Papers by Aaron Schein
An Ordinal Latent Variable Model of Conflict Intensity (2023.acl-long)
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| Challenge: | Advances in automated event extraction yield massive data sets of “who did what to whom” micro-records that enable data-driven approaches to monitoring conflict. |
| Approach: | They propose a probabilistic generative model that assumes each observed event is associated with a latent intensity class. |
| Outcome: | The proposed model obtains comparatively good held-out predictive performance on a conflictual to cooperative scale. |
Activation Scaling for Steering and Interpreting Language Models (2024.findings-emnlp)
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| Challenge: | a successful intervention should flip the correct with the wrong token, while remaining sparse. |
| Approach: | They propose to use activation scaling to flip the correct with the wrong token . they use gradient-based optimization to learn and evaluate a specific kind of efficient intervention . |
| Outcome: | The proposed method performs comparable with steering vectors but is much less minimal. |
Context versus Prior Knowledge in Language Models (2024.acl-long)
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| Challenge: | Existing studies have investigated how often a model will rely on prior knowledge over conflicting contextual information in answering questions. |
| Approach: | They propose two mutual information-based metrics to measure a model’s dependency on a context and on its prior about an entity. |
| Outcome: | The proposed metrics show that language models can integrate prior knowledge and new information in a predictable way across different questions and contexts. |
Sentiment as an Ordinal Latent Variable (2023.eacl-main)
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| Challenge: | Existing dictionaries are limited in coverage and sentiment scales vary widely; some are discrete others continuous. |
| Approach: | They propose a Bayesian generative model that learns a composite sentiment dictionary as an interpolation between six existing dictionaries with different scales. |
| Outcome: | The proposed model learns a composite sentiment dictionary as an interpolation between six existing dictionaries with different scales. |