Saved in:
| Main Authors: | Wu, Da, Yang, Jingye, Wang, Kai |
|---|---|
| Format: | Preprint |
| Published: |
2023
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2312.03633 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
The Role of Deductive and Inductive Reasoning in Large Language Models
by: Cai, Chengkun, et al.
Published: (2024)
by: Cai, Chengkun, et al.
Published: (2024)
Mitigating Reversal Curse in Large Language Models via Semantic-aware Permutation Training
by: Guo, Qingyan, et al.
Published: (2024)
by: Guo, Qingyan, et al.
Published: (2024)
JustLogic: A Comprehensive Benchmark for Evaluating Deductive Reasoning in Large Language Models
by: Chen, Michael K., et al.
Published: (2025)
by: Chen, Michael K., et al.
Published: (2025)
An Analysis and Mitigation of the Reversal Curse
by: Lv, Ang, et al.
Published: (2023)
by: Lv, Ang, et al.
Published: (2023)
The Factorization Curse: Which Tokens You Predict Underlie the Reversal Curse and More
by: Kitouni, Ouail, et al.
Published: (2024)
by: Kitouni, Ouail, et al.
Published: (2024)
Disentangling Logic: The Role of Context in Large Language Model Reasoning Capabilities
by: Hua, Wenyue, et al.
Published: (2024)
by: Hua, Wenyue, et al.
Published: (2024)
Training Large Language Models for Reasoning through Reverse Curriculum Reinforcement Learning
by: Xi, Zhiheng, et al.
Published: (2024)
by: Xi, Zhiheng, et al.
Published: (2024)
DivLogicEval: A Framework for Benchmarking Logical Reasoning Evaluation in Large Language Models
by: Chung, Tsz Ting, et al.
Published: (2025)
by: Chung, Tsz Ting, et al.
Published: (2025)
LogicTree: Structured Proof Exploration for Coherent and Rigorous Logical Reasoning with Large Language Models
by: He, Kang, et al.
Published: (2025)
by: He, Kang, et al.
Published: (2025)
The Reversal Curse: LLMs trained on "A is B" fail to learn "B is A"
by: Berglund, Lukas, et al.
Published: (2023)
by: Berglund, Lukas, et al.
Published: (2023)
Single layer tiny Co$^4$ outpaces GPT-2 and GPT-BERT
by: Zain, Noor Ul, et al.
Published: (2025)
by: Zain, Noor Ul, et al.
Published: (2025)
To Think or Not to Think: Exploring the Unthinking Vulnerability in Large Reasoning Models
by: Zhu, Zihao, et al.
Published: (2025)
by: Zhu, Zihao, et al.
Published: (2025)
ArabianGPT: Native Arabic GPT-based Large Language Model
by: Koubaa, Anis, et al.
Published: (2024)
by: Koubaa, Anis, et al.
Published: (2024)
DeduCE: Deductive Consistency as a Framework to Evaluate LLM Reasoning
by: Pandey, Atharva, et al.
Published: (2025)
by: Pandey, Atharva, et al.
Published: (2025)
Patent Language Model Pretraining with ModernBERT
by: Yousefiramandi, Amirhossein, et al.
Published: (2025)
by: Yousefiramandi, Amirhossein, et al.
Published: (2025)
Harnessing Large Language Models: Fine-tuned BERT for Detecting Charismatic Leadership Tactics in Natural Language
by: Saeid, Yasser, et al.
Published: (2024)
by: Saeid, Yasser, et al.
Published: (2024)
Large Language Models Are Better Logical Fallacy Reasoners with Counterargument, Explanation, and Goal-Aware Prompt Formulation
by: Jeong, Jiwon, et al.
Published: (2025)
by: Jeong, Jiwon, 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)
by: Shrestha, Safal, et al.
Published: (2025)
CogGPT: Unleashing the Power of Cognitive Dynamics on Large Language Models
by: Lv, Yaojia, et al.
Published: (2024)
by: Lv, Yaojia, et al.
Published: (2024)
Why Does ChatGPT "Delve" So Much? Exploring the Sources of Lexical Overrepresentation in Large Language Models
by: Juzek, Tom S., et al.
Published: (2024)
by: Juzek, Tom S., et al.
Published: (2024)
AR$^2$: Adversarial Reinforcement Learning for Abstract Reasoning in Large Language Models
by: Yeh, Cheng-Kai, et al.
Published: (2025)
by: Yeh, Cheng-Kai, et al.
