Saved in:
| Main Authors: | Soto, Rafael Rivera, Chen, Barry, Andrews, Nicholas |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2505.14608 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Mitigating Paraphrase Attacks on Machine-Text Detectors via Paraphrase Inversion
by: Soto, Rafael Rivera, et al.
Published: (2024)
by: Soto, Rafael Rivera, et al.
Published: (2024)
Few-Shot Detection of Machine-Generated Text using Style Representations
by: Soto, Rafael Rivera, et al.
Published: (2024)
by: Soto, Rafael Rivera, et al.
Published: (2024)
Too Big to Fool: Resisting Deception in Language Models
by: Samsami, Mohammad Reza, et al.
Published: (2024)
by: Samsami, Mohammad Reza, et al.
Published: (2024)
Still "Talking About Large Language Models": Some Clarifications
by: Shanahan, Murray
Published: (2024)
by: Shanahan, Murray
Published: (2024)
Stress-testing Machine Generated Text Detection: Shifting Language Models Writing Style to Fool Detectors
by: Pedrotti, Andrea, et al.
Published: (2025)
by: Pedrotti, Andrea, et al.
Published: (2025)
SequentialBreak: Large Language Models Can be Fooled by Embedding Jailbreak Prompts into Sequential Prompt Chains
by: Saiem, Bijoy Ahmed, et al.
Published: (2024)
by: Saiem, Bijoy Ahmed, et al.
Published: (2024)
TACO: Adversarial Camouflage Optimization on Trucks to Fool Object Detectors
by: Dimitriu, Adonisz, et al.
Published: (2024)
by: Dimitriu, Adonisz, et al.
Published: (2024)
Can Large Language Models Still Explain Themselves? Investigating the Impact of Quantization on Self-Explanations
by: Wang, Qianli, et al.
Published: (2026)
by: Wang, Qianli, et al.
Published: (2026)
Aioli: A Unified Optimization Framework for Language Model Data Mixing
by: Chen, Mayee F., et al.
Published: (2024)
by: Chen, Mayee F., et al.
Published: (2024)
Direct Preference Optimization: Your Language Model is Secretly a Reward Model
by: Rafailov, Rafael, et al.
Published: (2023)
by: Rafailov, Rafael, et al.
Published: (2023)
StyleBench: Evaluating thinking styles in Large Language Models
by: Guo, Junyu, et al.
Published: (2025)
by: Guo, Junyu, et al.
Published: (2025)
Do Large Language Models Have Compositional Ability? An Investigation into Limitations and Scalability
by: Xu, Zhuoyan, et al.
Published: (2024)
by: Xu, Zhuoyan, et al.
Published: (2024)
Having Beer after Prayer? Measuring Cultural Bias in Large Language Models
by: Naous, Tarek, et al.
Published: (2023)
by: Naous, Tarek, et al.
Published: (2023)
Evaluating Gender Bias Transfer between Pre-trained and Prompt-Adapted Language Models
by: Mackraz, Natalie, et al.
Published: (2024)
by: Mackraz, Natalie, et al.
Published: (2024)
A Mousetrap: Fooling Large Reasoning Models for Jailbreak with Chain of Iterative Chaos
by: Yao, Yang, et al.
Published: (2025)
by: Yao, Yang, et al.
Published: (2025)
Do Language Models Have Bayesian Brains? Distinguishing Stochastic and Deterministic Decision Patterns within Large Language Models
by: Cui, Andrea Yaoyun, et al.
Published: (2025)
by: Cui, Andrea Yaoyun, et al.
Published: (2025)
Small Models Are (Still) Effective Cross-Domain Argument Extractors
by: Gantt, William, et al.
Published: (2024)
by: Gantt, William, et al.
Published: (2024)
What is it for a Machine Learning Model to Have a Capability?
by: Harding, Jacqueline, et al.
Published: (2024)
by: Harding, Jacqueline, et al.
Published: (2024)
Your Finetuned Large Language Model is Already a Powerful Out-of-distribution Detector
by: Zhang, Andi, et al.
Published: (2024)
by: Zhang, Andi, et al.
Published: (2024)
Kakugo: Distillation of Low-Resource Languages into Small Language Models
by: Devine, Peter, et al.
Published: (2026)
by: Devine, Peter, et al.
Published: (2026)
Large Language Models as Optimizers
by: Yang, Chengrun, et al.
