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Found 12 papers, 7 papers with code
The PRISM Alignment Project: What Participatory, Representative and Individualised Human Feedback Reveals About the Subjective and Multicultural Alignment of Large Language Models
Human feedback plays a central role in the alignment of Large Language Models (LLMs).
Understanding the Role of Input Token Characters in Language Models: How Does Information Loss Affect Performance?
Understanding how and what pre-trained language models (PLMs) learn about language is an open challenge in natural language processing.
Active Learning Principles for In-Context Learning with Large Language Models
The remarkable advancements in large language models (LLMs) have significantly enhanced the performance in few-shot learning settings.
On the Limitations of Simulating Active Learning
Active learning (AL) is a human-and-model-in-the-loop paradigm that iteratively selects informative unlabeled data for human annotation, aiming to improve over random sampling.
Dynamic Benchmarking of Masked Language Models on Temporal Concept Drift with Multiple Views
Temporal concept drift refers to the problem of data changing over time.
Investigating Multi-source Active Learning for Natural Language Inference
We show that four popular active learning schemes fail to outperform random selection when applied to unlabelled pools comprised of multiple data sources on the task of natural language inference.
Challenges and Strategies in Cross-Cultural NLP
Various efforts in the Natural Language Processing (NLP) community have been made to accommodate linguistic diversity and serve speakers of many different languages.
Active Learning by Acquiring Contrastive Examples
Common acquisition functions for active learning use either uncertainty or diversity sampling, aiming to select difficult and diverse data points from the pool of unlabeled data, respectively.
Frustratingly Simple Pretraining Alternatives to Masked Language Modeling
Masked language modeling (MLM), a self-supervised pretraining objective, is widely used in natural language processing for learning text representations.
On the Importance of Effectively Adapting Pretrained Language Models for Active Learning
Recent Active Learning (AL) approaches in Natural Language Processing (NLP) proposed using off-the-shelf pretrained language models (LMs).
Domain Adversarial Fine-Tuning as an Effective Regularizer
To address this issue, we introduce a new regularization technique, AFTER; domain Adversarial Fine-Tuning as an Effective Regularizer.
Attention-based Conditioning Methods for External Knowledge Integration
This form of conditioning on the attention distribution, enforces the contribution of the most salient words for the task at hand.
