9 papers
Multimodal Unlearning Across Vision, Language, Video, and Audio: Survey of Methods, Datasets, and Benchmarks
Nobin Sarwar, Shubhashis Roy Dipta, Zheyuan Liu +1
With the growing adoption of VLMs, DMs, LLMs, and AFMs, these multimodal foundation models can inadvertently encode sensitive, copyrighted, biased, or unsafe cross-modal associatio…
MathAtlas: A Benchmark for Autoformalization in the Wild
Nilay Patel, Noah Arias, Davit Babayan +7
Current autoformalization benchmarks are largely focused on olympiad or undergraduate mathematics, while graduate and research-level mathematics remains underexplored. In this pape…
Residual Matrix Transformers: Scaling the Size of the Residual Stream
Brian Mak, Jeffrey Flanigan
The residual stream acts as a memory bus where transformer layers both store and access features (Elhage et al., 2021). We consider changing the mechanism for retrieving and storin…
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…
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…
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…