Claude AI tag

Gwern Branwen

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Links
Miscellaneous
Link Bibliography

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Links

“From Words to Numbers: Your Large Language Model Is Secretly A Capable Regressor When Given In-Context Examples”, Vacareanu et al 2024

From Words to Numbers: Your Large Language Model Is Secretly A Capable Regressor When Given In-Context Examples

“VisualWebBench: How Far Have Multimodal LLMs Evolved in Web Page Understanding and Grounding?”, Liu et al 2024

VisualWebBench: How Far Have Multimodal LLMs Evolved in Web Page Understanding and Grounding?

“FABLES: Evaluating Faithfulness and Content Selection in Book-Length Summarization”, Kim et al 2024

FABLES: Evaluating faithfulness and content selection in book-length summarization

“`ArtPrompt`: ASCII Art-Based Jailbreak Attacks against Aligned LLMs”, Jiang et al 2024

ArtPrompt: ASCII Art-based Jailbreak Attacks against Aligned LLMs

“Using Hallucinations to Bypass GPT-4’s Filter”, Lemkin 2024

Using Hallucinations to Bypass GPT-4’s Filter

“Sleeper Agents: Training Deceptive LLMs That Persist Through Safety Training”, Hubinger et al 2024

Sleeper Agents: Training Deceptive LLMs that Persist Through Safety Training

“Summon a Demon and Bind It: A Grounded Theory of LLM Red Teaming in the Wild”, Inie et al 2023

Summon a Demon and Bind it: A Grounded Theory of LLM Red Teaming in the Wild

“Scalable and Transferable Black-Box Jailbreaks for Language Models via Persona Modulation”, Shah et al 2023

Scalable and Transferable Black-Box Jailbreaks for Language Models via Persona Modulation

“Specific versus General Principles for Constitutional AI”, Kundu et al 2023

Specific versus General Principles for Constitutional AI

“PAIR: Jailbreaking Black Box Large Language Models in 20 Queries”, Chao et al 2023

PAIR: Jailbreaking Black Box Large Language Models in 20 Queries

“Beyond Memorization: Violating Privacy Via Inference With Large Language Models”, Staab et al 2023

Beyond Memorization: Violating Privacy Via Inference with Large Language Models

“SWE-Bench: Can Language Models Resolve Real-World GitHub Issues?”, Jimenez et al 2023

SWE-bench: Can Language Models Resolve Real-World GitHub Issues?

“Lost in the Middle: How Language Models Use Long Contexts”, Liu et al 2023

Lost in the Middle: How Language Models Use Long Contexts

“Opportunities and Risks of LLMs for Scalable Deliberation With Polis”, Small et al 2023

Opportunities and Risks of LLMs for Scalable Deliberation with Polis

“Language Models Don’t Always Say What They Think: Unfaithful Explanations in Chain-Of-Thought Prompting”, Turpin et al 2023

Language Models Don’t Always Say What They Think: Unfaithful Explanations in Chain-of-Thought Prompting

“A Radical Plan to Make AI Good, Not Evil”, Knight 2023

A Radical Plan to Make AI Good, Not Evil

“Constitutional AI: Harmlessness from AI Feedback”, Bai et al 2022

Constitutional AI: Harmlessness from AI Feedback

“Red Teaming Language Models to Reduce Harms: Methods, Scaling Behaviors, and Lessons Learned”, Ganguli et al 2022

Red Teaming Language Models to Reduce Harms: Methods, Scaling Behaviors, and Lessons Learned

“A General Language Assistant As a Laboratory for Alignment”, Askell et al 2021

A General Language Assistant as a Laboratory for Alignment

“The Perception of Rhythm in Language”, Cutler 1994

The perception of rhythm in language

Miscellaneous

Link Bibliography

https://arxiv.org/abs/2402.11753: “ArtPrompt: ASCII Art-Based Jailbreak Attacks against Aligned LLMs”, Fengqing Jiang, Zhangchen Xu, Luyao Niu, Zhen Xiang, Bhaskar Ramasubramanian, Bo Li, Radha Poovendran

link-bibliography
https://arxiv.org/abs/2401.05566#anthropic: “Sleeper Agents: Training Deceptive LLMs That Persist Through Safety Training”, Evan Hubinger, Carson Denison, Jesse Mu, Mike Lambert, Meg Tong, Monte MacDiarmid, Tamera Lanham, Daniel M. Ziegler

link-bibliography
https://arxiv.org/abs/2310.08419: “PAIR: Jailbreaking Black Box Large Language Models in 20 Queries”, Patrick Chao, Alexander Robey, Edgar Dobriban, Hamed Hassani, George J. Pappas, Eric Wong

link-bibliography
https://arxiv.org/abs/2305.04388: “Language Models Don’t Always Say What They Think: Unfaithful Explanations in Chain-Of-Thought Prompting”, Miles Turpin, Julian Michael, Ethan Perez, Samuel R. Bowman

link-bibliography
https://www.wired.com/story/anthropic-ai-chatbots-ethics/: “A Radical Plan to Make AI Good, Not Evil”, Will Knight

link-bibliography
https://www.anthropic.com/red_teaming.pdf: “Red Teaming Language Models to Reduce Harms: Methods, Scaling Behaviors, and Lessons Learned”, Deep Ganguli, Liane Lovitt, Jackson Kernion, Amanda Askell, Yuntao Bai, Saurav Kadavath, Ben Mann, Ethan Perez

link-bibliography
https://arxiv.org/abs/2112.00861#anthropic: “A General Language Assistant As a Laboratory for Alignment”, Amanda Askell, Yuntao Bai, Anna Chen, Dawn Drain, Deep Ganguli, Tom Henighan, Andy Jones, Nicholas Joseph

link-bibliography

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