Data of the "Leaky Thoughts: Large Reasoning Models Are Not Private Thinkers" paper
AI & ML interests
LLM, trustworthy AI, AI security, privacy, calibration, hallucination
Recent Activity
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NAACL 2025 Findings "Scaling Up Membership Inference: When and How Attacks Succeed on Large Language Models" https://arxiv.org/abs/2411.00154
List of research articles of Parameter Lab
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Leaky Thoughts: Large Reasoning Models Are Not Private Thinkers
Paper • 2506.15674 • Published -
C-SEO Bench: Does Conversational SEO Work?
Paper • 2506.11097 • Published -
Scaling Up Membership Inference: When and How Attacks Succeed on Large Language Models
Paper • 2411.00154 • Published -
TRAP: Targeted Random Adversarial Prompt Honeypot for Black-Box Identification
Paper • 2402.12991 • Published
Fine-tuned models for black-box LLM calibration, trained for "Apricot: Calibrating Large Language Models Using Their Generations Only" (ACL 2024)
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parameterlab/apricot_binary_trivia_qa_deberta-v3-base_for_vicuna-7b-v1.5
Text Classification • 0.2B • Updated • 13 • 1 -
parameterlab/apricot_clustering_trivia_qa_deberta-v3-base_for_vicuna-7b-v1.5
Text Classification • 0.2B • Updated • 12 -
parameterlab/apricot_binary_coqa_deberta-v3-base_for_vicuna-7b-v1.5
Text Classification • 0.2B • Updated • 13 -
parameterlab/apricot_clustering_coqa_deberta-v3-base_for_vicuna-7b-v1.5
Text Classification • 0.2B • Updated • 12
Data of the "Leaky Thoughts: Large Reasoning Models Are Not Private Thinkers" paper
List of research articles of Parameter Lab
-
Leaky Thoughts: Large Reasoning Models Are Not Private Thinkers
Paper • 2506.15674 • Published -
C-SEO Bench: Does Conversational SEO Work?
Paper • 2506.11097 • Published -
Scaling Up Membership Inference: When and How Attacks Succeed on Large Language Models
Paper • 2411.00154 • Published -
TRAP: Targeted Random Adversarial Prompt Honeypot for Black-Box Identification
Paper • 2402.12991 • Published
NAACL 2025 Findings "Scaling Up Membership Inference: When and How Attacks Succeed on Large Language Models" https://arxiv.org/abs/2411.00154
Fine-tuned models for black-box LLM calibration, trained for "Apricot: Calibrating Large Language Models Using Their Generations Only" (ACL 2024)
-
parameterlab/apricot_binary_trivia_qa_deberta-v3-base_for_vicuna-7b-v1.5
Text Classification • 0.2B • Updated • 13 • 1 -
parameterlab/apricot_clustering_trivia_qa_deberta-v3-base_for_vicuna-7b-v1.5
Text Classification • 0.2B • Updated • 12 -
parameterlab/apricot_binary_coqa_deberta-v3-base_for_vicuna-7b-v1.5
Text Classification • 0.2B • Updated • 13 -
parameterlab/apricot_clustering_coqa_deberta-v3-base_for_vicuna-7b-v1.5
Text Classification • 0.2B • Updated • 12