Convergent Evolution: How Different Language Models Learn Similar Number Representations
Fuente:
arXiv
Saved in:
| Main Authors: | Fu, Deqing, Zhou, Tianyi, Belkin, Mikhail, Sharan, Vatsal, Jia, Robin |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Transformers Learn to Achieve Second-Order Convergence Rates for In-Context Linear Regression
by: Fu, Deqing, et al.
Published: (2023)
by: Fu, Deqing, et al.
Published: (2023)
Pre-trained Large Language Models Use Fourier Features to Compute Addition
by: Zhou, Tianyi, et al.
Published: (2024)
by: Zhou, Tianyi, et al.
Published: (2024)
Transformers Learn Low Sensitivity Functions: Investigations and Implications
by: Vasudeva, Bhavya, et al.
Published: (2024)
by: Vasudeva, Bhavya, et al.
Published: (2024)
FoNE: Precise Single-Token Number Embeddings via Fourier Features
by: Zhou, Tianyi, et al.
Published: (2025)
by: Zhou, Tianyi, et al.
Published: (2025)
Promote, Suppress, Iterate: How Language Models Answer One-to-Many Factual Queries
by: Yan, Tianyi Lorena, et al.
Published: (2025)
by: Yan, Tianyi Lorena, et al.
Published: (2025)
Latent Concept Disentanglement in Transformer-based Language Models
by: Hong, Guan Zhe, et al.
Published: (2025)
by: Hong, Guan Zhe, et al.
Published: (2025)
Transformers Provably Learn Algorithmic Solutions for Graph Connectivity, But Only with the Right Data
by: Ye, Qilin, et al.
Published: (2025)
by: Ye, Qilin, et al.
Published: (2025)
Catching rationalization in the act: detecting motivated reasoning before and after CoT via activation probing
by: Mirtaheri, Parsa, et al.
Published: (2026)
by: Mirtaheri, Parsa, et al.
Published: (2026)
DeLLMa: Decision Making Under Uncertainty with Large Language Models
by: Liu, Ollie, et al.
Published: (2024)
by: Liu, Ollie, et al.
Published: (2024)
Multilingual Prompt Engineering in Large Language Models: A Survey Across NLP Tasks
by: Vatsal, Shubham, et al.
Published: (2025)
by: Vatsal, Shubham, et al.
Published: (2025)
Ulterior Motives: Detecting Misaligned Reasoning in Continuous Thought Models
by: Ramjee, Sharan
Published: (2026)
by: Ramjee, Sharan
Published: (2026)
Limitations on Accurate, Trusted, Human-level Reasoning
by: Panigrahy, Rina, et al.
Published: (2025)
by: Panigrahy, Rina, et al.
Published: (2025)
Emotion Classification in Low and Moderate Resource Languages
by: Tafreshi, Shabnam, et al.
Published: (2024)
by: Tafreshi, Shabnam, et al.
Published: (2024)
Textual Steering Vectors Can Improve Visual Understanding in Multimodal Large Language Models
by: Gan, Woody Haosheng, et al.
Published: (2025)
by: Gan, Woody Haosheng, et al.
Published: (2025)
Toward universal steering and monitoring of AI models
by: Beaglehole, Daniel, et al.
Published: (2025)
by: Beaglehole, Daniel, et al.
Published: (2025)
Can GPT Redefine Medical Understanding? Evaluating GPT on Biomedical Machine Reading Comprehension
by: Vatsal, Shubham, et al.
Published: (2024)
by: Vatsal, Shubham, et al.
Published: (2024)
Learning to Interpret Weight Differences in Language Models
by: Goel, Avichal, et al.
Published: (2025)
by: Goel, Avichal, et al.
Published: (2025)
What makes Reasoning Models Different? Follow the Reasoning Leader for Efficient Decoding
by: Li, Ming, et al.
Published: (2025)
by: Li, Ming, et al.
Published: (2025)
Schoenfeld's Anatomy of Mathematical Reasoning by Language Models
by: Li, Ming, et al.
Published: (2025)
by: Li, Ming, et al.
Published: (2025)
Luna-2: Scalable Single-Token Evaluation with Small Language Models
by: Goel, Vatsal, et al.
Published: (2026)
by: Goel, Vatsal, et al.
Published: (2026)
One Model for All: Multi-Objective Controllable Language Models
by: He, Qiang, et al.
Published: (2026)
by: He, Qiang, et al.
Published: (2026)
Exploring Concept Depth: How Large Language Models Acquire Knowledge and Concept at Different Layers?
by: Jin, Mingyu, et al.
