Papers by Danielle Bitterman
Measuring Pointwise 𝒱-Usable Information In-Context-ly (2023.findings-emnlp)
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| Challenge: | In-context learning (ICL) is a new learning paradigm that has gained popularity along with the development of large language models. |
| Approach: | They propose to adapt a recently proposed hardness metric, pointwise V-usable information (PVI), to an in-context version. |
| Outcome: | The proposed hardness metric is compared with the original model and is more efficient because it requires only a few exemplars and does not require fine-tuning. |
When Raw Data Prevails: Are Large Language Model Embeddings Effective in Numerical Data Representation for Medical Machine Learning Applications? (2024.findings-emnlp)
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Yanjun Gao, Skatje Myers, Shan Chen, Dmitriy Dligach, Timothy Miller, Danielle Bitterman, Matthew Churpek, Majid Afshar
| Challenge: | Numerical data is pivotal for medical questions and answers, but tabular data is not fully integrated into LLMs. |
| Approach: | They examine the effectiveness of vector representations from last hidden states of LLMs for medical diagnostics and prognostics using electronic health record data. |
| Outcome: | The proposed representations outperform those using raw numerical EHR data in medical diagnostics and prognostics. |
Language Models are Surprisingly Fragile to Drug Names in Biomedical Benchmarks (2024.findings-emnlp)
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Jack Gallifant, Shan Chen, Pedro Moreira, Nikolaj Munch, Mingye Gao, Jackson Pond, Leo Anthony Celi, Hugo Aerts, Thomas Hartvigsen, Danielle Bitterman
| Challenge: | Medical knowledge is context-dependent and requires consistent reasoning across various natural language expressions of semantically equivalent phrases. |
| Approach: | They create a robustness dataset to evaluate performance differences on medical benchmarks . they swap brand and generic drug names using physician expert annotations based on medical terminology . |
| Outcome: | The proposed model shows a consistent performance drop of 1-10% on medical benchmarks. |
WorldMedQA-V: a multilingual, multimodal medical examination dataset for multimodal language models evaluation (2025.findings-naacl)
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João Matos, Shan Chen, Siena Kathleen V. Placino, Yingya Li, Juan Carlos Climent Pardo, Daphna Idan, Takeshi Tohyama, David Restrepo, Luis Filipe Nakayama, José María Millet Pascual-Leone, Guergana K Savova, Hugo Aerts, Leo Anthony Celi, An-Kwok Ian Wong, Danielle Bitterman, Jack Gallifant
| Challenge: | Existing multiple-choice question and answer (QA) datasets are text-only and available in a limited subset of languages and countries. |
| Approach: | They propose a multilingual, multimodal benchmarking dataset to evaluate multimodal/vision language models in healthcare. |
| Outcome: | The WorldMedQA-V includes 568 labeled multiple-choice QAs paired with 568 medical images from four countries. |
Sparse Autoencoder Features for Classifications and Transferability (2025.emnlp-main)
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| Challenge: | Sparse Autoencoders (SAEs) provide potential for uncovering structured, human-interpretable representations in Large Language Models (LLMs). |
| Approach: | They analyze SAEs for interpretable feature extraction from Large Language Models in safety-critical classification tasks. |
| Outcome: | The proposed framework outperforms hidden-state and BoW models while demonstrating cross-lingual toxicity detection and visual classification tasks. |
When Models Reason in Your Language: Controlling Thinking Language Comes at the Cost of Accuracy (2025.findings-emnlp)
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| Challenge: | Recent Large Reasoning Models (LRMs) with thinking traces have shown strong performance on English reasoning tasks. |
| Approach: | They evaluate two leading LRMs with thinking traces on established benchmark XReasoning and propose directions for future research. |
| Outcome: | The proposed models often revert to English or produce fragmented reasoning in other languages, revealing a substantial gap in the capability of thinking in non-English languages. |