Across Single and Multiple Devices

https://github.com/boleima/topro

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ToPro: Token-Level Prompt Decomposition for Cross-Lingual Sequence Labeling Tasks

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