Paper Summaries

A Survey of Large Language Models - Summary

This paper surveys the recent advances in Large Language Models (LLMs), which are pre-trained Transformer models over large-scale corpora. The paper discusses the background, key findings, and mainstream techniques of LLMs, focusing on pre-training, adaptation tuning, utilization, and capacity eval

Arxiv URL: https://arxiv.org/abs/2303.18223v1

Authors: Wayne Xin Zhao, Kun Zhou, Junyi Li, Tianyi Tang, Xiaolei Wang, Yupeng Hou, Yingqian Min, Beichen Zhang, Junjie Zhang, Zican Dong, Yifan Du, Chen Yang, Yushuo Chen, Zhipeng Chen, Jinhao Jiang, Ruiyang Ren, Yifan Li, Xinyu Tang, Zikang Liu, Peiyu Liu, Jian-Yun Nie, Ji-Rong Wen

Summary:

This paper surveys the recent advances in Large Language Models (LLMs), which are pre-trained Transformer models over large-scale corpora. The paper discusses the background, key findings, and mainstream techniques of LLMs, focusing on pre-training, adaptation tuning, utilization, and capacity evaluation. The paper also summarizes the available resources for developing LLMs and discusses the remaining issues for future directions.

Key Insights & Learnings:

Language modeling is a major approach to advancing language intelligence of machines.
LLMs display surprising emergent abilities that may not be observed in previous smaller PLMs.
LLMs have revolutionized the way that humans develop and use AI algorithms.
The development of LLMs no longer draws a clear distinction between research and engineering.
LLMs are posing a significant impact on the AI community and the advent of ChatGPT and GPT-4 leads to the rethinking of the possibilities of artificial general intelligence (AGI).

Terms Mentioned: Large Language Models, Emergent Abilities, Adaptation Tuning, Utilization, Alignment, Capacity Evaluation, Language Modeling, Statistical Language Models, Neural Language Models, Pre-trained Language Models, Transformer Architecture, Self-Attention Mechanisms, Artificial Intelligence, Natural Language Processing, Recurrent Neural Networks, Word2Vec, Bidirectional LSTM, BERT, GPT-2, BART, ChatGPT, Artificial General Intelligence

Technologies / Libraries Mentioned: ELMo, Transformer, PyTorch, TensorFlow

Instruction Tuning with GPT-4 - Summary

The paper presents the first attempt to use GPT-4 to generate instruction-following data for Large Language Models (LLMs) finetuning. The 52K English and Chinese instruction-following data generated by GPT-4 leads to superior zero-shot performance on new tasks compared to the instruction-following

Are We Really Making Much Progress in Text Classification? A Comparative Review - Summary

This paper reviews and compares methods for single-label and multi-label text classification, categorizing them into bag-of-words, sequence-based, graph-based, and hierarchical methods. The findings reveal that pre-trained language models outperform all recently proposed graph-based and hierarchy-b

AudioGPT: Understanding and Generating Speech, Music, Sound, and Talking Head - Summary

The paper proposes a multi-modal AI system named AudioGPT that complements Large Language Models (LLMs) with foundation models to process complex audio information and solve numerous understanding and generation tasks. AudioGPT is connected with an input/output interface (ASR, TTS) to support spoke

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