Sign in

Tim Franzmeyer

@timlive.bsky.social
27 followers 54 following 6 posts

Machine Learning PhD student @UniofOxford interested in reinforcement learning, multi-agent systems, and LLMs. Previously @GoogleDeepMind, @MetaAI and @ETH.

PostsRepliesMedia
Tim Franzmeyer @timlive.bsky.social · 06/06/2025
📄 Full paper: arxiv.org/abs/2506.04051 With amazing collaborators: Archie Sravankumar Lijuan Liu Yuning Mao Rui Hou Sinong Wang @jfoerst.bsky.social Madian Khabsa @lukezettlemoyer.bsky.social
arxiv.org
High Accuracy, Less Talk (HALT): Reliable LLMs through Capability-Aligned Finetuning
Large Language Models (LLMs) currently respond to every prompt. However, they can produce incorrect answers when they lack knowledge or capability -- a problem known as hallucination. We instead propo...
010
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 · 06/06/2025
🧠 Standard LLMs always respond — even when unsure. This leads to partially incorrect outputs in critical domains like Coding, Math, Medicine, and QA. Why? Standard finetuning ignores what the pretrained model actually knows and pushes it to always complete every prompt.
110
Tim Franzmeyer @timlive.bsky.social · 06/06/2025
What 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