Published: (2024)
by: Jin, Mingyu, et al.
Published: (2024)
Why Larger Language Models Do In-context Learning Differently?
by: Shi, Zhenmei, et al.
Published: (2024)
by: Shi, Zhenmei, et al.
Published: (2024)
Bias Amplification in Language Model Evolution: An Iterated Learning Perspective
by: Ren, Yi, et al.
Published: (2024)
by: Ren, Yi, et al.
Published: (2024)
Can GPT Improve the State of Prior Authorization via Guideline Based Automated Question Answering?
by: Vatsal, Shubham, et al.
Published: (2024)
by: Vatsal, Shubham, et al.
Published: (2024)
Language Models Learn Universal Representations of Numbers and Here's Why You Should Care
by: Štefánik, Michal, et al.
Published: (2025)
by: Štefánik, Michal, et al.
Published: (2025)
FairPy: A Toolkit for Evaluation of Prediction Biases and their Mitigation in Large Language Models
by: Viswanath, Hrishikesh, et al.
Published: (2023)
by: Viswanath, Hrishikesh, et al.
Published: (2023)
How Instruction and Reasoning Data shape Post-Training: Data Quality through the Lens of Layer-wise Gradients
by: Li, Ming, et al.
Published: (2025)
by: Li, Ming, et al.
Published: (2025)
Improving Large Language Model Safety with Contrastive Representation Learning
by: Simko, Samuel, et al.
Published: (2025)
by: Simko, Samuel, et al.
Published: (2025)
EmbedLLM: Learning Compact Representations of Large Language Models
by: Zhuang, Richard, et al.
Published: (2024)
by: Zhuang, Richard, et al.
Published: (2024)
A Survey on Diffusion Language Models
by: Li, Tianyi, et al.
Published: (2025)
by: Li, Tianyi, et al.
Published: (2025)
Resa: Transparent Reasoning Models via SAEs
by: Wang, Shangshang, et al.
Published: (2025)
by: Wang, Shangshang, et al.
Published: (2025)
Uncovering Emergent Physics Representations Learned In-Context by Large Language Models
by: Song, Yeongwoo, et al.
Published: (2025)
by: Song, Yeongwoo, et al.
Published: (2025)
Large Language Models in the Task of Automatic Validation of Text Classifier Predictions
by: Tsymbalov, Aleksandr, et al.
Published: (2025)
by: Tsymbalov, Aleksandr, et al.
Published: (2025)
The Features at Convergence Theorem: a first-principles alternative to the Neural Feature Ansatz for how networks learn representations
by: Boix-Adsera, Enric, et al.
Published: (2025)
by: Boix-Adsera, Enric, et al.
Published: (2025)
EPSVec: Efficient and Private Synthetic Data Generation via Dataset Vectors
by: Banayeeanzade, Amin, et al.
Published: (2026)
by: Banayeeanzade, Amin, et al.
Published: (2026)
Discovering Hierarchical Latent Capabilities of Language Models via Causal Representation Learning
by: Jin, Jikai, et al.
Published: (2025)
by: Jin, Jikai, et al.
Published: (2025)
Sink-Aware Pruning for Diffusion Language Models
by: Myrzakhan, Aidar, et al.
Published: (2026)
by: Myrzakhan, Aidar, et al.
Published: (2026)
Leveraging Large Language Models for Wireless Symbol Detection via In-Context Learning
by: Abbas, Momin, et al.
Published: (2024)
by: Abbas, Momin, et al.
Published: (2024)
Exploiting Transliterated Words for Finding Similarity in Inter-Language News Articles using Machine Learning
by: Naeem, Sameea, et al.
Published: (2022)
by: Naeem, Sameea, et al.
Published: (2022)
Similar Items
-
Transformers Learn to Achieve Second-Order Convergence Rates for In-Context Linear Regression
by: Fu, Deqing, et al.
Published: (2023) -
Pre-trained Large Language Models Use Fourier Features to Compute Addition
by: Zhou, Tianyi, et al.
Published: (2024) -
Transformers Learn Low Sensitivity Functions: Investigations and Implications
by: Vasudeva, Bhavya, et al.
Published: (2024) -
FoNE: Precise Single-Token Number Embeddings via Fourier Features
by: Zhou, Tianyi, et al.
Published: (2025) -
Promote, Suppress, Iterate: How Language Models Answer One-to-Many Factual Queries
by: Yan, Tianyi Lorena, et al.
Published: (2025)