Published: (2023)
by: Yang, Chengrun, et al.
Published: (2023)
Frontier LLMs Still Struggle with Simple Reasoning Tasks
by: Malek, Alan, et al.
Published: (2025)
by: Malek, Alan, et al.
Published: (2025)
Prompt Optimization Via Diffusion Language Models
by: Wang, Shiyu, et al.
Published: (2026)
by: Wang, Shiyu, et al.
Published: (2026)
Base Models Look Human To AI Detectors
by: Xu, Yixuan Even, et al.
Published: (2026)
by: Xu, Yixuan Even, et al.
Published: (2026)
ELSA: A Style Aligned Dataset for Emotionally Intelligent Language Generation
by: Gandhi, Vishal, et al.
Published: (2025)
by: Gandhi, Vishal, et al.
Published: (2025)
(How) Do Language Models Track State?
by: Li, Belinda Z., et al.
Published: (2025)
by: Li, Belinda Z., et al.
Published: (2025)
Steering into New Embedding Spaces: Analyzing Cross-Lingual Alignment Induced by Model Interventions in Multilingual Language Models
by: Sundar, Anirudh, et al.
Published: (2025)
by: Sundar, Anirudh, et al.
Published: (2025)
Capturing Sparks of Abstraction for the ARC Challenge
by: Andrews, Martin
Published: (2024)
by: Andrews, Martin
Published: (2024)
AfroBench: How Good are Large Language Models on African Languages?
by: Ojo, Jessica, et al.
Published: (2023)
by: Ojo, Jessica, et al.
Published: (2023)
CORM: Cache Optimization with Recent Message for Large Language Model Inference
by: Dai, Jincheng, et al.
Published: (2024)
by: Dai, Jincheng, et al.
Published: (2024)
StyleRec: A Benchmark Dataset for Prompt Recovery in Writing Style Transformation
by: Liu, Shenyang, et al.
Published: (2025)
by: Liu, Shenyang, et al.
Published: (2025)
ROPO: Robust Preference Optimization for Large Language Models
by: Liang, Xize, et al.
Published: (2024)
by: Liang, Xize, et al.
Published: (2024)
Words or Vision: Do Vision-Language Models Have Blind Faith in Text?
by: Deng, Ailin, et al.
Published: (2025)
by: Deng, Ailin, et al.
Published: (2025)
How do Language Models Bind Entities in Context?
by: Feng, Jiahai, et al.
Published: (2023)
by: Feng, Jiahai, et al.
Published: (2023)
SLOT: Sample-specific Language Model Optimization at Test-time
by: Hu, Yang, et al.
Published: (2025)
by: Hu, Yang, et al.
Published: (2025)
Extracting and Encoding: Leveraging Large Language Models and Medical Knowledge to Enhance Radiological Text Representation
by: Messina, Pablo, et al.
Published: (2024)
by: Messina, Pablo, et al.
Published: (2024)
How Bad is Training on Synthetic Data? A Statistical Analysis of Language Model Collapse
by: Seddik, Mohamed El Amine, et al.
Published: (2024)
by: Seddik, Mohamed El Amine, et al.
Published: (2024)
Do Large Language Models Know How Much They Know?
by: Prato, Gabriele, et al.
Published: (2025)
by: Prato, Gabriele, et al.
Published: (2025)
Unfamiliar Finetuning Examples Control How Language Models Hallucinate
by: Kang, Katie, et al.
Published: (2024)
by: Kang, Katie, et al.
Published: (2024)
How Important Is Tokenization in French Medical Masked Language Models?
by: Labrak, Yanis, et al.
Published: (2024)
by: Labrak, Yanis, et al.
Published: (2024)
Similar Items
-
Mitigating Paraphrase Attacks on Machine-Text Detectors via Paraphrase Inversion
by: Soto, Rafael Rivera, et al.
Published: (2024) -
Few-Shot Detection of Machine-Generated Text using Style Representations
by: Soto, Rafael Rivera, et al.
Published: (2024) -
Too Big to Fool: Resisting Deception in Language Models
by: Samsami, Mohammad Reza, et al.
Published: (2024) -
Still "Talking About Large Language Models": Some Clarifications
by: Shanahan, Murray
Published: (2024) -
Stress-testing Machine Generated Text Detection: Shifting Language Models Writing Style to Fool Detectors
by: Pedrotti, Andrea, et al.
Published: (2025)