Meta|in|context learning in large language models

Meta-in-context learning in large language models - arXiv

Title:Meta-in-context learning in large language models ... Abstract:Large language models have shown tremendous performance in a variety of tasks ...

Meta-in-context learning in large language models

Looking at two idealized domains, a one-dimensional regression task and a two-armed bandit task, we show that meta-in-context learning adaptively reshapes a ...

Meta-in-context learning in large language models - OpenReview

Large language models have shown tremendous performance in a variety of tasks. In-context learning -- the ability to improve at a task after being provided ...

Meta-in-context learning in large language models

Meta-in-context learning adaptively modifies a large language model's priors over latent variables and that it adjusts its learning strategies.

Meta-in-context learning in large language models - NIPS

Large language models (LLMs) are taking not only machine learning research but also society by storm [1, 2, 3]. Part of what makes these models so persuasive is ...

Meta In-Context Learning Makes Large Language Models Better ...

A new meta-training framework for zero and few-shot RE where an LLM is tuned to do ICL on a diverse collection of RE datasets.

[R] Why Can GPT Learn In-Context? Language Models Secretly ...

Large pretrained language models have shown surprising In-Context Learning (ICL) ability. With a few demonstration input-label pairs, they ...

Meta-In-Context Learning For Large Language Models (LLMs)

This paper demonstrates that the in-context learning abilities of large language models can be recursively improved via in-context learning itself.

Meta In-Context Learning: Harnessing Large Language Models for ...

Meta learning alleviates the demand for data collection and annotation since it transfers the knowledge from previous tasks to unseen tasks.

Meta-learning via Language Model In-context Tuning - ACL Anthology

Compared to non-fine-tuned in-context learning (i.e. prompting a raw LM), in-context tuning meta-trains the model to learn from in-context examples. On ...

Meta-Learning the Difference: Preparing Large Language Models ...

We prepare PLMs for data- and parameter-efficient adaptation by learning to learn the difference between general and adapted PLMs.

Meta-learning via language model in-context tuning - Amazon Science

The goal of meta-learning is to learn to adapt to a new task with only a few labeled examples. Inspired by the recent progress in large language models, ...

IAD: In-Context Learning Ability Decoupler of Large Language ...

Large Language Models (LLMs) exhibit remarkable In-Context Learning (ICL) ability, where the model learns tasks from prompts consisting of input-output examples ...

Meta-in-context learning in large language models | Request PDF

Request PDF | Meta-in-context learning in large language models | Large language models have shown tremendous performance in a variety of tasks.

Meta-in-context learning in large language models

Large language models have shown tremendous performance in a variety of tasks. In-context learning - the ability to improve at a task after ...

Solving a machine-learning mystery | MIT News

In the machine-learning research community, many scientists have come to believe that large language models can perform in-context learning ...

How in-context learning improves large language models

One powerful ability is at least partly responsible for their growing popularity: in-context learning, which enriches a model with examples ...

META LEARNING WITH LANGUAGE MODELS: CHALLENGES ...

To realize the full potential of available limited resources, we propose a meta learning technique (MLT) that combines individual models built ...

Out-of-context Meta-learning in Large Language Models - ICLR 2025

Out-of-context Meta-learning in Large Language Models. Dmitrii Krasheninnikov · Egor Krasheninnikov · David Krueger.

What is In-context Learning, and how does it work - Lakera AI

In-context learning (ICL) is a technique where task demonstrations are integrated into the prompt in a natural language format.