Tim Franzmeyer @timlive.bsky.social · 06/06/2025🚨 One model, high correctness: With low-threshold tuning, we take Llama3-70B from: ➡️ 51% → 87% correctness ➡️ Retaining 53% of the original completeness 100
Tim Franzmeyer @timlive.bsky.social · 06/06/2025⚖️ HALT allows you to trade off completeness and correctness We introduce a threshold that tunes how eagerly the model should respond: Low threshold = more reliable answers 🔒 (Left box) High threshold = more detailed answers 📝(Right box) 100
Tim Franzmeyer @timlive.bsky.social · 06/06/2025🛠️ Our approach: Adjust finetuning responses to match the capabilities of the LLM 1️⃣ Break pretrained LLM responses into factual fragments 2️⃣ Use ground truth to flag incorrect fragments 3️⃣ Modify finetuning responses by removing or replacing errors with “Unsure from here” 🚧 100
Tim Franzmeyer @timlive.bsky.social · 06/06/2025What if LLMs knew when to stop? 🚧 HALT finetuning teaches LLMs to only generate content they’re confident is correct. 🔍 Insight: Post-training must be adjusted to the model’s capabilities. ⚖️ Tunable trade-off: Higher correctness 🔒 vs. More completeness 📝 🧵 1101