2 citations · 9 across the 12 of their papers we have counts for
8 papers · 1 filter
Bias Analysis and Mitigation through Protected Attribute Detection and Regard Classification
Takuma Udagawa, Yang Zhao, Hiroshi Kanayama +1
Large language models (LLMs) acquire general linguistic knowledge from massive-scale pretraining. However, pretraining data mainly comprised of web-crawled texts contain undesirabl…
GneissWeb: Preparing High Quality Data for LLMs at Scale
Hajar Emami Gohari, Swanand Ravindra Kadhe, Syed Yousaf Shah +29
Data quantity and quality play a vital role in determining the performance of Large Language Models (LLMs). High-quality data, in particular, can significantly boost the LLM's abil…
INDUS: Effective and Efficient Language Models for Scientific Applications
Bishwaranjan Bhattacharjee, Aashka Trivedi, Masayasu Muraoka +33
Large language models (LLMs) trained on general domain corpora showed remarkable results on natural language processing (NLP) tasks. However, previous research demonstrated LLMs tr…
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…
Muted: Multilingual Targeted Offensive Speech Identification and Visualization
Christoph Tillmann, Aashka Trivedi, Sara Rosenthal +4
Offensive language such as hate, abuse, and profanity (HAP) occurs in various content on the web. While previous work has mostly dealt with sentence level annotations, there have b…
Teach me with a Whisper: Enhancing Large Language Models for Analyzing Spoken Transcripts using Speech Embeddings
Fatema Hasan, Yulong Li, James Foulds +2
Speech data has rich acoustic and paralinguistic information with important cues for understanding a speaker's tone, emotion, and intent, yet traditional large language models such…
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…
Neural Architecture Search for Effective Teacher-Student Knowledge Transfer in Language Models
Aashka Trivedi, Takuma Udagawa, Michele Merler +3
Large pretrained language models have achieved state-of-the-art results on a variety of downstream tasks. Knowledge Distillation (KD) into a smaller student model addresses their i…