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References (42)

[1]
PolBiX: Detecting LLMs' Political Bias in Fact-Checking through X-phemisms
2025Charlott Jakob, David Harbecke et al.
[2]
Probing the Geometry of Truth: Consistency and Generalization of Truth Directions in LLMs Across Logical Transformations and Question Answering Tasks
2025Yuntai Bao, Xuhong Zhang et al.
[3]
Monitoring Reasoning Models for Misbehavior and the Risks of Promoting Obfuscation
2025Bowen Baker, Joost Huizinga et al.
[4]
Critique Fine-Tuning: Learning to Critique is More Effective than Learning to Imitate
2025Yubo Wang, Xiang Yue et al.
[5]
I Don't Know: Explicit Modeling of Uncertainty with an [IDK] Token
2024Roi Cohen, Konstantin Dobler et al.
[6]
Sycophancy in Large Language Models: Causes and Mitigations
2024Lars Malmqvist
[7]
Language Models Learn to Mislead Humans via RLHF
2024Jiaxin Wen, Ruiqi Zhong et al.
[8]
On the Relationship between Truth and Political Bias in Language Models
2024S. Fulay, William Brannon et al.
[9]
BERTGuard: Two-Tiered Multi-Domain Fake News Detection with Class Imbalance Mitigation
2024Mohammad Q. Alnabhan, Paula Branco
[10]
The Llama 3 Herd of Models
2024Abhimanyu Dubey, Abhinav Jauhri et al.
[11]
Measuring Political Bias in Large Language Models: What Is Said and How It Is Said
2024Yejin Bang, Delong Chen et al.
[12]
Easy-to-Hard Generalization: Scalable Alignment Beyond Human Supervision
2024Zhiqing Sun, Longhui Yu et al.
[13]
The Unreasonable Effectiveness of Easy Training Data for Hard Tasks
2024Peter Hase, Mohit Bansal et al.
[14]
Truth Forest: Toward Multi-Scale Truthfulness in Large Language Models through Intervention without Tuning
2023Zhongzhi Chen, Xingwu Sun et al.
[15]
AI Control: Improving Safety Despite Intentional Subversion
2023R. Greenblatt, Buck Shlegeris et al.
[16]
Eliciting Latent Knowledge from Quirky Language Models
2023Alex Troy Mallen, Nora Belrose
[17]
Scheming AIs: Will AIs fake alignment during training in order to get power?
2023Joe Carlsmith
[18]
A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questions
2023Lei Huang, Weijiang Yu et al.
[19]
Towards Understanding Sycophancy in Language Models
2023Mrinank Sharma, Meg Tong et al.
[20]
The Geometry of Truth: Emergent Linear Structure in Large Language Model Representations of True/False Datasets
2023Samuel Marks, Max Tegmark

Showing 20 of 42 references

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