Search Results for author:
Found 15 papers, 4 papers with code
From One to Many: Expanding the Scope of Toxicity Mitigation in Language Models
To date, toxicity mitigation in language models has almost entirely been focused on single-language settings.
Investigating Continual Pretraining in Large Language Models: Insights and Implications
This paper studies the evolving domain of Continual Learning (CL) in large language models (LLMs), with a focus on developing strategies for efficient and sustainable training.
Elo Uncovered: Robustness and Best Practices in Language Model Evaluation
In Natural Language Processing (NLP), the Elo rating system, originally designed for ranking players in dynamic games such as chess, is increasingly being used to evaluate Large Language Models (LLMs) through "A vs B" paired comparisons.
Which Prompts Make The Difference? Data Prioritization For Efficient Human LLM Evaluation
Human evaluation is increasingly critical for assessing large language models, capturing linguistic nuances, and reflecting user preferences more accurately than traditional automated metrics.
Goodtriever: Adaptive Toxicity Mitigation with Retrieval-augmented Models
Considerable effort has been dedicated to mitigating toxicity, but existing methods often require drastic modifications to model parameters or the use of computationally intensive auxiliary models.
On the Challenges of Using Black-Box APIs for Toxicity Evaluation in Research
We evaluate the implications of these changes on the reproducibility of findings that compare the relative merits of models and methods that aim to curb toxicity.
PASHA: Efficient HPO and NAS with Progressive Resource Allocation
Hyperparameter optimization (HPO) and neural architecture search (NAS) are methods of choice to obtain the best-in-class machine learning models, but in practice they can be costly to run.
Continual Learning with Transformers for Image Classification
This phenomenon is known as catastrophic forgetting and it is often difficult to prevent due to practical constraints, such as the amount of data that can be stored or the limited computation sources that can be used.
Memory Efficient Continual Learning with Transformers
Moreover, applications increasingly rely on large pre-trained neural networks, such as pre-trained Transformers, since the resources or data might not be available in sufficiently large quantities to practitioners to train the model from scratch.
Learning to Rank in the Position Based Model with Bandit Feedback
Personalization is a crucial aspect of many online experiences.
Linear Bandits with Stochastic Delayed Feedback
Stochastic linear bandits are a natural and well-studied model for structured exploration/exploitation problems and are widely used in applications such as online marketing and recommendation.
Differentially Private Variational Dropout
In this paper, we modify the recently proposed variational dropout technique which provided an elegant Bayesian interpretation to dropout, and show that the intrinsic noise in the variational dropout can be exploited to obtain a degree of differential privacy.
Differentially Private Dropout
Large data collections required for the training of neural networks often contain sensitive information such as the medical histories of patients, and the privacy of the training data must be preserved.
Incremental Variational Inference for Latent Dirichlet Allocation
We introduce incremental variational inference and apply it to latent Dirichlet allocation (LDA).
A Bayesian Tensor Factorization Model via Variational Inference for Link Prediction
Probabilistic approaches for tensor factorization aim to extract meaningful structure from incomplete data by postulating low rank constraints.
