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cs.LG2026
Operationalising the Superficial Alignment Hypothesis via Task Complexity
Tomás Vergara-Browne, Darshan Patil, Ivan Titov +3
The superficial alignment hypothesis (SAH) posits that large language models learn most of their knowledge during pre-training, and that post-training merely surfaces this knowledg…
cs.LG2024
What's New in My Data? Novelty Exploration via Contrastive Generation
Masaru Isonuma, Ivan Titov
Fine-tuning is widely used to adapt language models for specific goals, often leveraging real-world data such as patient records, customer-service interactions, or web content in l…
cs.LG2017★ 54 cited
Emergence of Language with Multi-agent Games: Learning to Communicate with Sequences of Symbols
Serhii Havrylov, Ivan Titov
Learning to communicate through interaction, rather than relying on explicit supervision, is often considered a prerequisite for developing a general AI. We study a setting where t…