Cover Image

Language Models Do Hard Arithmetic Tasks Easily and Hardly Do Easy Arithmetic Tasks

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Gambardella, Andrew, Iwasawa, Yusuke, Matsuo, Yutaka
Format:	Preprint
Published:	2024
Subjects:	Machine Learning Artificial Intelligence Computation and Language
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Inconsistent Tokenizations Cause Language Models to be Perplexed by Japanese Grammar
by: Gambardella, Andrew, et al.
Published: (2025)

Semantic Token Clustering for Efficient Uncertainty Quantification in Large Language Models
by: Cao, Qi, et al.
Published: (2026)

The Unreasonable Effectiveness of Easy Training Data for Hard Tasks
by: Hase, Peter, et al.
Published: (2024)

Curriculum Reinforcement Learning from Easy to Hard Tasks Improves LLM Reasoning
by: Parashar, Shubham, et al.
Published: (2025)

Rethinking Evaluation of Sparse Autoencoders through the Representation of Polysemous Words
by: Minegishi, Gouki, et al.
Published: (2025)

Exposing Limitations of Language Model Agents in Sequential-Task Compositions on the Web
by: Furuta, Hiroki, et al.
Published: (2023)

Which Programming Language and What Features at Pre-training Stage Affect Downstream Logical Inference Performance?
by: Uchiyama, Fumiya, et al.
Published: (2024)

Self-Harmony: Learning to Harmonize Self-Supervision and Self-Play in Test-Time Reinforcement Learning
by: Wang, Ru, et al.
Published: (2025)

Position Coupling: Improving Length Generalization of Arithmetic Transformers Using Task Structure
by: Cho, Hanseul, et al.
Published: (2024)

Safe Transformer: An Explicit Safety Bit For Interpretable And Controllable Alignment
by: Feng, Jingyuan, et al.
Published: (2026)

Easy-to-Hard Generalization: Scalable Alignment Beyond Human Supervision
by: Sun, Zhiqing, et al.
Published: (2024)

Large Language Models as Theory of Mind Aware Generative Agents with Counterfactual Reflection
by: Yang, Bo, et al.
Published: (2025)

Easy2Hard-Bench: Standardized Difficulty Labels for Profiling LLM Performance and Generalization
by: Ding, Mucong, et al.
Published: (2024)

Omanic: Towards Step-wise Evaluation of Multi-hop Reasoning in Large Language Models
by: Gu, Xiaojie, et al.
Published: (2026)

RevOrder: A Novel Method for Enhanced Arithmetic in Language Models
by: Shen, Si, et al.
Published: (2024)

A Careful Examination of Large Language Model Performance on Grade School Arithmetic
by: Zhang, Hugh, et al.
Published: (2024)

OccamLLM: Fast and Exact Language Model Arithmetic in a Single Step
by: Dugan, Owen, et al.
Published: (2024)

Task Arithmetic for Language Expansion in Speech Translation
by: Cheng, Yao-Fei, et al.
Published: (2024)

Task Arithmetic can Mitigate Synthetic-to-Real Gap in Automatic Speech Recognition
by: Su, Hsuan, et al.
Published: (2024)

Integrating Arithmetic Learning Improves Mathematical Reasoning in Smaller Models
by: Gangwar, Neeraj, et al.
Published: (2025)

Mathematical Reasoning in Large Language Models: Assessing Logical and Arithmetic Errors across Wide Numerical Ranges
by: Shrestha, Safal, et al.
Published: (2025)

Towards Empirical Interpretation of Internal Circuits and Properties in Grokked Transformers on Modular Polynomials
by: Furuta, Hiroki, et al.
Published: (2024)

Bias Vector: Mitigating Biases in Language Models with Task Arithmetic Approach
by: Shirafuji, Daiki, et al.
Published: (2024)

How Numerical Precision Affects Arithmetical Reasoning Capabilities of LLMs
by: Feng, Guhao, et al.
Published: (2024)

Soft Tokens, Hard Truths
by: Butt, Natasha, et al.
Published: (2025)

Transformers Can Do Arithmetic with the Right Embeddings
by: McLeish, Sean, et al.
Published: (2024)

EH-MAM: Easy-to-Hard Masked Acoustic Modeling for Self-Supervised Speech Representation Learning
by: Seth, Ashish, et al.
Published: (2024)

Large Language Models as Generalizable Policies for Embodied Tasks
by: Szot, Andrew, et al.
Published: (2023)

Divide, Reweight, and Conquer: A Logit Arithmetic Approach for In-Context Learning
by: Huang, Chengsong, et al.
Published: (2024)

(How) Do Language Models Track State?
by: Li, Belinda Z., et al.
Published: (2025)

Language Models Use Trigonometry to Do Addition
by: Kantamneni, Subhash, et al.
Published: (2025)

ConsistentEE: A Consistent and Hardness-Guided Early Exiting Method for Accelerating Language Models Inference
by: Zeng, Ziqian, et al.
Published: (2023)

QED-Nano: Teaching a Tiny Model to Prove Hard Theorems
by: LM-Provers, et al.
Published: (2026)

Beyond Induction Heads: In-Context Meta Learning Induces Multi-Phase Circuit Emergence
by: Minegishi, Gouki, et al.
Published: (2025)

AttributionBench: How Hard is Automatic Attribution Evaluation?
by: Li, Yifei, et al.
Published: (2024)

Verifier-Backed Hard Problem Generation for Mathematical Reasoning
by: Lai, Yuhang, et al.
Published: (2026)

Reuse your FLOPs: Scaling RL on Hard Problems by Conditioning on Very Off-Policy Prefixes
by: Setlur, Amrith, et al.
Published: (2026)

Understanding Emergent Misalignment via Feature Superposition Geometry
by: Minegishi, Gouki, et al.
Published: (2026)

Zipping the Thought: When and How Compressed Reasoning Data Works in LLM Post-Training
by: Matsutani, Kohsei, et al.
Published: (2026)

Do Large Language Models Show Biases in Causal Learning?
by: Carro, Maria Victoria, et al.
Published: (2024)