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R. Costa-jussà ","raw":" and M. R. Costa-jussà "},{"type":"new_line","raw":"\n"},{"type":"text","value":"(","raw":"("},{"type":"mention","user":"mt-upc","raw":"@mt-upc"},{"type":"text","value":", ","raw":", "},{"type":"mention","user":"GroNLP","raw":"@GroNLP"},{"type":"text","value":", ","raw":", "},{"type":"mention","user":"facebook","raw":"@facebook"},{"type":"text","value":")","raw":")"},{"type":"new_line","raw":"\n"},{"type":"new_line","raw":"\n"},{"type":"text","value":"This primer can serve as a comprehensive introduction to recent advances in interpretability for Transformer-based LMs for a technical audience, employing a unified notation to introduce network modules and present state-of-the-art interpretability methods.","raw":"This primer can serve as a comprehensive introduction to recent advances in interpretability for Transformer-based LMs for a technical audience, employing a unified notation to introduce network modules and present state-of-the-art interpretability methods."},{"type":"new_line","raw":"\n"},{"type":"new_line","raw":"\n"},{"type":"text","value":"Interpretability methods are presented with detailed formulations and categorized as either localizing the inputs or model components responsible for a particular prediction or decoding information stored in learned representations. Then, various insights on the role of specific model components are summarized alongside recent work using model internals to direct editing and mitigate hallucinations.","raw":"Interpretability methods are presented with detailed formulations and categorized as either localizing the inputs or model components responsible for a particular prediction or decoding information stored in learned representations. 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