Pinned Repositories
ALCE
[EMNLP 2023] Enabling Large Language Models to Generate Text with Citations. Paper: https://arxiv.org/abs/2305.14627
DensePhrases
[ACL 2021] Learning Dense Representations of Phrases at Scale; EMNLP'2021: Phrase Retrieval Learns Passage Retrieval, Too https://arxiv.org/abs/2012.12624
LLM-Shearing
[ICLR 2024] Sheared LLaMA: Accelerating Language Model Pre-training via Structured Pruning
LM-BFF
[ACL 2021] LM-BFF: Better Few-shot Fine-tuning of Language Models https://arxiv.org/abs/2012.15723
MeZO
[NeurIPS 2023] MeZO: Fine-Tuning Language Models with Just Forward Passes. https://arxiv.org/abs/2305.17333
PURE
[NAACL 2021] A Frustratingly Easy Approach for Entity and Relation Extraction https://arxiv.org/abs/2010.12812
SimCSE
[EMNLP 2021] SimCSE: Simple Contrastive Learning of Sentence Embeddings https://arxiv.org/abs/2104.08821
SWE-agent
SWE-agent takes a GitHub issue and tries to automatically fix it, using GPT-4, or your LM of choice. It solves 12.47% of bugs in the SWE-bench evaluation set and takes just 1.5 minutes to run.
SWE-bench
[ICLR 2024] SWE-Bench: Can Language Models Resolve Real-world Github Issues?
tree-of-thought-llm
[NeurIPS 2023] Tree of Thoughts: Deliberate Problem Solving with Large Language Models
Princeton Natural Language Processing's Repositories
princeton-nlp/SWE-agent
SWE-agent takes a GitHub issue and tries to automatically fix it, using GPT-4, or your LM of choice. It solves 12.47% of bugs in the SWE-bench evaluation set and takes just 1.5 minutes to run.
princeton-nlp/tree-of-thought-llm
[NeurIPS 2023] Tree of Thoughts: Deliberate Problem Solving with Large Language Models
princeton-nlp/SimCSE
[EMNLP 2021] SimCSE: Simple Contrastive Learning of Sentence Embeddings https://arxiv.org/abs/2104.08821
princeton-nlp/SWE-bench
[ICLR 2024] SWE-Bench: Can Language Models Resolve Real-world Github Issues?
princeton-nlp/MeZO
[NeurIPS 2023] MeZO: Fine-Tuning Language Models with Just Forward Passes. https://arxiv.org/abs/2305.17333
princeton-nlp/LLM-Shearing
[ICLR 2024] Sheared LLaMA: Accelerating Language Model Pre-training via Structured Pruning
princeton-nlp/ALCE
[EMNLP 2023] Enabling Large Language Models to Generate Text with Citations. Paper: https://arxiv.org/abs/2305.14627
princeton-nlp/SimPO
SimPO: Simple Preference Optimization with a Reference-Free Reward
princeton-nlp/LESS
ICML 2024: Less: Selecting Influential Data for Targeted Instruction Tuning
princeton-nlp/AutoCompressors
[EMNLP 2023] Adapting Language Models to Compress Long Contexts
princeton-nlp/WebShop
[NeurIPS 2022] đź›’WebShop: Towards Scalable Real-World Web Interaction with Grounded Language Agents
princeton-nlp/intercode
[NeurIPS 2023 D&B] Code repository for InterCode benchmark https://arxiv.org/abs/2306.14898
princeton-nlp/TransformerPrograms
[NeurIPS 2023] Learning Transformer Programs
princeton-nlp/CEPE
[ACL 2024] Long-Context Language Modeling with Parallel Encodings
princeton-nlp/QuRating
Selecting High-Quality Data for Training Language Models
princeton-nlp/LLMBar
[ICLR 2024] Evaluating Large Language Models at Evaluating Instruction Following
princeton-nlp/USACO
Can Language Models Solve Olympiad Programming?
princeton-nlp/NLProofS
EMNLP 2022: Generating Natural Language Proofs with Verifier-Guided Search https://arxiv.org/abs/2205.12443
princeton-nlp/MQuAKE
[EMNLP 2023] MQuAKE: Assessing Knowledge Editing in Language Models via Multi-Hop Questions
princeton-nlp/LM-Kernel-FT
A Kernel-Based View of Language Model Fine-Tuning https://arxiv.org/abs/2210.05643
princeton-nlp/c-sts
[EMNLP 2023] C-STS: Conditional Semantic Textual Similarity
princeton-nlp/Collie
[ICLR 2024] COLLIE: Systematic Construction of Constrained Text Generation Tasks
princeton-nlp/MABEL
EMNLP 2022: "MABEL: Attenuating Gender Bias using Textual Entailment Data" https://arxiv.org/abs/2210.14975
princeton-nlp/LM-Science-Tutor
princeton-nlp/PTP
Improving Language Understanding from Screenshots. Paper: https://arxiv.org/abs/2402.14073
princeton-nlp/corpus-poisoning
[EMNLP 2023] Poisoning Retrieval Corpora by Injecting Adversarial Passages https://arxiv.org/abs/2310.19156
princeton-nlp/lwm
We develop world models that can be adapted with natural language. Intergrating these models into artificial agents allows humans to effectively control these agents through verbal communication.
princeton-nlp/Heuristic-Core
The code accompanying the paper "The Heuristic Core: Understanding Subnetwork Generalization in Pretrained Language Models" - https://arxiv.org/abs/2403.03942
princeton-nlp/il-scaling-in-games
Official code repo of "Scaling Laws for Imitation Learning in NetHack"
princeton-nlp/MoQA