sail-sg / Cheating-LLM-BenchmarksLinks
[ICLR 2025] Cheating Automatic LLM Benchmarks: Null Models Achieve High Win Rates (Oral)
☆81Updated 10 months ago
Alternatives and similar repositories for Cheating-LLM-Benchmarks
Users that are interested in Cheating-LLM-Benchmarks are comparing it to the libraries listed below
Sorting:
- [ICLR'24] RAIN: Your Language Models Can Align Themselves without Finetuning☆97Updated last year
- Improved Few-Shot Jailbreaking Can Circumvent Aligned Language Models and Their Defenses (NeurIPS 2024)☆64Updated 7 months ago
- A Sober Look at Language Model Reasoning☆81Updated 2 months ago
- ☆31Updated last year
- Code for "Reasoning to Learn from Latent Thoughts"☆116Updated 5 months ago
- Code for safety test in "Keeping LLMs Aligned After Fine-tuning: The Crucial Role of Prompt Templates"☆18Updated last year
- Official code for SEAL: Steerable Reasoning Calibration of Large Language Models for Free☆40Updated 4 months ago
- PaCE: Parsimonious Concept Engineering for Large Language Models (NeurIPS 2024)☆39Updated 9 months ago
- [ACL 2024] Code and data for "Machine Unlearning of Pre-trained Large Language Models"☆59Updated 11 months ago
- ☆59Updated last year
- Code release for "Debating with More Persuasive LLMs Leads to More Truthful Answers"☆114Updated last year
- NeurIPS'24 - LLM Safety Landscape☆28Updated 6 months ago
- Test-time-training on nearest neighbors for large language models☆45Updated last year
- ☆39Updated last year
- ☆36Updated 8 months ago
- An official implementation of "Catastrophic Failure of LLM Unlearning via Quantization" (ICLR 2025)☆29Updated 6 months ago
- ☆30Updated last year
- [ICLR 2025] Official Repository for "Tamper-Resistant Safeguards for Open-Weight LLMs"☆59Updated 2 months ago
- Stanford NLP Python library for benchmarking the utility of LLM interpretability methods☆124Updated 2 months ago
- Does Refusal Training in LLMs Generalize to the Past Tense? [ICLR 2025]☆72Updated 7 months ago
- ☆25Updated 5 months ago
- The official repository of 'Unnatural Language Are Not Bugs but Features for LLMs'☆21Updated 3 months ago
- ☆50Updated last year
- Röttger et al. (NAACL 2024): "XSTest: A Test Suite for Identifying Exaggerated Safety Behaviours in Large Language Models"☆110Updated 6 months ago
- Codebase for decoding compressed trust.☆24Updated last year
- The official repository of the paper "On the Exploitability of Instruction Tuning".☆64Updated last year
- Safe Unlearning: A Surprisingly Effective and Generalizable Solution to Defend Against Jailbreak Attacks☆29Updated last year
- [ICLR 2025] When Attention Sink Emerges in Language Models: An Empirical View (Spotlight)☆118Updated last month
- [ICML 2024] Agent Smith: A Single Image Can Jailbreak One Million Multimodal LLM Agents Exponentially Fast☆112Updated last year
- ☆34Updated 7 months ago