Papers by Willie Neiswanger
Uncertainty Quantification with Pre-trained Language Models: A Large-Scale Empirical Analysis (2022.findings-emnlp)
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Yuxin Xiao, Paul Pu Liang, Umang Bhatt, Willie Neiswanger, Ruslan Salakhutdinov, Louis-Philippe Morency
| Challenge: | Pre-trained language models (PLMs) have gained increasing popularity due to compelling prediction performance in diverse natural language processing tasks. |
| Approach: | They compare three popular options for encoding and Temp Scaling for PLMs . they recommend using Temp Loss as uncertainty quantifier and Focal Loss for fine-tuning . |
| Outcome: | Using pre-trained language models, we compare three options on NLP classification tasks and domain shift. |
Textual Steering Vectors Can Improve Visual Understanding in Multimodal Large Language Models (2026.acl-long)
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Woody Haosheng Gan, Deqing Fu, Julian Asilis, Ollie Liu, Vatsal Sharan, Robin Jia, Willie Neiswanger
| Challenge: | Steering methods have emerged as effective tools for guiding large language models’ behavior, yet multimodal large language model lacks comparable techniques due to architectural diversity and limited availability of multimodal steering vectors. |
| Approach: | They validate steering vectors derived solely from text-only LLM backbones and use a cross-modal transfer technique to reuse existing interpretability tools. |
| Outcome: | The proposed steering vectors can guide and enhance multimodal models using SPAR, Mean Shift, and Linear Probing. |
TokenSmith: Streamlining Data Editing, Search, and Inspection for Large-Scale Language Model Training and Interpretability (2025.emnlp-demos)
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Mohammad Aflah Khan, Ameya Godbole, Johnny Wei, Ryan Yixiang Wang, James Flemings, Krishna P. Gummadi, Willie Neiswanger, Robin Jia
| Challenge: | Existing workflows for pretraining large language models are cumbersome, fragmented and inaccessible. |
| Approach: | They propose an open-source library for editing, inspection, and analysis of large language model datasets. |
| Outcome: | TokenSmith is an open-source library for editing, inspection, and analysis of large language model datasets. |