All Topics
All Topics
Technology
Technology
AI
AI
Business
Business
Entertainment
Entertainment
News
News
Programming
Programming
Science
Science
Design
Design
Environment
Environment
Finance
Finance
Crypto
Crypto
Politics
Politics
Sports
Sports
Education
Education
Gaming
Gaming
Art
Art
Music
Music
Health
Health
Security
Security
Books
Books
Food
Food
Travel
Travel
Personal
Personal
Bluesky
Twitter

Transformer LMs encode some relational knowledge linearly, but not all

Much of the knowledge encoded in transformer language models (LMs) may be expressed in terms of relations: relations between words and their synonyms, entities and their attributes, etc. We show…

Read the full article

You might also wanna read

Research Proves Transformer Language Models Are Injective and Invertible

Transformer components such as non-linear activations and normalization are inherently non-injective, suggesting that different inputs could

arxiv.org·8mo ago

Understanding Linear Representations and Superposition in Large Language Model Interpretability

As LLMs become larger, more capable, and more ubiquitous, the field of mechanistic interpretability -- that is, understanding the inner wor.

ternarysearch.blogspot.com·5mo ago

How Transformers Process Information: A Deep Dive into Complexity

Exploring how transformers handle information reveals complexities in compositionality. Our analysis shows surprising gaps in their zero-sho

machinebrief.com·6d ago

Study Finds Single Transformer Layer Can Match Full-Parameter RL Training in LLMs

Reinforcement learning (RL) has become a central component of post-training large language models (LLMs), yet little is understood about how

arxiv.org·15d ago

Study Finds Single Transformer Layer Can Match Full-Parameter RL Training in LLMs

Reinforcement learning (RL) has become a central component of post-training large language models (LLMs), yet little is understood about how

arxiv.org·15d ago

Analyzing Positional Encodings in Transformer Models: Impact on Expressiveness and Generalization

Positional encodings are a core part of transformer-based models, enabling processing of sequential data without recurrence. This paper pres

arxiv.org·1y ago

Towards Mechanistically Understanding Why Memorized Knowledge Fails to Generalize in Large Language Model Finetuning

arXiv:2607.08393v1 Announce Type: cross Abstract: Fine-tuning LLMs to inject new knowledge faces a critical challenge: LLMs can quickly memo

machinebrief.com·7d ago

Comments

Sign in to join the conversation.

No comments yet. Be the first.