Research - Papers
Explore a selection of our published work on a variety of key research challenges in AI.
Task Me Anything
Benchmarks for large multimodal language models (MLMs) now serve to simultaneously assess the general capabilities of models instead of evaluating for a specific capability. As a result, when a…
The Art of Saying No: Contextual Noncompliance in Language Models
Chat-based language models are designed to be helpful, yet they should not comply with every user request. While most existing work primarily focuses on refusal of"unsafe"queries, we posit that the…
ACE2-SOM: Coupling an ML atmospheric emulator to a slab ocean and learning the sensitivity of climate to changed CO$_2$
Although autoregressive machine learning‐based emulators have been trained to produce stable and accurate rollouts in the climate of the present‐day and recent past, none so far have been trained to…
Tülu 3: Pushing Frontiers in Open Language Model Post-Training
Language model post-training is applied to refine behaviors and unlock new skills across a wide range of recent language models, but open recipes for applying these techniques lag behind proprietary…
Applying Intrinsic Debiasing on Downstream Tasks: Challenges and Considerations for Machine Translation
Most works on gender bias focus on intrinsic bias -- removing traces of information about a protected group from the model's internal representation. However, these works are often disconnected from…
ArxivDIGESTables: Synthesizing Scientific Literature into Tables using Language Models
When conducting literature reviews, scientists often create literature review tables—tables whose rows are publications and whose columns constitute a schema, a set of aspects used to compare and…
Detection and Measurement of Syntactic Templates in Generated Text
Recent work on evaluating the diversity of text generated by LLMs has focused on word-level features. Here we offer an analysis of syntactic features to characterize general repetition in models,…
Evaluating n-Gram Novelty of Language Models Using Rusty-DAWG
How novel are texts generated by language models (LMs) relative to their training corpora? In this work, we investigate the extent to which modern LMs generate /n/-grams from their training data,…
Measuring and Improving Attentiveness to Partial Inputs with Counterfactuals
The inevitable appearance of spurious correlations in training datasets hurts the generalization of NLP models on unseen data. Previous work has found that datasets with paired inputs are prone to…
Mechanistic?
The rise of the term “mechanistic interpretability” has accompanied increasing interest in understanding neural models—particularly language models. However, this jargon has also led to a fair…