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Long Phan

Center for AI Safety

15 papers hereh-index 123.6k citations20 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • first author4
  • middle author9
  • last author1

Across the 14 of 15 papers where every author was matched, so the position is known.

fields
  • cs.CL10
  • cs.LG4
  • cs.AI1
affiliations
  • Center for AI Safety
Homepage
same name
  • Long Phan — 11 papers, h 10
  • Long Phan — 1 paper

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20202024
most citedSciFive: a text-to-text transformer model for biomedical literature

94 citations · 230 across the 14 of their papers we have counts for

collaborators
Showing cs.LGShow all

4 papers · 1 filter

cs.LG2024★ 7 cited

Improving Alignment and Robustness with Circuit Breakers

Andy Zou, Long Phan, Justin Wang +7

AI systems can take harmful actions and are highly vulnerable to adversarial attacks. We present an approach, inspired by recent advances in representation engineering, that interr…

cs.LG2024★ 13 cited

The WMDP Benchmark: Measuring and Reducing Malicious Use With Unlearning

Nathaniel Li, Alexander Pan, Anjali Gopal +54

The White House Executive Order on Artificial Intelligence highlights the risks of large language models (LLMs) empowering malicious actors in developing biological, cyber, and che…

cs.LG2024★ 23 cited

HarmBench: A Standardized Evaluation Framework for Automated Red Teaming and Robust Refusal

Mantas Mazeika, Long Phan, Xuwang Yin +9

Automated red teaming holds substantial promise for uncovering and mitigating the risks associated with the malicious use of large language models (LLMs), yet the field lacks a sta…

cs.LG2023★ 65 cited

Representation Engineering: A Top-Down Approach to AI Transparency

Andy Zou, Long Phan, Sarah Chen +18

In this paper, we identify and characterize the emerging area of representation engineering (RepE), an approach to enhancing the transparency of AI systems that draws on insights f…

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