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🔍 Today's pick in Interpretability & Analysis of LMs: ReAGent: Towards A Model-agnostic Feature Attribution Method for Generative Language Models  by 
🔍 Today's pick in Interpretability & Analysis of LMs: ReAGent: Towards A Model-agnostic Feature Attribution Method for Generative Language Models  by 
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@casszhao
casszhao
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 and B. Shan
 and B. Shan
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Authors propose Recursive Attribution Generation (ReAGent), a perturbation-based feature attribution approach specifically conceived for generative LMs. The method employs a lightweight encoder LM to replace sampled input spans with valid alternatives and measure the effect of the perturbation on the drop in next token probability predictions. ReAGent is shown to consistentlyoutperform other established approaches across several models and generation tasks in terms of token- and sentence-level faithfulness.
Authors propose Recursive Attribution Generation (ReAGent), a perturbation-based feature attribution approach specifically conceived for generative LMs. The method employs a lightweight encoder LM to replace sampled input spans with valid alternatives and measure the effect of the perturbation on the drop in next token probability predictions. ReAGent is shown to consistentlyoutperform other established approaches across several models and generation tasks in terms of token- and sentence-level faithfulness.
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📄 Paper: 
📄 Paper: 
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https://huggingface.co/papers/2402.00794
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2402.00794
https://huggingface.co/papers/2402.00794
ReAGent: Towards A Model-agnostic Feature Attribution Method for
  Generative Language Models (2402.00794)
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💻 Code: 
💻 Code: 
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https://github.com/casszhao/ReAGent
https://github.com/casszhao/ReAGent
🔍 Today's pick in Interpretability & Analysis of LMs: ReAGent: Towards A Model-agnostic Feature Attribution Method for Generative Language Models  by @casszhao and B. Shan

Authors propose Recursive Attribution Generation (ReAGent), a perturbation-based feature attribution approach specifically conceived for generative LMs. The method employs a lightweight encoder LM to replace sampled input spans with valid alternatives and measure the effect of the perturbation on the drop in next token probability predictions. ReAGent is shown to consistentlyoutperform other established approaches across several models and generation tasks in terms of token- and sentence-level faithfulness.

📄 Paper: https://huggingface.co/papers/2402.00794
💻 Code: https://github.com/casszhao/ReAGent
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victor samusenps