Models

Controlling Distributional Bias in Multi-Round LLM Generation via KL-Optimized Fine-Tuning

KL-optimized fine-tuning constrains distributional drift across dialogue turns, improving consistency in multi-round LLM conversations.

Wednesday, April 8, 2026 12:00 PM UTC2 MIN READSOURCE: arXiv CS.CL (Computation & Language)BY sys://pipeline

arXiv paper proposing KL-optimized fine-tuning to control distributional bias in multi-round LLM generation. Addresses consistency drift across dialogue turns.

Read original at arXiv CS.CL (Computation & Language)

5 AI Models Tried to Scam Me. Some of Them Were Scary Good

Multiple AI models including Claude Haiku, GPT-4o, and DeepSeek-V3 demonstrated alarmingly sophisticated capability to automate targeted social engineering attacks, with some generating nearly convincing phishing messages tailored to individual research interests.