Can LLMs Follow Simple Rules?
Fuente:
arXiv
Saved in:
| Main Authors: | Mu, Norman, Chen, Sarah, Wang, Zifan, Chen, Sizhe, Karamardian, David, Aljeraisy, Lulwa, Alomair, Basel, Hendrycks, Dan, Wagner, David |
|---|---|
| Format: | Preprint |
| Published: |
2023
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Jatmo: Prompt Injection Defense by Task-Specific Finetuning
by: Piet, Julien, et al.
Published: (2023)
by: Piet, Julien, et al.
Published: (2023)
Can LLMs Ask Good Questions?
by: Zhang, Yueheng, et al.
Published: (2025)
by: Zhang, Yueheng, et al.
Published: (2025)
HarmBench: A Standardized Evaluation Framework for Automated Red Teaming and Robust Refusal
by: Mazeika, Mantas, et al.
Published: (2024)
by: Mazeika, Mantas, et al.
Published: (2024)
PAL: Proxy-Guided Black-Box Attack on Large Language Models
by: Sitawarin, Chawin, et al.
Published: (2024)
by: Sitawarin, Chawin, et al.
Published: (2024)
Generative AI Security: Challenges and Countermeasures
by: Zhu, Banghua, et al.
Published: (2024)
by: Zhu, Banghua, et al.
Published: (2024)
TextQuests: How Good are LLMs at Text-Based Video Games?
by: Phan, Long, et al.
Published: (2025)
by: Phan, Long, et al.
Published: (2025)
Representing Rule-based Chatbots with Transformers
by: Friedman, Dan, et al.
Published: (2024)
by: Friedman, Dan, et al.
Published: (2024)
Introduction to AI Safety, Ethics, and Society
by: Hendrycks, Dan
Published: (2024)
by: Hendrycks, Dan
Published: (2024)
Enough Coin Flips Can Make LLMs Act Bayesian
by: Gupta, Ritwik, et al.
Published: (2025)
by: Gupta, Ritwik, et al.
Published: (2025)
Do LLMs Follow Their Own Rules? A Reflexive Audit of Self-Stated Safety Policies
by: Mittal, Avni
Published: (2026)
by: Mittal, Avni
Published: (2026)
A Closer Look at System Prompt Robustness
by: Mu, Norman, et al.
Published: (2025)
by: Mu, Norman, et al.
Published: (2025)
Can Post-Training Transform LLMs into Causal Reasoners?
by: Chen, Junqi, et al.
Published: (2026)
by: Chen, Junqi, et al.
Published: (2026)
Can LLMs Speak For Diverse People? Tuning LLMs via Debate to Generate Controllable Controversial Statements
by: Li, Ming, et al.
Published: (2024)
by: Li, Ming, et al.
Published: (2024)
Tamper-Resistant Safeguards for Open-Weight LLMs
by: Tamirisa, Rishub, et al.
Published: (2024)
by: Tamirisa, Rishub, et al.
Published: (2024)
Can GRPO Help LLMs Transcend Their Pretraining Origin?
by: Ni, Kangqi, et al.
Published: (2025)
by: Ni, Kangqi, et al.
Published: (2025)
How Do LLMs Persuade? Linear Probes Can Uncover Persuasion Dynamics in Multi-Turn Conversations
by: Jaipersaud, Brandon, et al.
Published: (2025)
by: Jaipersaud, Brandon, et al.
Published: (2025)
Can We Count on LLMs? The Fixed-Effect Fallacy and Claims of GPT-4 Capabilities
by: Ball, Thomas, et al.
Published: (2024)
by: Ball, Thomas, et al.
Published: (2024)
Uncovering Latent Human Wellbeing in Language Model Embeddings
by: Freire, Pedro, et al.
Published: (2024)
by: Freire, Pedro, et al.
Published: (2024)
Frontier LLMs Still Struggle with Simple Reasoning Tasks
by: Malek, Alan, et al.
Published: (2025)
by: Malek, Alan, et al.
Published: (2025)
Is In-Context Learning Sufficient for Instruction Following in LLMs?
by: Zhao, Hao, et al.
Published: (2024)
by: Zhao, Hao, et al.
Published: (2024)
From Language Modeling to Instruction Following: Understanding the Behavior Shift in LLMs after Instruction Tuning
by: Wu, Xuansheng, et al.
Published: (2023)
by: Wu, Xuansheng, et al.
Published: (2023)
RuleR: Improving LLM Controllability by Rule-based Data Recycling
by: Li, Ming, et al.
