Larger language models do in-context learning differently

J Wei, J Wei, Y Tay, D Tran, A Webson, Y Lu… - arXiv preprint arXiv …, 2023 - arxiv.org
We study how in-context learning (ICL) in language models is affected by semantic priors
versus input-label mappings. We investigate two setups-ICL with flipped labels and ICL with …

Testing the general deductive reasoning capacity of large language models using ood examples

A Saparov, RY Pang, V Padmakumar… - Advances in …, 2023 - proceedings.neurips.cc
Given the intractably large size of the space of proofs, any model that is capable of general
deductive reasoning must generalize to proofs of greater complexity. Recent studies have …

In-context learning with iterative demonstration selection

C Qin, A Zhang, C Chen, A Dagar, W Ye - arXiv preprint arXiv:2310.09881, 2023 - arxiv.org
Spurred by advancements in scale, large language models (LLMs) have demonstrated
strong few-shot learning ability via in-context learning (ICL). However, the performance of …

Open-Ethical AI: Advancements in Open-Source Human-Centric Neural Language Models

S Sicari, JF Cevallos M, A Rizzardi… - ACM Computing …, 2024 - dl.acm.org
This survey summarizes the most recent methods for building and assessing helpful, honest,
and harmless neural language models, considering small, medium, and large-size models …

Instruct me more! random prompting for visual in-context learning

J Zhang, B Wang, L Li, Y Nakashima… - Proceedings of the …, 2024 - openaccess.thecvf.com
Large-scale models trained on extensive datasets, have emerged as the preferred approach
due to their high generalizability across various tasks. In-context learning (ICL), a popular …

Skill-based few-shot selection for in-context learning

S An, B Zhou, Z Lin, Q Fu, B Chen, N Zheng… - arXiv preprint arXiv …, 2023 - arxiv.org
In-context learning is the paradigm that adapts large language models to downstream tasks
by providing a few examples. Few-shot selection--selecting appropriate examples for each …

Magnifico: Evaluating the in-context learning ability of large language models to generalize to novel interpretations

A Patel, S Bhattamishra, S Reddy… - arXiv preprint arXiv …, 2023 - arxiv.org
Humans possess a remarkable ability to assign novel interpretations to linguistic
expressions, enabling them to learn new words and understand community-specific …

Towards General Industrial Intelligence: A Survey on IIoT-Enhanced Continual Large Models

J Chen, J He, F Chen, Z Lv, J Tang, W Li, Z Liu… - arXiv preprint arXiv …, 2024 - arxiv.org
Currently, most applications in the Industrial Internet of Things (IIoT) still rely on CNN-based
neural networks. Although Transformer-based large models (LMs), including language …

Do large language models have compositional ability? an investigation into limitations and scalability

Z Xu, Z Shi, Y Liang - ICLR 2024 Workshop on Mathematical and …, 2024 - openreview.net
Large language models (LLM) have emerged as a powerful tool exhibiting remarkable in-
context learning (ICL) capabilities. In this study, we delve into the ICL capabilities of LLMs on …

Leveraging code to improve in-context learning for semantic parsing

B Bogin, S Gupta, P Clark, A Sabharwal - arXiv preprint arXiv:2311.09519, 2023 - arxiv.org
In-context learning (ICL) is an appealing approach for semantic parsing due to its few-shot
nature and improved generalization. However, learning to parse to rare domain-specific …