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References (24)

[1]
Narrow Finetuning Leaves Clearly Readable Traces in Activation Differences
2025Julian Minder, Clément Dumas et al.
[2]
Persona Vectors: Monitoring and Controlling Character Traits in Language Models
2025Runjin Chen, Andy Arditi et al.
[3]
Refusal in Language Models Is Mediated by a Single Direction
2024Andy Arditi, Oscar Obeso et al.
[4]
AI Sandbagging: Language Models can Strategically Underperform on Evaluations
2024Teun van der Weij, Felix Hofstätter et al.
[5]
LESS: Selecting Influential Data for Targeted Instruction Tuning
2024Mengzhou Xia, Sadhika Malladi et al.
[6]
Sleeper Agents: Training Deceptive LLMs that Persist Through Safety Training
2024Evan Hubinger, Carson E. Denison et al.
[7]
Instruction-Following Evaluation for Large Language Models
2023Jeffrey Zhou, Tianjian Lu et al.
[8]
The Linear Representation Hypothesis and the Geometry of Large Language Models
2023Kiho Park, Yo Joong Choe et al.
[9]
Representation Engineering: A Top-Down Approach to AI Transparency
2023Andy Zou, Long Phan et al.
[10]
LMSYS-Chat-1M: A Large-Scale Real-World LLM Conversation Dataset
2023Lianmin Zheng, Wei-Lin Chiang et al.
[11]
Steering Language Models With Activation Engineering
2023Alexander Matt Turner, Lisa Thiergart et al.
[12]
XSTest: A Test Suite for Identifying Exaggerated Safety Behaviours in Large Language Models
2023Paul Röttger, Hannah Rose Kirk et al.
[13]
Jailbroken: How Does LLM Safety Training Fail?
2023Alexander Wei, Nika Haghtalab et al.
[14]
Direct Preference Optimization: Your Language Model is Secretly a Reward Model
2023Rafael Rafailov, Archit Sharma et al.
[15]
Discovering Language Model Behaviors with Model-Written Evaluations
2022Ethan Perez, Sam Ringer et al.
[16]
Constitutional AI: Harmlessness from AI Feedback
2022Yuntao Bai, Saurav Kadavath et al.
[17]
Toy Models of Superposition
2022Nelson Elhage, Tristan Hume et al.
[18]
Training language models to follow instructions with human feedback
2022Long Ouyang, Jeff Wu et al.
[19]
Datamodels: Understanding Predictions with Data and Data with Predictions
2022Andrew Ilyas, Sung Min Park et al.
[20]
Training Verifiers to Solve Math Word Problems
2021K. Cobbe, Vineet Kosaraju et al.

Showing 20 of 24 references

Founder's Pitch

"Develop a tool for tracing and mitigating emergent behaviors in LLMs by identifying responsible training data using activation-based data attribution."

LLM SafetyScore: 5View PDF ↗

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10

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1/4 signals

2.5

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