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All or None: Identifiable Linear Properties of Next-token Predictors in Language Modeling

All or None: Identifiable Linear Properties of Next-token Predictors in Language Modeling

30 October 2024
Emanuele Marconato
Sébastien Lachapelle
Sebastian Weichwald
Luigi Gresele
ArXivPDFHTML

Papers citing "All or None: Identifiable Linear Properties of Next-token Predictors in Language Modeling"

3 / 3 papers shown
Title
An Empirically Grounded Identifiability Theory Will Accelerate Self-Supervised Learning Research
An Empirically Grounded Identifiability Theory Will Accelerate Self-Supervised Learning Research
Patrik Reizinger
Randall Balestriero
David Klindt
Wieland Brendel
31
0
0
17 Apr 2025
I Predict Therefore I Am: Is Next Token Prediction Enough to Learn Human-Interpretable Concepts from Data?
I Predict Therefore I Am: Is Next Token Prediction Enough to Learn Human-Interpretable Concepts from Data?
Yuhang Liu
Dong Gong
Erdun Gao
Zhen Zhang
Biwei Huang
Mingming Gong
Anton van den Hengel
Javen Qinfeng Shi
J. Shi
55
0
0
12 Mar 2025
What is causal about causal models and representations?
What is causal about causal models and representations?
Frederik Hytting Jørgensen
Luigi Gresele
S. Weichwald
CML
97
0
0
31 Jan 2025
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