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Found 6 papers, 5 papers with code
PSYDIAL: Personality-based Synthetic Dialogue Generation using Large Language Models
Experimental results indicate that while pre-trained models and those fine-tuned with a chit-chat dataset struggle to generate responses reflecting personality, models trained with PSYDIAL show significant improvements.
ExeGPT: Constraint-Aware Resource Scheduling for LLM Inference
This paper presents ExeGPT, a distributed system designed for constraint-aware LLM inference.
NASH: A Simple Unified Framework of Structured Pruning for Accelerating Encoder-Decoder Language Models
Structured pruning methods have proven effective in reducing the model size and accelerating inference speed in various network architectures such as Transformers.
Token-Scaled Logit Distillation for Ternary Weight Generative Language Models
Generative Language Models (GLMs) have shown impressive performance in tasks such as text generation, understanding, and reasoning.
Revisiting Intermediate Layer Distillation for Compressing Language Models: An Overfitting Perspective
Knowledge distillation (KD) is a highly promising method for mitigating the computational problems of pre-trained language models (PLMs).
Understanding and Improving Knowledge Distillation for Quantization-Aware Training of Large Transformer Encoders
In particular, KD has been employed in quantization-aware training (QAT) of Transformer encoders like BERT to improve the accuracy of the student model with the reduced-precision weight parameters.
