4 papers
Agents' Last Exam
Yiyou Sun, Xinyang Han, Weichen Zhang +306
Recent AI systems have achieved strong results on a wide range of benchmarks, yet these gains have not translated into economically meaningful deployment across many professional d…
Censored LLMs as a Natural Testbed for Secret Knowledge Elicitation
Helena Casademunt, Bartosz CywiÅski, Khoi Tran +3
Large language models sometimes produce false or misleading responses. Two approaches to this problem are honesty elicitation -- modifying prompts or weights so that the model answ…
Narrow Finetuning Leaves Clearly Readable Traces in Activation Differences
Julian Minder, Clément Dumas, Stewart Slocum +4
Finetuning on narrow domains has become an essential tool to adapt Large Language Models (LLMs) to specific tasks and to create models with known unusual properties that are useful…
Steering Out-of-Distribution Generalization with Concept Ablation Fine-Tuning
Helena Casademunt, Caden Juang, Adam Karvonen +3
Fine-tuning large language models (LLMs) can lead to unintended out-of-distribution generalization. Standard approaches to this problem rely on modifying training data, for example…