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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/suzyahyah/icl_anti-lm_decoding

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Anti-LM Decoding for Zero-shot In-context Machine Translation

https://github.com/swj0419/in-context-pretraining

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In-context Pretraining: Language Modeling Beyond Document Boundaries

https://github.com/sycny/rae

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Retrieval-enhanced Knowledge Editing in Language Models for Multi-Hop Question Answering

https://github.com/syp2ysy/prompt-self

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Exploring Effective Factors for Improving Visual In-Context Learning

https://github.com/takagi97/llms-are-parallel-multilingual-learners

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Revealing the Parallel Multilingual Learning within Large Language Models

https://github.com/takagi97/pmt2i

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Boosting Text-To-Image Generation via Multilingual Prompting in Large Multimodal Models

https://github.com/tbijl/course_ranking_data

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Efficient course recommendations with T5-based ranking and summarization

https://github.com/teachable-ai-lab/tutor_gym

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TutorGym: A Testbed for Evaluating AI Agents as Tutors and Students

https://github.com/telepathylabsai/prompt-based-user-simulator

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In-Context Learning User Simulators for Task-Oriented Dialog Systems

https://github.com/tengfeiliu966/hc-llm

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HC-LLM: Historical-Constrained Large Language Models for Radiology Report Generation

https://github.com/terarachang/LLMDecomp

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When Parts Are Greater Than Sums: Individual LLM Components Can Outperform Full Models

https://github.com/terarachang/dataicl

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Data Curation Alone Can Stabilize In-context Learning

https://github.com/tergelmunkhbat/concise-reasoning

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Self-Training Elicits Concise Reasoning in Large Language Models

https://github.com/text-machine-lab/extending_psycholinguistic_dataset

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Larger Probes Tell a Different Story: Extending Psycholinguistic Datasets Via In-Context Learning

https://github.com/text-machine-lab/mini_gpt

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Emergent Abilities in Reduced-Scale Generative Language Models

https://github.com/text-machine-lab/understanding_prompts_via_corruption

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Deconstructing In-Context Learning: Understanding Prompts via Corruption

https://github.com/tfburns/amicl-and-residual-attention-streams

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Associative memory inspires improvements for in-context learning using a novel attention residual stream architecture

https://github.com/thiqah/arablegaleval

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ArabLegalEval: A Multitask Benchmark for Assessing Arabic Legal Knowledge in Large Language Models

https://github.com/thomasyyyoung/toxibenchcn

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Exploring Multimodal Challenges in Toxic Chinese Detection: Taxonomy, Benchmark, and Findings

https://github.com/thu-coai/picl

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Pre-Training to Learn in Context

https://github.com/thu-coai/vpo

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VPO: Aligning Text-to-Video Generation Models with Prompt Optimization

https://github.com/thudm/longbench

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LongBench v2: Towards Deeper Understanding and Reasoning on Realistic Long-context Multitasks

https://github.com/thuml/AutoTimes

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AutoTimes: Autoregressive Time Series Forecasters via Large Language Models

https://github.com/tianyi-lab/bento

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BenTo: Benchmark Task Reduction with In-Context Transferability

https://github.com/tianyi-lab/c3po

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C3PO: Critical-Layer, Core-Expert, Collaborative Pathway Optimization for Test-Time Expert Re-Mixing

https://github.com/tiger-ai-lab/longiclbench

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Long-context LLMs Struggle with Long In-context Learning

https://github.com/tiktok/fast_prompt_alignment

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Fast Prompt Alignment for Text-to-Image Generation

https://github.com/tileb1/cribo

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CrIBo: Self-Supervised Learning via Cross-Image Object-Level Bootstrapping

https://github.com/tizzzzy/demonstration_selection_overview

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Comparative Analysis of Demonstration Selection Algorithms for LLM In-Context Learning

https://github.com/tjtp/d2controller

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Dynamic Demonstrations Controller for In-Context Learning

https://github.com/tml-epfl/icl-alignment

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Is In-Context Learning Sufficient for Instruction Following in LLMs?

https://github.com/tml-epfl/llm-adaptive-attacks

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Jailbreaking Leading Safety-Aligned LLMs with Simple Adaptive Attacks

https://github.com/tmllab/2024_icml_dicl

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Machine Vision Therapy: Multimodal Large Language Models Can Enhance Visual Robustness via Denoising In-Context Learning

https://github.com/tmllab/machine_vision_therapy

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Machine Vision Therapy: Multimodal Large Language Models Can Enhance Visual Robustness via Denoising In-Context Learning

https://github.com/tmlr-group/noisyrationales

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

https://github.com/tongnie/awesome-llm4tr

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Exploring the Roles of Large Language Models in Reshaping Transportation Systems: A Survey, Framework, and Roadmap

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