6 citations · 6 across the 11 of their papers we have counts for
7 papers · 1 filter
Prompt, Translate, Fine-Tune, Re-Initialize, or Instruction-Tune? Adapting LLMs for In-Context Learning in Low-Resource Languages
Christopher Toukmaji, Jeffrey Flanigan
LLMs are typically trained in high-resource languages, and tasks in lower-resourced languages tend to underperform the higher-resource language counterparts for in-context learning…
RAC: Efficient LLM Factuality Correction with Retrieval Augmentation
Changmao Li, Jeffrey Flanigan
Large Language Models (LLMs) exhibit impressive results across a wide range of natural language processing (NLP) tasks, yet they can often produce factually incorrect outputs. This…
Large Language Model Unlearning via Embedding-Corrupted Prompts
Chris Yuhao Liu, Yaxuan Wang, Jeffrey Flanigan +1
Large language models (LLMs) have advanced to encompass extensive knowledge across diverse domains. Yet controlling what a large language model should not know is important for ens…
Future Language Modeling from Temporal Document History
Changmao Li, Jeffrey Flanigan
Predicting the future is of great interest across many aspects of human activity. Businesses are interested in future trends, traders are interested in future stock prices, and com…
Unsupervised End-to-End Task-Oriented Dialogue with LLMs: The Power of the Noisy Channel
Brendan King, Jeffrey Flanigan
Training task-oriented dialogue systems typically requires turn-level annotations for interacting with their APIs: e.g. a dialogue state and the system actions taken at each step.…
Task Contamination: Language Models May Not Be Few-Shot Anymore
Changmao Li, Jeffrey Flanigan
Large language models (LLMs) offer impressive performance in various zero-shot and few-shot tasks. However, their success in zero-shot and few-shot settings may be affected by task…
A New Approach Towards Autoformalization
Nilay Patel, Rahul Saha, Jeffrey Flanigan
Verifying mathematical proofs is difficult, but can be automated with the assistance of a computer. Autoformalization is the task of automatically translating natural language math…