Copertina

Benchmarking General-Purpose In-Context Learning

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Wang, Fan, Lin, Chuan, Cao, Yang, Kang, Yu
Natura:	Preprint
Pubblicazione:	2024
Soggetti:	Artificial Intelligence Machine Learning
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

In-Context Learning can Perform Continual Learning Like Humans
di: Kang, Liuwang, et al.
Pubblicazione: (2025)

Context and Diversity Matter: The Emergence of In-Context Learning in World Models
di: Wang, Fan, et al.
Pubblicazione: (2025)

Towards Large-Scale In-Context Reinforcement Learning by Meta-Training in Randomized Worlds
di: Wang, Fan, et al.
Pubblicazione: (2025)

General-Purpose In-Context Learning by Meta-Learning Transformers
di: Kirsch, Louis, et al.
Pubblicazione: (2022)

Towards General-Purpose Model-Free Reinforcement Learning
di: Fujimoto, Scott, et al.
Pubblicazione: (2025)

Nemotron-Cascade: Scaling Cascaded Reinforcement Learning for General-Purpose Reasoning Models
di: Wang, Boxin, et al.
Pubblicazione: (2025)

Long-Context Attention Benchmark: From Kernel Efficiency to Distributed Context Parallelism
di: Bu, Tao, et al.
Pubblicazione: (2025)

FLIP: Flow-Centric Generative Planning as General-Purpose Manipulation World Model
di: Gao, Chongkai, et al.
Pubblicazione: (2024)

Grounding Foundation Models through Federated Transfer Learning: A General Framework
di: Kang, Yan, et al.
Pubblicazione: (2023)

Relational In-Context Learning via Synthetic Pre-training with Structural Prior
di: Wang, Yanbo, et al.
Pubblicazione: (2026)

Towards General Purpose Robots at Scale: Lifelong Learning and Learning to Use Memory
di: Yue, William
Pubblicazione: (2024)

RL + Transformer = A General-Purpose Problem Solver
di: Rentschler, Micah, et al.
Pubblicazione: (2025)

ThinkSwitch: Context Distillation with LoRA and Weight Interpolation for Specific-Purpose Reasoning Tasks
di: Saini, Dhruv, et al.
Pubblicazione: (2026)

Directed Acyclic Graph Structure Learning from Dynamic Graphs
di: Fan, Shaohua, et al.
Pubblicazione: (2022)

LLMs as Scalable, General-Purpose Simulators For Evolving Digital Agent Training
di: Wang, Yiming, et al.
Pubblicazione: (2025)

Generalizing Graph Neural Networks on Out-Of-Distribution Graphs
di: Fan, Shaohua, et al.
Pubblicazione: (2021)

Advancing General-Purpose Reasoning Models with Modular Gradient Surgery
di: Cai, Min, et al.
Pubblicazione: (2026)

Autoencoder-based General Purpose Representation Learning for Customer Embedding
di: Bertrand, Jan Henrik, et al.
Pubblicazione: (2024)

Large-Small Model Collaborative Framework for Federated Continual Learning
di: Yu, Hao, et al.
Pubblicazione: (2025)

FedRD: Reducing Divergences for Generalized Federated Learning via Heterogeneity-aware Parameter Guidance
di: Wang, Kaile, et al.
Pubblicazione: (2026)

LMAct: A Benchmark for In-Context Imitation Learning with Long Multimodal Demonstrations
di: Ruoss, Anian, et al.
Pubblicazione: (2024)

One for All: A Non-Linear Transformer can Enable Cross-Domain Generalization for In-Context Reinforcement Learning
di: He, Bowen, et al.
Pubblicazione: (2026)

PepBenchmark: A Standardized Benchmark for Peptide Machine Learning
di: Zhang, Jiahui, et al.
Pubblicazione: (2026)

Data-centric Federated Graph Learning with Large Language Models
di: Yan, Bo, et al.
Pubblicazione: (2025)

Meta-Learning Transformers to Improve In-Context Generalization
di: Braccaioli, Lorenzo, et al.
Pubblicazione: (2025)

Exploring Context Generalizability in Citywide Crowd Mobility Prediction: An Analytic Framework and Benchmark
di: Chen, Liyue, et al.
Pubblicazione: (2021)

FedCCA: Client-Centric Adaptation against Data Heterogeneity in Federated Learning on IoT Devices
di: Wang, Kaile, et al.
Pubblicazione: (2026)

ODRL: A Benchmark for Off-Dynamics Reinforcement Learning
di: Lyu, Jiafei, et al.
Pubblicazione: (2024)

CLIMB: Class-imbalanced Learning Benchmark on Tabular Data
di: Liu, Zhining, et al.
Pubblicazione: (2025)

Towards Monotonic Improvement in In-Context Reinforcement Learning
di: Zhang, Wenhao, et al.
Pubblicazione: (2025)

Robometer: Scaling General-Purpose Robotic Reward Models via Trajectory Comparisons
di: Liang, Anthony, et al.
Pubblicazione: (2026)

Causal Information Prioritization for Efficient Reinforcement Learning
di: Cao, Hongye, et al.
Pubblicazione: (2025)

A Physics-preserved Transfer Learning Method for Differential Equations
di: Yang, Hao-Ran, et al.
Pubblicazione: (2025)

ID-MixGCL: Identity Mixup for Graph Contrastive Learning
di: Zhang, Gehang, et al.
Pubblicazione: (2023)

Generative Pre-Trained Transformer for Symbolic Regression Base In-Context Reinforcement Learning
di: Li, Yanjie, et al.
Pubblicazione: (2024)

Large Language Models for Constructing and Optimizing Machine Learning Workflows: A Survey
di: Gu, Yang, et al.
Pubblicazione: (2024)

Automated Structured Radiology Report Generation with Rich Clinical Context
di: Kang, Seongjae, et al.
Pubblicazione: (2025)

GeFL: Model-Agnostic Federated Learning with Generative Models
di: Kang, Honggu, et al.
Pubblicazione: (2024)

100-LongBench: Are de facto Long-Context Benchmarks Literally Evaluating Long-Context Ability?
di: Yang, Wang, et al.
Pubblicazione: (2025)

Think Then Embed: Generative Context Improves Multimodal Embedding
di: Cui, Xuanming, et al.
Pubblicazione: (2025)