Summary Neurons in Large Language Models Dead N-gram Positional arxiv.org
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The analysis reveals that the initial part of the network in large language models is sparse, with numerous inactive neurons.
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Key Points
- Large language models (LLMs) have sparse activation patterns in the early part of the network, with many "dead" neurons.
- Positional neurons in FFN layers challenge the key-value memory view and can be used in ways that don't align with this view.
- Larger language models have dedicated neurons for certain features, but the space of semantic concepts is larger than the available neurons.
- Token-detecting neurons in LLMs cover different tokens in different layers, allowing larger models to effectively cover many tokens overall.
- Dead neurons in LLMs are identified and positional neurons encode information about token position.
- Positional neurons in language models can accurately encode absolute position without requiring positional encoding.
- Neurons have been a fundamental unit of analysis in various neural network models, including convolutional networks for images and text classifiers.
- The behavior of neurons in large language models has been studied in relation to N-gram detection. Larger models have more neurons responsible for detecting N-grams.
Summaries
32 word summary
The analysis explores the activation patterns of neurons in large language models, specifically the OPT family. It reveals that the early part of the network is sparse, with many neurons being "dead."
42 word summary
This analysis focuses on large language models (LLMs), specifically the OPT family of models, and examines the activation patterns of neurons within these models. The early part of the network is found to be sparse, with many neurons being "dead." The role
474 word summary
In this analysis of large language models (LLMs), the authors focus on the OPT family of models and examine the activation patterns of neurons within the models. They find that the early part of the network is sparse, with many neurons being "dead"
The role of positional neurons in FFN layers of large language models is still poorly understood. These neurons challenge the key-value memory view of FFN layers and suggest that the layers can be used in ways that don't align with this view. The study
In large language models, dedicated neurons are assigned to certain features in the early layers, and larger models tend to be more sparse. However, the space of fine-grained semantic concepts is too large compared to the number of neurons available. Dead neurons can
Token-detecting neurons in large language models have an ensemble-like behavior, where they cover largely different tokens in different layers. This behavior allows larger models to effectively cover many tokens overall. Previous evidence in computer vision models and transformers also supports this ensemble-like
The study examines dead neurons in large language models and identifies positional neurons that encode information about token position. The top suppressed concepts in these models are the tokens that trigger the neurons, and vector updates for these neurons point towards the next token candidates while pointing away
Positional neurons in large language models can have activation patterns that depend on token position. These neurons can reach extreme values of 0 or 1, indicating whether they are activated or not based solely on position. There are also positional neurons whose activation patterns
Positional neurons in language models can encode absolute position accurately, but do not require positional encoding. The presence of oscillatory neurons, along with other positional neurons, allows for encoding absolute position. However, these oscillatory patterns only appear with longer training time
Historically, neurons have been a fundamental unit of analysis in various neural network models. Initial works focused on convolutional networks for images and later for text classifiers. Similar findings of n-gram detectors have been observed in small convolutional text classifiers, but
This summary provides a list of references mentioned in the document "Neurons in Large Language Models Dead N-gram Positional." The references include papers and datasets related to language models and neural networks. Some of the key papers mentioned include "Adaptively
This document contains a list of references to various papers and technical reports related to neural language models. The papers cover topics such as scaling laws for neural language models, the impact of positional encoding on length generalization in transformers, text modular networks, revealing the
This document provides information on the behavior of neurons in large language models, specifically focusing on the detection of N-grams. The results show that larger models have more neurons responsible for detecting N-grams. There is a significant increase in the number of covered