Learning to Learn a Language

Prior-Fitted Language Model, trained solely on synthetic non-linguistic data, learns to infer and predict real languages from context with frozen weights, achieving strong cross-lingual compression and reasoning without ever seeing real text.

Lennart Carstens-Behrens, Holger Fröhlich

Published Oct 5, 2026 · ▲ 7 on Hugging Face · Code ★ 1

Labels Override Definitions in Jev-Style Typed Decision Models

Typed decision models exhibit option-label bias because prompts prepend labels to definitions, letting label semantics override rules; removing labels or altering formatting fixes it.

Seyedarmin Azizi, Erfan Baghaei Potraghloo, Massoud Pedram

Published Oct 1, 2026 · 0 citations · ▲ 5 on Hugging Face

Efficient Task Adaptation in Large Language Models: A Survey of Weight-Based, Prompt-Based, and Embedding-Based Adaptations

This survey unifies weight, prompt, and embedding adaptation methods for large language models into one taxonomy, analyzing trade-offs and cross-paradigm relationships.

Jungwon Park, Changin Choi, Jimyeong Kim, Nojun Kwak and 1 more

Published Oct 1, 2026 · 0 citations

Capturing In-Context Learning Dynamics with Task Operators

Task Operator captures ICL as stable per-task affine attention transformations, enabling efficient zero-shot replay that nearly matches in-context performance and scales beyond context limits.

Guangzhi Xiong, Zhenghao He, Bohan Liu, Sanchit Sinha and 2 more

Atlanta Poster Session 5, Fri, Dec 11, 10:00 AM–1:00 PM, Hall C1 · Published Oct 1, 2026 · 0 citations

BDH-CQ: In-Context Learning with Recurrent Latent Reasoning

BDH-CQ combines in-context learning with recurrent latent reasoning, achieving 29.5% ARC-AGI-1 pass@2 at $0.0007 per task to set a new cost-efficiency frontier.

Björn Engdahl, Adrian Kosowski, Jan Chorowski, Zuzanna Stamirowska and 5 more

Published Aug 10, 2026 · 0 citations · ▲ 797 on Hugging Face · Code ★ 11,072

TextReg: Mitigating Prompt Distributional Overfitting via Regularized Text-Space Optimization

TextReg mitigates prompt distributional overfitting via regularized text-space optimization, improving out-of-distribution accuracy by up to 16.5% over prior methods.

傅卢成, Ye Yu, Yiyang Wang, Yiqiao Jin and 3 more

Published May 20, 2026 · 0 citations · ▲ 7 on Hugging Face · Code

MAPLE: Many-Shot Adaptive Pseudo-Labeling for In-Context Learning

MAPLE uses influence-based pseudo-labeling to adaptively select many-shot ICL demonstrations, boosting LLM performance without extensive labeling costs.

Zihan Chen, Song Wang, Zhen Tan, Jundong Li and 1 more

Published May 22, 2025 · 0 citations

Divide, Reweight, and Conquer: A Logit Arithmetic Approach for In-Context Learning

LARA improves in-context learning by dividing long demonstrations into shorter parallel groups and reweighting their logits via non-gradient optimization, boosting accuracy and memory efficiency over baselines on BBH and MMLU.

Chengsong Huang, Langlin Huang, Jiaxin Huang

Published Oct 14, 2024 · 1 citation · ▲ 1 on Hugging Face · Code ★ 8

ChatIE: Zero-Shot Information Extraction via Chatting with ChatGPT

ChatIE reframes zero-shot information extraction as multi-turn ChatGPT dialogue, surpassing some fully supervised models on several benchmark datasets.

Wei, Xiang, Xingyu Cui, Ning Cheng, Xiaobin Wang and 8 more

Published Feb 20, 2023 · 147 citations

CAPO: A Primal-Dual Framework for Constraint-Aware Prompt Optimization

Victor Ye Dong, Reid Pryzant, Yi Liu, Jian Jiao

Sydney Poster Session 6, Thu, Dec 10, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

SPLICE: Structured Prompt Local Iterative Combinatorial Evolution

Dr. Anish Acharya, Phillip Studans, Amit Dhanda, Ninad V Rao and 3 more

Sydney Poster Session 4, Wed, Dec 9, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

Read-Only Zero-Shot Classifier Expansion from Pairwise Semantics

Zhiqiang Zhong, Jun Pang

Paris Poster Session 2, Wed, Dec 9, 5:00 PM–7:00 PM, Paris Poster Hall · Published 2026

Reinforced Fast Weights via Next-Sequence Prediction

Hee Seung Hwang, Xindi Wu, Sanghyuk Chun, Zhiwei Deng and 1 more

Sydney Poster Session 6, Thu, Dec 10, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

Transformers Can Learn Multiclass Classification In-Context: Isotropy Governs Generalization

Daehan Yoon, Chulhee Yun

Sydney Poster Session 3, Wed, Dec 9, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

From Contexts to Conditionals: Statistical Self-Consistency of Persona Prompting

Patrik Wolf, Thomas Kleine Buening, Andreas Krause, Celestine Mendler-Dünner

Sydney Poster Session 5, Thu, Dec 10, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

One-Layer Transformers Provably Learn In-Context K-Nearest Neighbor Prediction with Chain-of-Thought

Lyumin Wu, Yuan Cao

Sydney Poster Session 4, Wed, Dec 9, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

Optimal In-Context Learning of Autoregressive Processes under Heterogeneous Second-Order Moments of the Prompts

Hanna Tseran, Masaaki Imaizumi

Atlanta Poster Session 1, Wed, Dec 9, 10:00 AM–1:00 PM, Hall C1 · Published 2026

Salvation Lies Within: Proactive Prefix Re-forming for LLM-based Tagging

SHULAN WANG, YingJie Zhu, Yuting Yan, Ke Cheng and 7 more

Sydney Poster Session 2, Tue, Dec 8, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

Prompt Optimization Makes Misalignment Legible

Caleb Biddulph, Micah Carroll

Sydney Poster Session 2, Tue, Dec 8, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

Closed-Loop Alignment: Socially Coupled In-Context Learning and Relational Posterior Collapse

Hidenori Tanaka

Sydney Poster Session 2, Tue, Dec 8, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026