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Found 6 papers, 0 papers with code
Detectors for Safe and Reliable LLMs: Implementations, Uses, and Limitations
Large language models (LLMs) are susceptible to a variety of risks, from non-faithful output to biased and toxic generations.
Efficient Models for the Detection of Hate, Abuse and Profanity
This is unacceptable in civil discourse. The detection of Hate, Abuse and Profanity in text is a vital component of creating civil and unbiased LLMs, which is needed not only for English, but for all languages.
An Empirical Investigation into the Effect of Parameter Choices in Knowledge Distillation
We present a large-scale empirical study of how choices of configuration parameters affect performance in knowledge distillation (KD).
Muted: Multilingual Targeted Offensive Speech Identification and Visualization
Offensive language such as hate, abuse, and profanity (HAP) occurs in various content on the web.
A Comparative Analysis of Task-Agnostic Distillation Methods for Compressing Transformer Language Models
Our target of study includes Output Distribution (OD) transfer, Hidden State (HS) transfer with various layer mapping strategies, and Multi-Head Attention (MHA) transfer based on MiniLMv2.
Neural Architecture Search for Effective Teacher-Student Knowledge Transfer in Language Models
In each episode of the search process, a NAS controller predicts a reward based on the distillation loss and latency of inference.