Published: (2025)
AgentMath: Empowering Mathematical Reasoning for Large Language Models via Tool-Augmented Agent
by: Luo, Haipeng, et al.
Published: (2025)
by: Luo, Haipeng, et al.
Published: (2025)
The Curse of Depth in Large Language Models
by: Sun, Wenfang, et al.
Published: (2025)
by: Sun, Wenfang, et al.
Published: (2025)
TokUR: Token-Level Uncertainty Estimation for Large Language Model Reasoning
by: Zhang, Tunyu, et al.
Published: (2025)
by: Zhang, Tunyu, et al.
Published: (2025)
RE-Adapt: Reverse Engineered Adaptation of Large Language Models
by: Fleshman, William, et al.
Published: (2024)
by: Fleshman, William, et al.
Published: (2024)
Enhancing Zero-Shot Chain-of-Thought Reasoning in Large Language Models through Logic
by: Zhao, Xufeng, et al.
Published: (2023)
by: Zhao, Xufeng, et al.
Published: (2023)
Training Language Models for Social Deduction with Multi-Agent Reinforcement Learning
by: Sarkar, Bidipta, et al.
Published: (2025)
by: Sarkar, Bidipta, et al.
Published: (2025)
DyVal: Dynamic Evaluation of Large Language Models for Reasoning Tasks
by: Zhu, Kaijie, et al.
Published: (2023)
by: Zhu, Kaijie, et al.
Published: (2023)
CityGPT: Empowering Urban Spatial Cognition of Large Language Models
by: Feng, Jie, et al.
Published: (2024)
by: Feng, Jie, et al.
Published: (2024)
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)
Large Language and Reasoning Models are Shallow Disjunctive Reasoners
by: Khalid, Irtaza, et al.
Published: (2025)
by: Khalid, Irtaza, et al.
Published: (2025)
On the Overscaling Curse of Parallel Thinking: System Efficacy Contradicts Sample Efficiency
by: Wang, Yiming, et al.
Published: (2026)
by: Wang, Yiming, et al.
Published: (2026)
Generative Evaluation of Complex Reasoning in Large Language Models
by: Lin, Haowei, et al.
Published: (2025)
by: Lin, Haowei, et al.
Published: (2025)
Large Language Model Reasoning Failures
by: Song, Peiyang, et al.
Published: (2026)
by: Song, Peiyang, et al.
Published: (2026)
Do Depth-Grown Models Overcome the Curse of Depth? An In-Depth Analysis
by: Kapl, Ferdinand, et al.
Published: (2025)
by: Kapl, Ferdinand, et al.
Published: (2025)
Reinforcement Learning for Reasoning in Large Language Models with One Training Example
by: Wang, Yiping, et al.
Published: (2025)
by: Wang, Yiping, et al.
Published: (2025)
Cancer Diagnosis Categorization in Electronic Health Records Using Large Language Models and BioBERT: Model Performance Evaluation Study
by: Hashtarkhani, Soheil, et al.
Published: (2025)
by: Hashtarkhani, Soheil, et al.
Published: (2025)
Multi-Step Deductive Reasoning Over Natural Language: An Empirical Study on Out-of-Distribution Generalisation
by: Bao, Qiming, et al.
Published: (2022)
by: Bao, Qiming, et al.
Published: (2022)
CausalVLBench: Benchmarking Visual Causal Reasoning in Large Vision-Language Models
by: Komanduri, Aneesh, et al.
Published: (2025)
by: Komanduri, Aneesh, et al.
Published: (2025)
Self-Training Elicits Concise Reasoning in Large Language Models
by: Munkhbat, Tergel, et al.
Published: (2025)
by: Munkhbat, Tergel, et al.
Published: (2025)
Similar Items
-
The Role of Deductive and Inductive Reasoning in Large Language Models
by: Cai, Chengkun, et al.
Published: (2024) -
Mitigating Reversal Curse in Large Language Models via Semantic-aware Permutation Training
by: Guo, Qingyan, et al.
Published: (2024) -
JustLogic: A Comprehensive Benchmark for Evaluating Deductive Reasoning in Large Language Models
by: Chen, Michael K., et al.
Published: (2025) -
An Analysis and Mitigation of the Reversal Curse
by: Lv, Ang, et al.
Published: (2023) -
The Factorization Curse: Which Tokens You Predict Underlie the Reversal Curse and More
by: Kitouni, Ouail, et al.
Published: (2024)