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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/cisco-open/DeepVision/tree/main/recallm

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RecallM: An Adaptable Memory Mechanism with Temporal Understanding for Large Language Models

https://github.com/cisnlp/manchu-in-context-mt

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Understanding In-Context Machine Translation for Low-Resource Languages: A Case Study on Manchu

https://github.com/cisnlp/xampler

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XAMPLER: Learning to Retrieve Cross-Lingual In-Context Examples

https://github.com/cmooncs/semeval-2024_multimodal_ecpe

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JMI at SemEval 2024 Task 3: Two-step approach for multimodal ECAC using in-context learning with GPT and instruction-tuned Llama models

https://github.com/codeeff/ecco

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

https://github.com/codelion/optillm

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Eliciting Fine-Tuned Transformer Capabilities via Inference-Time Techniques

https://github.com/cognitiveailab/bytesized32

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ByteSized32: A Corpus and Challenge Task for Generating Task-Specific World Models Expressed as Text Games

https://github.com/colored-dye/truthfulness_probe_generalization

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Probing the Geometry of Truth: Consistency and Generalization of Truth Directions in LLMs Across Logical Transformations and Question Answering Tasks

https://github.com/corl-team/rebased

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Linear Transformers with Learnable Kernel Functions are Better In-Context Models

https://github.com/corxyz/icl-cmr

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Linking In-context Learning in Transformers to Human Episodic Memory

https://github.com/cristianocapone/abss

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Adaptive behavior with stable synapses

https://github.com/csebuetnlp/illusionvqa

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IllusionVQA: A Challenging Optical Illusion Dataset for Vision Language Models

https://github.com/cycloneboy/base_sql

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BASE-SQL: A powerful open source Text-To-SQL baseline approach

https://github.com/czbnlp/LPE-SQL

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Leveraging Prior Experience: An Expandable Auxiliary Knowledge Base for Text-to-SQL

https://github.com/d09942015ntu/cot_ood_latent

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Chain-of-Thought Prompting for Out-of-Distribution Samples: A Latent-Variable Study

https://github.com/dair-iitd/SSP

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SSP: Self-Supervised Prompting for Cross-Lingual Transfer to Low-Resource Languages using Large Language Models

https://github.com/dair-iitd/fusic-kbqa

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Few-shot Transfer Learning for Knowledge Base Question Answering: Fusing Supervised Models with In-Context Learning

https://github.com/danschumac1/civilpromptreasoninggpt4

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Team UTSA-NLP at SemEval 2024 Task 5: Prompt Ensembling for Argument Reasoning in Civil Procedures with GPT4

https://github.com/david-rundel/tabpfn_iml

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Interpretable Machine Learning for TabPFN

https://github.com/debugml/incontext_influences

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In-context Example Selection with Influences

https://github.com/declare-lab/trust-align

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Measuring and Enhancing Trustworthiness of LLMs in RAG through Grounded Attributions and Learning to Refuse

https://github.com/deeplearning-wisc/picle

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PICLe: Eliciting Diverse Behaviors from Large Language Models with Persona In-Context Learning

https://github.com/deeplearnxmu/ssr

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Mitigating Catastrophic Forgetting in Large Language Models with Self-Synthesized Rehearsal

https://github.com/deepmind/emergent_in_context_learning

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Data Distributional Properties Drive Emergent In-Context Learning in Transformers

https://github.com/delta-lab-ai/data_efficient_nopt

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Data-Efficient Operator Learning via Unsupervised Pretraining and In-Context Learning

https://github.com/demoleiwang/llmsrec_syn

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The Whole is Better than the Sum: Using Aggregated Demonstrations in In-Context Learning for Sequential Recommendation

https://github.com/deqingfu/transformers-icl-higher-order

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Transformers Learn to Achieve Second-Order Convergence Rates for In-Context Linear Regression

https://github.com/derrickzhuyz/statqa

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Are Large Language Models Good Statisticians?

https://github.com/dhdhagar/byokg

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

https://github.com/diegodoimo/geometry_icl_finetuning

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The representation landscape of few-shot learning and fine-tuning in large language models

https://github.com/dki-lab/few-shot-bioie

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Thinking about GPT-3 In-Context Learning for Biomedical IE? Think Again

https://github.com/dki-lab/pangu

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Don't Generate, Discriminate: A Proposal for Grounding Language Models to Real-World Environments

https://github.com/dminus1/LLM-OOD-control

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Controlling Out-of-Domain Gaps in LLMs for Genre Classification and Generated Text Detection

https://github.com/donttal/tara

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Distinguishability Calibration to In-Context Learning

https://github.com/dqxiu/icl_paperlist

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A Survey on In-context Learning

https://github.com/dtsip/in-context-learning

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What Can Transformers Learn In-Context? A Case Study of Simple Function Classes

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