googleinterns / localizing-paragraph-memorization
☆14Updated 10 months ago
Alternatives and similar repositories for localizing-paragraph-memorization:
Users that are interested in localizing-paragraph-memorization are comparing it to the libraries listed below
- Codebase for Instruction Following without Instruction Tuning☆33Updated 3 months ago
- Official Code Repository for [AutoScale–Automatic Prediction of Compute-optimal Data Compositions for Training LLMs]☆9Updated 3 months ago
- Official implementation of Privacy Implications of Retrieval-Based Language Models (EMNLP 2023). https://arxiv.org/abs/2305.14888☆35Updated 7 months ago
- ☆15Updated 5 months ago
- Long Context Extension and Generalization in LLMs☆40Updated 3 months ago
- Code for "Seeking Neural Nuggets: Knowledge Transfer in Large Language Models from a Parametric Perspective"☆31Updated 8 months ago
- [ACL 2023 Findings] What In-Context Learning “Learns” In-Context: Disentangling Task Recognition and Task Learning☆22Updated last year
- Data Valuation on In-Context Examples (ACL23)☆23Updated this week
- [ICLR'24 spotlight] Tool-Augmented Reward Modeling☆44Updated 3 weeks ago
- [ACL'24 Oral] Analysing The Impact of Sequence Composition on Language Model Pre-Training☆18Updated 5 months ago
- Towards Systematic Measurement for Long Text Quality☆31Updated 4 months ago
- Evaluate the Quality of Critique☆35Updated 7 months ago
- [EMNLP 2023] Knowledge Rumination for Pre-trained Language Models☆17Updated last year
- [NeurIPS 2024 Spotlight] Code and data for the paper "Finding Transformer Circuits with Edge Pruning".☆42Updated last month
- Restore safety in fine-tuned language models through task arithmetic☆26Updated 9 months ago
- AbstainQA, ACL 2024☆25Updated 3 months ago
- Official repository for MATES: Model-Aware Data Selection for Efficient Pretraining with Data Influence Models [NeurIPS 2024]☆56Updated 2 months ago
- Is In-Context Learning Sufficient for Instruction Following in LLMs?☆26Updated 7 months ago
- The source code of "Merging Experts into One: Improving Computational Efficiency of Mixture of Experts (EMNLP 2023)":