princeton-nlp
- A
https://github.com/princeton-nlp/ALCE [EMNLP 2023] Enabling Large Language Models to Generate Text with Citations. Paper: https://arxiv.org/abs/2305.14627
- C
https://github.com/princeton-nlp/c-sts [EMNLP 2023] C-STS: Conditional Semantic Textual Similarity
- C
https://github.com/princeton-nlp/CEPE [ACL 2024] Long-Context Language Modeling with Parallel Encodings
- C
https://github.com/princeton-nlp/CharXiv CharXiv: Charting Gaps in Realistic Chart Understanding in Multimodal LLMs
- E
https://github.com/princeton-nlp/Edge-Pruning Code and data for the paper "Finding Transformer Circuits with Edge Pruning".
- E
https://github.com/princeton-nlp/ELIZA-Transformer Representing Rule-based Chatbots with Transformers
- H
https://github.com/princeton-nlp/Heuristic-Core [ACL 2024] The Heuristic Core: Understanding Subnetwork Generalization in Pretrained Language Models - https://arxiv.org/abs/2403.03942
- I
https://github.com/princeton-nlp/il-scaling-in-games Official code repo of "Scaling Laws for Imitation Learning in Single-Agent Games"
- I
https://github.com/princeton-nlp/intercode [NeurIPS 2023 D&B] Code repository for InterCode benchmark https://arxiv.org/abs/2306.14898
- L
https://github.com/princeton-nlp/LESS [ICML 2024] LESS: Selecting Influential Data for Targeted Instruction Tuning
- L
https://github.com/princeton-nlp/LLM-Shearing [ICLR 2024] Sheared LLaMA: Accelerating Language Model Pre-training via Structured Pruning
- L
https://github.com/princeton-nlp/LLMBar [ICLR 2024] Evaluating Large Language Models at Evaluating Instruction Following
- P
https://github.com/princeton-nlp/PTP Improving Language Understanding from Screenshots. Paper: https://arxiv.org/abs/2402.14073
- Q
https://github.com/princeton-nlp/QuRating [ICML 2024] Selecting High-Quality Data for Training Language Models
- S
https://github.com/princeton-nlp/SimCSE [EMNLP 2021] SimCSE: Simple Contrastive Learning of Sentence Embeddings https://arxiv.org/abs/2104.08821
