Search Results for author:
Found 11 papers, 10 papers with code
Do language models plan ahead for future tokens?
Do transformers "think ahead" during inference at a given position?
Nomic Embed: Training a Reproducible Long Context Text Embedder
This technical report describes the training of nomic-embed-text-v1, the first fully reproducible, open-source, open-weights, open-data, 8192 context length English text embedding model that outperforms both OpenAI Ada-002 and OpenAI text-embedding-3-small on short and long-context tasks.
Language Model Inversion
We consider the problem of language model inversion and show that next-token probabilities contain a surprising amount of information about the preceding text.
Tree Prompting: Efficient Task Adaptation without Fine-Tuning
Prompting language models (LMs) is the main interface for applying them to new tasks.
Text Embeddings Reveal (Almost) As Much As Text
How much private information do text embeddings reveal about the original text?
Unsupervised Text Deidentification
We propose an unsupervised deidentification method that masks words that leak personally-identifying information.
Explaining Patterns in Data with Language Models via Interpretable Autoprompting
Large language models (LLMs) have displayed an impressive ability to harness natural language to perform complex tasks.
Second-Order NLP Adversarial Examples
In these methods, a valid adversarial example fools the model being attacked, and is determined to be semantically or syntactically valid by a second model.
Searching for a Search Method: Benchmarking Search Algorithms for Generating NLP Adversarial Examples
We study the behavior of several black-box search algorithms used for generating adversarial examples for natural language processing (NLP) tasks.
TextAttack: A Framework for Adversarial Attacks, Data Augmentation, and Adversarial Training in NLP
TextAttack also includes data augmentation and adversarial training modules for using components of adversarial attacks to improve model accuracy and robustness.
Reevaluating Adversarial Examples in Natural Language
State-of-the-art attacks on NLP models lack a shared definition of a what constitutes a successful attack.
