activity
20232026
most citedTowards Understanding Sycophancy in Language Models

130 citations · 186 across the 7 of their papers we have counts for

collaborators

7 papers

cs.LG2026

Training Agents to Self-Report Misbehavior

Bruce W. Lee, Chen Yueh-Han, Tomek Korbak

Frontier AI agents may pursue hidden goals while concealing their pursuit from oversight. Alignment training aims to prevent such behavior by reinforcing the correct goals, but ali…

cs.AI2025★ 2 cited

Chain of Thought Monitorability: A New and Fragile Opportunity for AI Safety

Tomek Korbak, Mikita Balesni, Elizabeth Barnes +38

AI systems that "think" in human language offer a unique opportunity for AI safety: we can monitor their chains of thought (CoT) for the intent to misbehave. Like all other known A…

cs.LG2024★ 12 cited

Is Model Collapse Inevitable? Breaking the Curse of Recursion by Accumulating Real and Synthetic Data

Matthias Gerstgrasser, Rylan Schaeffer, Apratim Dey +11

The proliferation of generative models, combined with pretraining on web-scale data, raises a timely question: what happens when these models are trained on their own generated out…

cs.LG2024★ 16 cited

Foundational Challenges in Assuring Alignment and Safety of Large Language Models

Usman Anwar, Abulhair Saparov, Javier Rando +39

This work identifies 18 foundational challenges in assuring the alignment and safety of large language models (LLMs). These challenges are organized into three different categories…

cs.CL2023★ 130 cited

Towards Understanding Sycophancy in Language Models

Mrinank Sharma, Meg Tong, Tomasz Korbak +16

Human feedback is commonly utilized to finetune AI assistants. But human feedback may also encourage model responses that match user beliefs over truthful ones, a behaviour known a…

cs.CL2023★ 24 cited

Inverse Scaling: When Bigger Isn't Better

Ian R. McKenzie, Alexander Lyzhov, Michael Pieler +24

Work on scaling laws has found that large language models (LMs) show predictable improvements to overall loss with increased scale (model size, training data, and compute). Here, w…

cs.AI2023★ 2 cited

Models of symbol emergence in communication: a conceptual review and a guide for avoiding local minima

Julian Zubek, Tomasz Korbak, Joanna Rączaszek-Leonardi

Computational simulations are a popular method for testing hypotheses about the emergence of communication. This kind of research is performed in a variety of traditions including…