Cover Image

Delving into the Reversal Curse: How Far Can Large Language Models Generalize?

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Lin, Zhengkai, Fu, Zhihang, Liu, Kai, Xie, Liang, Lin, Binbin, Wang, Wenxiao, Cai, Deng, Wu, Yue, Ye, Jieping
Format:	Preprint
Published:	2024
Subjects:	Computation and Language
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Controlling Thinking Speed in Reasoning Models
by: Lin, Zhengkai, et al.
Published: (2025)

Model Compression and Efficient Inference for Large Language Models: A Survey
by: Wang, Wenxiao, et al.
Published: (2024)

From Yes-Men to Truth-Tellers: Addressing Sycophancy in Large Language Models with Pinpoint Tuning
by: Chen, Wei, et al.
Published: (2024)

Structure-aware Domain Knowledge Injection for Large Language Models
by: Liu, Kai, et al.
Published: (2024)

Don't Take Things Out of Context: Attention Intervention for Enhancing Chain-of-Thought Reasoning in Large Language Models
by: Yan, Shaotian, et al.
Published: (2025)

ROPO: Robust Preference Optimization for Large Language Models
by: Liang, Xize, et al.
Published: (2024)

Concise and Organized Perception Facilitates Reasoning in Large Language Models
by: Liu, Junjie, et al.
Published: (2023)

Enhancing Chain-of-Thought Reasoning with Critical Representation Fine-tuning
by: Huang, Chenxi, et al.
Published: (2025)

Exploring the Reversal Curse and Other Deductive Logical Reasoning in BERT and GPT-Based Large Language Models
by: Wu, Da, et al.
Published: (2023)

INSIDE: LLMs' Internal States Retain the Power of Hallucination Detection
by: Chen, Chao, et al.
Published: (2024)

Enhancing Multiple Dimensions of Trustworthiness in LLMs via Sparse Activation Control
by: Xiao, Yuxin, et al.
Published: (2024)

SciPIP: An LLM-based Scientific Paper Idea Proposer
by: Wang, Wenxiao, et al.
Published: (2024)

An Analysis and Mitigation of the Reversal Curse
by: Lv, Ang, et al.
Published: (2023)

Enhancing LLM's Cognition via Structurization
by: Liu, Kai, et al.
Published: (2024)

Reverse Training to Nurse the Reversal Curse
by: Golovneva, Olga, et al.
Published: (2024)

How Far Can We Extract Diverse Perspectives from Large Language Models?
by: Hayati, Shirley Anugrah, et al.
Published: (2023)

The Illusion of Latent Generalization: Bi-directionality and the Reversal Curse
by: Coda-Forno, Julian, et al.
Published: (2026)

Evaluating the Reversal Curse in Model Editing
by: Xu, Hao-Xiang, et al.
Published: (2023)

DiffER: Diffusion Entity-Relation Modeling for Reversal Curse in Diffusion Large Language Models
by: He, Shaokai, et al.
Published: (2026)

GeoCAD: Local Geometry-Controllable CAD Generation with Large Language Models
by: Zhang, Zhanwei, et al.
Published: (2025)

Mitigating Reversal Curse in Large Language Models via Semantic-aware Permutation Training
by: Guo, Qingyan, et al.
Published: (2024)

The Factorization Curse: Which Tokens You Predict Underlie the Reversal Curse and More
by: Kitouni, Ouail, et al.
Published: (2024)

Instance-adaptive Zero-shot Chain-of-Thought Prompting
by: Yuan, Xiaosong, et al.
Published: (2024)

ROUTE: Robust Multitask Tuning and Collaboration for Text-to-SQL
by: Qin, Yang, et al.
Published: (2024)

R-Horizon: How Far Can Your Large Reasoning Model Really Go in Breadth and Depth?
by: Lu, Yi, et al.
Published: (2025)

Large Language Models for Predictive Analysis: How Far Are They?
by: Chen, Qin, et al.
Published: (2025)

InsQABench: Benchmarking Chinese Insurance Domain Question Answering with Large Language Models
by: Ding, Jing, et al.
Published: (2025)

Multilingual Large Language Models and Curse of Multilinguality
by: Gurgurov, Daniil, et al.
Published: (2024)

How Far Can Unsupervised RLVR Scale LLM Training?
by: He, Bingxiang, et al.
Published: (2026)

Break the Chain: Large Language Models Can be Shortcut Reasoners
by: Ding, Mengru, et al.
Published: (2024)

MotiveBench: How Far Are We From Human-Like Motivational Reasoning in Large Language Models?
by: Yong, Xixian, et al.
Published: (2025)

Deceptive Semantic Shortcuts on Reasoning Chains: How Far Can Models Go without Hallucination?
by: Li, Bangzheng, et al.
Published: (2023)

Delving into Multilingual Ethical Bias: The MSQAD with Statistical Hypothesis Tests for Large Language Models
by: Yu, Seunguk, et al.
Published: (2025)

Improving Complex Reasoning with Dynamic Prompt Corruption: A soft prompt Optimization Approach
by: Fan, Sinan, et al.
Published: (2025)

Vulnerability Detection with Code Language Models: How Far Are We?
by: Ding, Yangruibo, et al.
Published: (2024)

Memory Tokens: Large Language Models Can Generate Reversible Sentence Embeddings
by: Sastre, Ignacio, et al.
Published: (2025)

Towards a Theoretical Understanding of the 'Reversal Curse' via Training Dynamics
by: Zhu, Hanlin, et al.
Published: (2024)

OS-MAP: How Far Can Computer-Using Agents Go in Breadth and Depth?
by: Chen, Xuetian, et al.
Published: (2025)

LLM Compression: How Far Can We Go in Balancing Size and Performance?
by: Sk, Sahil, et al.
Published: (2025)

Additive Multi-Step Markov Chains and the Curse of Dimensionality in Large Language Models
by: Usatenko, O. V., et al.
Published: (2026)