Search Results for author:
Found 4 papers, 2 papers with code
Beyond Size: How Gradients Shape Pruning Decisions in Large Language Models
GBLM-Pruner leverages the first-order term of the Taylor expansion, operating in a training-free manner by harnessing properly normalized gradients from a few calibration samples to determine the pruning metric, and substantially outperforms competitive counterparts like SparseGPT and Wanda in multiple benchmarks.
SlimPajama-DC: Understanding Data Combinations for LLM Training
This paper aims to understand the impacts of various data combinations (e. g., web text, wikipedia, github, books) on the training of large language models using SlimPajama.
CSDS: A Fine-Grained Chinese Dataset for Customer Service Dialogue Summarization
Therefore, in this paper, we introduce a novel Chinese dataset for Customer Service Dialogue Summarization (CSDS).
Empirical Evaluation of Multi-task Learning in Deep Neural Networks for Natural Language Processing
Multi-Task Learning (MTL) aims at boosting the overall performance of each individual task by leveraging useful information contained in multiple related tasks.
