EmpathYang / ADEPTLinks
Source code and data for ADEPT: A DEbiasing PrompT Framework (AAAI-23).
☆14Updated 5 months ago
Alternatives and similar repositories for ADEPT
Users that are interested in ADEPT are comparing it to the libraries listed below
Sorting:
- Augmenting Statistical Models with Natural Language Parameters☆26Updated 8 months ago
- ☆44Updated last year
- This repository contains the official code for the paper: "Prompt Injection: Parameterization of Fixed Inputs"☆32Updated 8 months ago
- ☆27Updated 2 years ago
- Restore safety in fine-tuned language models through task arithmetic☆28Updated last year
- ☆29Updated last year
- ☆44Updated 9 months ago
- ☆14Updated last year
- ConceptVectors Benchmark and Code for the paper "Intrinsic Evaluation of Unlearning Using Parametric Knowledge Traces"☆35Updated 3 months ago
- ☆31Updated last year
- Code for ACL 2023 paper "BOLT: Fast Energy-based Controlled Text Generation with Tunable Biases".☆21Updated last year
- A Mechanistic Understanding of Alignment Algorithms: A Case Study on DPO and Toxicity.☆72Updated 3 months ago
- ☆40Updated last year
- [EMNLP-2022 Findings] Code for paper “ProGen: Progressive Zero-shot Dataset Generation via In-context Feedback”.☆26Updated 2 years ago
- [ACL 2023] Knowledge Unlearning for Mitigating Privacy Risks in Language Models☆81Updated 8 months ago
- AbstainQA, ACL 2024☆25Updated 7 months ago
- Tasks for describing differences between text distributions.☆16Updated 9 months ago
- Official code for ICML 2024 paper on Persona In-Context Learning (PICLe)☆24Updated 11 months ago
- Code for "Universal Adversarial Triggers Are Not Universal."☆17Updated last year
- A codebase for ACL 2023 paper: Mitigating Label Biases for In-context Learning☆10Updated last year
- EMNLP 2022: "MABEL: Attenuating Gender Bias using Textual Entailment Data" https://arxiv.org/abs/2210.14975☆38Updated last year
- EMNLP 2024: Model Editing Harms General Abilities of Large Language Models: Regularization to the Rescue☆35Updated last week
- ☆41Updated 8 months ago
- Lightweight Adapting for Black-Box Large Language Models☆22Updated last year
- [NAACL'25 Oral] Steering Knowledge Selection Behaviours in LLMs via SAE-Based Representation Engineering☆58Updated 6 months ago
- ICLR2024 Paper. Showing properties of safety tuning and exaggerated safety.☆84Updated last year
- Röttger et al. (NAACL 2024): "XSTest: A Test Suite for Identifying Exaggerated Safety Behaviours in Large Language Models"☆98Updated 3 months ago
- ☆50Updated last year
- PaCE: Parsimonious Concept Engineering for Large Language Models (NeurIPS 2024)☆35Updated 7 months ago
- ☆36Updated 2 years ago