3 papers
cs.LG2024
An Empirical Investigation into the Effect of Parameter Choices in Knowledge Distillation
Md Arafat Sultan, Aashka Trivedi, Parul Awasthy +1
We present a large-scale empirical study of how choices of configuration parameters affect performance in knowledge distillation (KD). An example of such a KD parameter is the meas…
cs.CL2024
Efficient Models for the Detection of Hate, Abuse and Profanity
Christoph Tillmann, Aashka Trivedi, Bishwaranjan Bhattacharjee
Large Language Models (LLMs) are the cornerstone for many Natural Language Processing (NLP) tasks like sentiment analysis, document classification, named entity recognition, questi…
cs.CL2023
A Comparative Analysis of Task-Agnostic Distillation Methods for Compressing Transformer Language Models
Takuma Udagawa, Aashka Trivedi, Michele Merler +1
Large language models have become a vital component in modern NLP, achieving state of the art performance in a variety of tasks. However, they are often inefficient for real-world…