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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/xhan77/in-context-alignment

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In-Context Alignment: Chat with Vanilla Language Models Before Fine-Tuning

https://github.com/xiamengzhou/training_trajectory_analysis

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Training Trajectories of Language Models Across Scales

https://github.com/xiaohao-liu/bundle-mllm

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Fine-tuning Multimodal Large Language Models for Product Bundling

https://github.com/xingxuanli/paraicl

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ParaICL: Towards Parallel In-Context Learning

https://github.com/xirui-li/drattack

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DrAttack: Prompt Decomposition and Reconstruction Makes Powerful LLM Jailbreakers

https://github.com/xiye17/complementaryexpl

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Complementary Explanations for Effective In-Context Learning

https://github.com/xiye17/textualexplincontext

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The Unreliability of Explanations in Few-shot Prompting for Textual Reasoning

https://github.com/xlang-ai/batch-prompting

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Batch Prompting: Efficient Inference with Large Language Model APIs

https://github.com/xlhex/acl2024_xicl

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Using Natural Language Explanations to Improve Robustness of In-context Learning

https://github.com/xsc1234/info-rag

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Unsupervised Information Refinement Training of Large Language Models for Retrieval-Augmented Generation

https://github.com/xuboshen/egoncepp

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EgoNCE++: Do Egocentric Video-Language Models Really Understand Hand-Object Interactions?

https://github.com/xwinks/llm_articulated_object_manipulation

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Kinematic-aware Prompting for Generalizable Articulated Object Manipulation with LLMs

https://github.com/xyzcs/infoac

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Addressing Order Sensitivity of In-Context Demonstration Examples in Causal Language Models

https://github.com/yandachen/iclsensitivity

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On the Relation between Sensitivity and Accuracy in In-context Learning

https://github.com/yandachen/in-context-tuning

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Meta-learning via Language Model In-context Tuning

https://github.com/yandex-research/mind-your-format

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Mind Your Format: Towards Consistent Evaluation of In-Context Learning Improvements

https://github.com/yandex-research/tabpfn-finetuning

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On Finetuning Tabular Foundation Models

https://github.com/yangjiaxi/deepthinking

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Iterative Forward Tuning Boosts In-Context Learning in Language Models

https://github.com/yanglinyi/supervised-knowledge-makes-large-language-models-better-in-context-learners

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Supervised Knowledge Makes Large Language Models Better In-context Learners

https://github.com/yangzhch6/alignedcot

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AlignedCoT: Prompting Large Language Models via Native-Speaking Demonstrations

https://github.com/yasdel/benchmark_recllm_fairness

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Understanding Biases in ChatGPT-based Recommender Systems: Provider Fairness, Temporal Stability, and Recency

https://github.com/yaxinzhuars/icxml

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ICXML: An In-Context Learning Framework for Zero-Shot Extreme Multi-Label Classification

https://github.com/yc-li20/emotion-prompt

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Revise, Reason, and Recognize: LLM-Based Emotion Recognition via Emotion-Specific Prompts and ASR Error Correction

https://github.com/yegcjs/diffusionllm

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Diffusion Language Models Can Perform Many Tasks with Scaling and Instruction-Finetuning

https://github.com/yenchehsiao/autonomousllmagentwithadaptingplanning

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Towards Autonomous Agents: Adaptive-planning, Reasoning, and Acting in Language Models

https://github.com/yfqiu-nlp/temporal-llms

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Are Large Language Models Temporally Grounded?

https://github.com/yhydhx/chatgpt-api

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DeID-GPT: Zero-shot Medical Text De-Identification by GPT-4

https://github.com/yhzhu99/llm4healthcare

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Prompting Large Language Models for Zero-Shot Clinical Prediction with Structured Longitudinal Electronic Health Record Data

https://github.com/yingcong-li/transformers-as-algorithms

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Transformers as Algorithms: Generalization and Stability in In-context Learning

https://github.com/yingkaid/jailfuzzer

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Fuzz-Testing Meets LLM-Based Agents: An Automated and Efficient Framework for Jailbreaking Text-To-Image Generation Models

https://github.com/yinzhangyue/selfaware

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Do Large Language Models Know What They Don't Know?

https://github.com/yiren-jian/lm-supcon

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Contrastive Learning for Prompt-Based Few-Shot Language Learners

https://github.com/yisunlp/DAIL

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Demonstration Augmentation for Zero-shot In-context Learning

https://github.com/yixinliu233/arc

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ARC: A Generalist Graph Anomaly Detector with In-Context Learning

https://github.com/yiye3/icleval

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ICLEval: Evaluating In-Context Learning Ability of Large Language Models

https://github.com/ym689/rec_icl

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Real-Time Personalization for LLM-based Recommendation with Customized In-Context Learning

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