Published: (2024)
by: Li, Ming, et al.
Published: (2024)
Can LLMs Correct Themselves? A Benchmark of Self-Correction in LLMs
by: Tie, Guiyao, et al.
Published: (2025)
by: Tie, Guiyao, et al.
Published: (2025)
Assessing Episodic Memory in LLMs with Sequence Order Recall Tasks
by: Pink, Mathis, et al.
Published: (2024)
by: Pink, Mathis, et al.
Published: (2024)
Mark My Words: Analyzing and Evaluating Language Model Watermarks
by: Piet, Julien, et al.
Published: (2023)
by: Piet, Julien, et al.
Published: (2023)
Climbing the Ladder of Reasoning: What LLMs Can-and Still Can't-Solve after SFT?
by: Sun, Yiyou, et al.
Published: (2025)
by: Sun, Yiyou, et al.
Published: (2025)
Utility Engineering: Analyzing and Controlling Emergent Value Systems in AIs
by: Mazeika, Mantas, et al.
Published: (2025)
by: Mazeika, Mantas, et al.
Published: (2025)
Can LLMs Convert Graphs to Text-Attributed Graphs?
by: Wang, Zehong, et al.
Published: (2024)
by: Wang, Zehong, et al.
Published: (2024)
dLLM: Simple Diffusion Language Modeling
by: Zhou, Zhanhui, et al.
Published: (2026)
by: Zhou, Zhanhui, et al.
Published: (2026)
Beyond Instruction Following: Evaluating Inferential Rule Following of Large Language Models
by: Sun, Wangtao, et al.
Published: (2024)
by: Sun, Wangtao, et al.
Published: (2024)
AxBench: Steering LLMs? Even Simple Baselines Outperform Sparse Autoencoders
by: Wu, Zhengxuan, et al.
Published: (2025)
by: Wu, Zhengxuan, et al.
Published: (2025)
Can LLMs Help Uncover Insights about LLMs? A Large-Scale, Evolving Literature Analysis of Frontier LLMs
by: Park, Jungsoo, et al.
Published: (2025)
by: Park, Jungsoo, et al.
Published: (2025)
Adaptive Inference-Time Compute: LLMs Can Predict if They Can Do Better, Even Mid-Generation
by: Manvi, Rohin, et al.
Published: (2024)
by: Manvi, Rohin, et al.
Published: (2024)
A Rule Based Solution to Co-reference Resolution in Clinical Text
by: Chen, Ping, et al.
Published: (2025)
by: Chen, Ping, et al.
Published: (2025)
SelfReflect: Can LLMs Communicate Their Internal Answer Distribution?
by: Kirchhof, Michael, et al.
Published: (2025)
by: Kirchhof, Michael, et al.
Published: (2025)
De Jure: Iterative LLM Self-Refinement for Structured Extraction of Regulatory Rules
by: Guliani, Keerat, et al.
Published: (2026)
by: Guliani, Keerat, et al.
Published: (2026)
Can AI Freelancers Compete? Benchmarking Earnings, Reliability, and Task Success at Scale
by: Noever, David, et al.
Published: (2025)
by: Noever, David, et al.
Published: (2025)
Rule by Rule: Learning with Confidence through Vocabulary Expansion
by: Nössig, Albert, et al.
Published: (2024)
by: Nössig, Albert, et al.
Published: (2024)
It's Not That Simple. An Analysis of Simple Test-Time Scaling
by: Wu, Guojun
Published: (2025)
by: Wu, Guojun
Published: (2025)
Light-IF: Endowing LLMs with Generalizable Reasoning via Preview and Self-Checking for Complex Instruction Following
by: Wang, Chenyang, et al.
Published: (2025)
by: Wang, Chenyang, et al.
Published: (2025)
Similar Items
-
Jatmo: Prompt Injection Defense by Task-Specific Finetuning
by: Piet, Julien, et al.
Published: (2023) -
Can LLMs Ask Good Questions?
by: Zhang, Yueheng, et al.
Published: (2025) -
HarmBench: A Standardized Evaluation Framework for Automated Red Teaming and Robust Refusal
by: Mazeika, Mantas, et al.
Published: (2024) -
PAL: Proxy-Guided Black-Box Attack on Large Language Models
by: Sitawarin, Chawin, et al.
Published: (2024) -
Generative AI Security: Challenges and Countermeasures
by: Zhu, Banghua, et al.
Published: (2024)