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Papers Explained 56: Alpaca

Papers Explained 56: Alpaca

Alpaca is fine-tuned from Meta’s LLaMA 7B model. The Alpaca model is trained on 52K instruction-following demonstrations generated in the…
Go to the profile of Ritvik Rastogi
Ritvik Rastogi
Papers Explained 55: LLaMA

Papers Explained 55: LLaMA

LLaMA is a collection of foundation language models ranging from 7B to 65B parameters, trained on trillions of tokens using publicly…
Go to the profile of Ritvik Rastogi
Ritvik Rastogi
Papers Explained 54: ChatGPT

Papers Explained 54: ChatGPT

ChatGPT is an interactive model designed to engage in conversations. Its conversational format allows ChatGPT to respond to subsequent…
Go to the profile of Ritvik Rastogi
Ritvik Rastogi
Papers Explained 53: Galactica

Papers Explained 53: Galactica

Galactica is an LLM specializing in scientific knowledge, surpasses existing models on a variety of scientific tasks, excelling in…
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Ritvik Rastogi
Papers Explained 52: BLOOM

Papers Explained 52: BLOOM

BLOOM is a 176B-parameter open-access decoder-only transformer model, collaboratively developed by hundreds of researchers, aiming to…
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Ritvik Rastogi
Papers Explained 51: OPT

Papers Explained 51: OPT

Open Pre-trained Transformers (OPT) comprise a suite of decoder-only pre-trained transformers with parameter ranges from 125M to 175B…
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Ritvik Rastogi
Papers Explained 50: PaLM

Papers Explained 50: PaLM

Pathways Language Model (PaLM) is a 540-billion parameter, densely activated, Transformer language model. It is trained on 6144 TPU v4…
Go to the profile of Ritvik Rastogi
Ritvik Rastogi
Papers Explained 49: Chinchilla

Papers Explained 49: Chinchilla

This paper investigated the optimal model size and number of tokens for training a transformer LLM within a given compute budget and…
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Ritvik Rastogi
Papers Explained 48: InstructGPT

Papers Explained 48: InstructGPT

This paper shows an avenue for aligning language models with user intent on a wide range of tasks by fine-tuning with human feedback…
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Ritvik Rastogi
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Detecting Sarcasm with Deep Convolutional Neural Networks

Detecting Sarcasm with Deep Convolutional Neural Networks

Overview This paper addresses a key NLP problem known as sarcasm detection using a combination of models based on convolutional neural…
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elvis
A Light Introduction to Transfer Learning for NLP

A Light Introduction to Transfer Learning for NLP

In this post, I will introduce transfer learning for natural language processing and key questions necessary to better understand this…
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elvis
Deep Learning for NLP: An Overview of Recent Trends

Deep Learning for NLP: An Overview of Recent Trends

In a timely new paper, Young and colleagues discuss some of the recent trends in deep learning based natural language processing (NLP)…
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elvis
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