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Found 13 papers, 4 papers with code
Unveiling Imitation Learning: Exploring the Impact of Data Falsity to Large Language Model
Many recent studies endeavor to improve open-source language models through imitation learning, and re-training on the synthetic instruction data from state-of-the-art proprietary models like ChatGPT and GPT-4.
HyperCLOVA X Technical Report
We introduce HyperCLOVA X, a family of large language models (LLMs) tailored to the Korean language and culture, along with competitive capabilities in English, math, and coding.
Universal Domain Adaptation for Robust Handling of Distributional Shifts in NLP
When deploying machine learning systems to the wild, it is highly desirable for them to effectively leverage prior knowledge to the unfamiliar domain while also firing alarms to anomalous inputs.
Instruction Tuning with Human Curriculum
In this work, we (1) introduce Curriculum Instruction Tuning, (2) explore the potential advantages of employing diverse curriculum strategies, and (3) delineate a synthetic instruction-response generation framework that complements our theoretical approach.
CELDA: Leveraging Black-box Language Model as Enhanced Classifier without Labels
Additionally, our proposed methodology can be applied universally to any LM and has the potential to scale to larger models, making it a more viable option for utilizing large LMs.
Probing Out-of-Distribution Robustness of Language Models with Parameter-Efficient Transfer Learning
As the size of the pre-trained language model (PLM) continues to increase, numerous parameter-efficient transfer learning methods have been proposed recently to compensate for the tremendous cost of fine-tuning.
Prompt-Augmented Linear Probing: Scaling beyond the Limit of Few-shot In-Context Learners
Through in-context learning (ICL), large-scale language models are effective few-shot learners without additional model fine-tuning.
Enhancing Out-of-Distribution Detection in Natural Language Understanding via Implicit Layer Ensemble
Out-of-distribution (OOD) detection aims to discern outliers from the intended data distribution, which is crucial to maintaining high reliability and a good user experience.
Finding the global semantic representation in GAN through Frechet Mean
This semantic basis represents sample-independent meaningful perturbations that change the same semantic attribute of an image on the entire latent space.
Self-Generated In-Context Learning: Leveraging Auto-regressive Language Models as a Demonstration Generator
Large-scale pre-trained language models (PLMs) are well-known for being capable of solving a task simply by conditioning a few input-label pairs dubbed demonstrations on a prompt without being explicitly tuned for the desired downstream task.
Analyzing the Latent Space of GAN through Local Dimension Estimation
In this paper, we approach this problem through a geometric analysis of latent spaces as a manifold.
Ground-Truth Labels Matter: A Deeper Look into Input-Label Demonstrations
Despite recent explosion of interests in in-context learning, the underlying mechanism and the precise impact of the quality of demonstrations remain elusive.
Masked Contrastive Learning for Anomaly Detection
However, the primary objective of contrastive learning is to learn task-agnostic features without any labels, which is not entirely suited to discern anomalies.
