Papers by James Wexler
ConstitutionalExperts: Training a Mixture of Principle-based Prompts (2024.acl-short)
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| Challenge: | Large language models (LLMs) are capable at a variety of tasks given the right prompt, but writing one remains a difficult and tedious process. |
| Approach: | They propose a method for learning a prompt consisting of constitutional principles, given a training dataset. |
| Outcome: | The proposed method outperforms other prompt optimization techniques by 10.9% and improves all techniques, suggesting its broad applicability. |
The Language Interpretability Tool: Extensible, Interactive Visualizations and Analysis for NLP Models (2020.emnlp-demos)
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Ian Tenney, James Wexler, Jasmijn Bastings, Tolga Bolukbasi, Andy Coenen, Sebastian Gehrmann, Ellen Jiang, Mahima Pushkarna, Carey Radebaugh, Emily Reif, Ann Yuan
| Challenge: | Existing tools for modeling and understanding models are limited . existing tools can assist practitioners in understanding and evaluating models . |
| Approach: | They present an open-source platform for visualization and understanding of NLP models. |
| Outcome: | The language interpretability tool (lit) is an open-source platform for visualization and understanding of NLP models. |