dongxinshuai / ASCC

Related projects ⓘ

Alternatives and complementary repositories for ASCC

• princeton-nlp / rationale-robustness
  NAACL 2022: Can Rationalization Improve Robustness? https://arxiv.org/abs/2204.11790
  ☆27Updated 2 years ago
• yanaiela / pararel
  ☆42Updated 10 months ago
• dugu9sword / dne
  ACL 2021 - Defense against Adversarial Attacks in NLP via Dirichlet Neighborhood Ensemble
  ☆15Updated last year
• mireshghallah / mixmatch
  Repository for ACL 2022 paper Mix and Match: Learning-free Controllable Text Generation using Energy Language Models
  ☆41Updated 2 years ago
• AI-secure / T3
  [EMNLP 2020] "T3: Tree-Autoencoder Constrained Adversarial Text Generation for Targeted Attack" by Boxin Wang, Hengzhi Pei, Boyuan Pan, Q…
  ☆26Updated 3 years ago
• ruiqi-zhong / DescribeDistributionalDifferences
  Code for preprint: Summarizing Differences between Text Distributions with Natural Language
  ☆42Updated last year
• QData / TextAttack-A2T
  A2T: Towards Improving Adversarial Training of NLP Models (EMNLP 2021 Findings)
  ☆24Updated 3 years ago
• jiacheng-ye / ZeroGen
  [EMNLP 2022] Code for our paper “ZeroGen: Efficient Zero-shot Learning via Dataset Generation”.
  ☆46Updated 2 years ago
• Hsuan-Tung / universal_attack_natural_trigger
  Natural Universal Trigger Search (NUTS)
  ☆21Updated 3 years ago
• peterwestuw / surface-form-competition
  ☆58Updated 2 years ago
• cookielee77 / CLARE
  Contextualized Perturbation for Textual Adversarial Attack, NAACL 2021
  ☆43Updated 3 years ago
• RockyLzy / TextDefender
  codes for "Searching for an Effective Defender:Benchmarking Defense against Adversarial Word Substitution"
  ☆31Updated last year
• zjiehang / RanMASK
  For Certified Robustness to Text Adversarial Attacks by Randomized [MASK]
  ☆15Updated last month
• AI-secure / InfoBERT
  [ICLR 2021] "InfoBERT: Improving Robustness of Language Models from An Information Theoretic Perspective" by Boxin Wang, Shuohang Wang, Y…
  ☆83Updated last year
• leix28 / prompt-universal-vulnerability
  Implementation of the paper "Exploring the Universal Vulnerability of Prompt-based Learning Paradigm" on Findings of NAACL 2022
  ☆27Updated 2 years ago
• successar / instance_attributions_NLP
  ☆17Updated 3 years ago
• pratyushmaini / llm_dataset_inference
  Official Repository for Dataset Inference for LLMs
  ☆23Updated 3 months ago
• minicheshire / Robust-Prefix-Tuning
  code for the ICLR'22 paper: On Robust Prefix-Tuning for Text Classification
  ☆27Updated 2 years ago
• thunlp / MixADA
  ☆19Updated 3 years ago
• JHL-HUST / PWWS
  Generating Natural Language Adversarial Examples through Probability Weighted Word Saliency
  ☆68Updated last year
• WinnieHAN / structure_adv
  ☆10Updated 4 years ago
• lushleaf / Structure-free-certified-NLP
  SAFER: A Structure-free Approach For cErtified Robustness to Adversarial Word Substitutions (ACL 2020)
  ☆27Updated 3 years ago
• robinjia / certified-word-sub
  Official repository for Jia, Raghunathan, Göksel, and Liang, "Certified Robustness to Adversarial Word Substitutions" (EMNLP 2019)
  ☆39Updated 4 years ago
• archiki / GrIPS
  Code for our paper: "GrIPS: Gradient-free, Edit-based Instruction Search for Prompting Large Language Models"
  ☆51Updated last year
• ekinakyurek / influence
  Code for "Tracing Knowledge in Language Models Back to the Training Data"
  ☆35Updated last year
• ewsheng / controllable-nlg-biases
  Framework for controlling demographic biases in NLG (using adversarial prompts)
  ☆20Updated last year
• txsun1997 / Metric-Fairness
  EMNLP'2022: BERTScore is Unfair: On Social Bias in Language Model-Based Metrics for Text Generation
  ☆38Updated 2 years ago
• pliang279 / LM_bias
  [ICML 2021] Towards Understanding and Mitigating Social Biases in Language Models
  ☆60Updated 2 years ago
• TevenLeScao / pet
  This repository contains the code for "How many data points is a prompt worth?"
  ☆49Updated 3 years ago
• technion-cs-nlp / Individual-Neurons-Pitfalls
  ☆10Updated 2 years ago