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📢 New paper: Applied interpretability 🤝 MT personalization! We steer LLM generations to mimic human translator styles on literary novels in 7 languages. 📚 SAE steering can beat few-shot prompting, leading to better personalization while maintaining quality. 🧵1/

📘 Literary translation isn't just about accuracy, but also creatively conveying meaning across languages. But LLMs prompted for MT are very literal. Prompting & steering to the rescue! Can we personalize LLM’s MT when few examples are available, without further tuning? 🔍 2/

📈 But can models recognize and replicate individual translator styles?: ✓ Classifiers can find styles with high acc. (humans kinda don’t) ✓ Multi-shot prompting boosts style a lot ✓ We can detect strong style traces in activations (esp. mid layers) 3/

💡 We compare prompting (zero and multi-shot + explanations) and inference-time interventions (ActAdd, REFT and SAEs). Following SpARE (@yuzhaouoe.bsky.social @alessiodevoto.bsky.social), we propose ✨ contrastive SAE steering ✨ with mutual info to personalize literary MT by tuning latent features 4/

🌍 Across 7 languages, our SAE-based method matches or outperforms traditional prompting methods! Our method obtains better human-like translations (H) personalization accuracy (P), and maintains translation quality (Comet ☄️ @nunonmg.bsky.social) especially for smaller LLMs. 5/

🔍 What’s happening in the model? We find that SAE steering and multi-shot prompting impact internal representations similarly, suggesting insight from user examples are summarized with extra interpretability potential (look at latents) and better efficiency (no long context) 6/