princeton-nlp

[NeurIPS 2023] Tree of Thoughts: Deliberate Problem Solving with Large Language Models

[EMNLP 2021] SimCSE: Simple Contrastive Learning of Sentence Embeddings https://arxiv.org/abs/2104.08821

[NeurIPS 2023] MeZO: Fine-Tuning Language Models with Just Forward Passes. https://arxiv.org/abs/2305.17333

[NeurIPS 2024] SimPO: Simple Preference Optimization with a Reference-Free Reward

[NAACL 2021] A Frustratingly Easy Approach for Entity and Relation Extraction https://arxiv.org/abs/2010.12812

[ACL 2021] LM-BFF: Better Few-shot Fine-tuning of Language Models https://arxiv.org/abs/2012.15723

[ICLR 2024] Sheared LLaMA: Accelerating Language Model Pre-training via Structured Pruning

[NeurIPS 2022] 🛒WebShop: Towards Scalable Real-World Web Interaction with Grounded Language Agents

[ACL 2021] Learning Dense Representations of Phrases at Scale; EMNLP'2021: Phrase Retrieval Learns Passage Retrieval, Too https://arxiv.org/abs/2012.12624

[ICML 2024] LESS: Selecting Influential Data for Targeted Instruction Tuning

[EMNLP 2023] Enabling Large Language Models to Generate Text with Citations. Paper: https://arxiv.org/abs/2305.14627

[EMNLP 2023] Adapting Language Models to Compress Long Contexts

Homepage for ProLong (Princeton long-context language models) and paper "How to Train Long-Context Language Models (Effectively)"

[NeurIPS 2023 D&B] Code repository for InterCode benchmark https://arxiv.org/abs/2306.14898

The HELMET Benchmark

[ICML 2024] Selecting High-Quality Data for Training Language Models

[ACL 2022] Structured Pruning Learns Compact and Accurate Models https://arxiv.org/abs/2204.00408

[EMNLP 2022] Training Language Models with Memory Augmentation https://arxiv.org/abs/2205.12674

[ACL 2024] Long-Context Language Modeling with Parallel Encodings

[NAACL 2021] Factual Probing Is [MASK]: Learning vs. Learning to Recall https://arxiv.org/abs/2104.05240

[NeurIPS 2023] Learning Transformer Programs

[NeurIPS 2024] CharXiv: Charting Gaps in Realistic Chart Understanding in Multimodal LLMs

EMNLP'2021: Simple Entity-centric Questions Challenge Dense Retrievers https://arxiv.org/abs/2109.08535

[ICLR 2024] Evaluating Large Language Models at Evaluating Instruction Following

Can Language Models Solve Olympiad Programming?

[EMNLP 2023] MQuAKE: Assessing Knowledge Editing in Language Models via Multi-Hop Questions

Princeton NLP's pre-training library based on fairseq with DeepSpeed kernel integration 🚃

[EMNLP 2024] A Retrieval Benchmark for Scientific Literature Search

EMNLP 2022: Generating Natural Language Proofs with Verifier-Guided Search https://arxiv.org/abs/2205.12443

A Kernel-Based View of Language Model Fine-Tuning https://arxiv.org/abs/2210.05643

[EMNLP 2020] Keep CALM and Explore: Language Models for Action Generation in Text-based Games

[EMNLP 2023] C-STS: Conditional Semantic Textual Similarity

[NeurIPS 2024 Spotlight] Code and data for the paper "Finding Transformer Circuits with Edge Pruning".

EMNLP 2021: Single-dataset Experts for Multi-dataset Question-Answering

[ICLR 2024] COLLIE: Systematic Construction of Constrained Text Generation Tasks

[NeurIPS 2022] DataMUX: Data Multiplexing for Neural Networks

EMNLP 2022: Finding Dataset Shortcuts with Grammar Induction https://arxiv.org/abs/2210.11560

[EMNLP 2023] Poisoning Retrieval Corpora by Injecting Adversarial Passages https://arxiv.org/abs/2310.19156