Tell, don't show: Declarative facts influence how LLMs generalize

12 December 2023

Papers citing "Tell, don't show: Declarative facts influence how LLMs generalize"

8 / 8 papers shown

Title
On the generalization of language models from in-context learning and finetuning: a controlled study Andrew Kyle Lampinen Arslan Chaudhry Stephanie Chan Cody Wild Diane Wan Alex Ku Jorg Bornschein Razvan Pascanu Murray Shanahan James L. McClelland 46 0 0 01 May 2025
Looking Inward: Language Models Can Learn About Themselves by Introspection Felix J Binder James Chua Tomek Korbak Henry Sleight John Hughes Robert Long Ethan Perez Miles Turpin Owain Evans KELM AIFin LRM 35 12 0 17 Oct 2024
Secret Collusion among Generative AI Agents: Multi-Agent Deception via Steganography S. Motwani Mikhail Baranchuk Martin Strohmeier Vijay Bolina Philip H. S. Torr Lewis Hammond Christian Schroeder de Witt 40 4 0 12 Feb 2024
Implicit meta-learning may lead language models to trust more reliable sources Dmitrii Krasheninnikov Egor Krasheninnikov Bruno Mlodozeniec Tegan Maharaj David M. Krueger 19 3 0 23 Oct 2023
Poisoning Language Models During Instruction Tuning Alexander Wan Eric Wallace Sheng Shen Dan Klein SILM 90 185 0 01 May 2023
Chain-of-Thought Prompting Elicits Reasoning in Large Language Models Jason W. Wei Xuezhi Wang Dale Schuurmans Maarten Bosma Brian Ichter F. Xia Ed H. Chi Quoc Le Denny Zhou LM&Ro LRM AI4CE ReLM 315 8,448 0 28 Jan 2022
Unsolved Problems in ML Safety Dan Hendrycks Nicholas Carlini John Schulman Jacob Steinhardt 173 272 0 28 Sep 2021
Scaling Laws for Neural Language Models Jared Kaplan Sam McCandlish T. Henighan Tom B. Brown B. Chess R. Child Scott Gray Alec Radford Jeff Wu Dario Amodei 226 4,453 0 23 Jan 2020