declare-lab / safety-arithmeticLinks
☆12Updated 8 months ago
Alternatives and similar repositories for safety-arithmetic
Users that are interested in safety-arithmetic are comparing it to the libraries listed below
Sorting:
- Code associated with Tuning Language Models by Proxy (Liu et al., 2024)☆118Updated last year
- Official code for SEAL: Steerable Reasoning Calibration of Large Language Models for Free☆40Updated 5 months ago
- Official code for ICML 2024 paper on Persona In-Context Learning (PICLe)☆26Updated last year
- DELLA-Merging: Reducing Interference in Model Merging through Magnitude-Based Sampling☆35Updated last year
- ☆41Updated 11 months ago
- [ICLR'24] RAIN: Your Language Models Can Align Themselves without Finetuning☆97Updated last year
- [ICML 2025] Weak-to-Strong Jailbreaking on Large Language Models☆85Updated 4 months ago
- In-Context Sharpness as Alerts: An Inner Representation Perspective for Hallucination Mitigation (ICML 2024)☆61Updated last year
- [NAACL'25 Oral] Steering Knowledge Selection Behaviours in LLMs via SAE-Based Representation Engineering☆63Updated 9 months ago
- Official code for Guiding Language Model Math Reasoning with Planning Tokens☆15Updated last year
- [NeurIPS 2023] Github repository for "Composing Parameter-Efficient Modules with Arithmetic Operations"☆61Updated last year
- ☆38Updated last year
- ☆34Updated last year
- ☆30Updated last year
- [ACL 2024] Code and data for "Machine Unlearning of Pre-trained Large Language Models"☆60Updated 11 months ago
- [NeurIPS 2024 D&B] Evaluating Copyright Takedown Methods for Language Models☆17Updated last year
- ☆11Updated 3 weeks ago
- Codes for Merging Large Language Models☆33Updated last year
- Restore safety in fine-tuned language models through task arithmetic☆28Updated last year
- Code & Data for our Paper "Alleviating Hallucinations of Large Language Models through Induced Hallucinations"☆70Updated last year
- ☆60Updated last year
- Semi-Parametric Editing with a Retrieval-Augmented Counterfactual Model☆68Updated 2 years ago
- ☆63Updated 6 months ago
- Our research proposes a novel MoGU framework that improves LLMs' safety while preserving their usability.☆16Updated 8 months ago
- Röttger et al. (NAACL 2024): "XSTest: A Test Suite for Identifying Exaggerated Safety Behaviours in Large Language Models"☆112Updated 6 months ago
- [EMNLP 2025 Main] ConceptVectors Benchmark and Code for the paper "Intrinsic Evaluation of Unlearning Using Parametric Knowledge Traces"☆35Updated last month
- [ICML 2024] Assessing the Brittleness of Safety Alignment via Pruning and Low-Rank Modifications☆84Updated 5 months ago
- "Simplicity Prevails: Rethinking Negative Preference Optimization for LLM Unlearning" by Chongyu Fan*, Jiancheng Liu*, Licong Lin*, Jingh…☆32Updated 3 months ago
- Personalized Steering of Large Language Models: Versatile Steering Vectors Through Bi-directional Preference Optimization☆31Updated last year
- ICLR2024 Paper. Showing properties of safety tuning and exaggerated safety.☆87Updated last year