4 citations · 8 across the 4 of their papers we have counts for
4 papers · 1 filter
How Susceptible are LLMs to Influence in Prompts?
Sotiris Anagnostidis, Jannis Bulian
Large Language Models (LLMs) are highly sensitive to prompts, including additional context provided therein. As LLMs grow in capability, understanding their prompt-sensitivity beco…
A Language Model's Guide Through Latent Space
Dimitri von Rütte, Sotiris Anagnostidis, Gregor Bachmann +1
Concept guidance has emerged as a cheap and simple way to control the behavior of language models by probing their hidden representations for concept vectors and using them to pert…
Dynamic Context Pruning for Efficient and Interpretable Autoregressive Transformers
Sotiris Anagnostidis, Dario Pavllo, Luca Biggio +3
Autoregressive Transformers adopted in Large Language Models (LLMs) are hard to scale to long sequences. Despite several works trying to reduce their computational cost, most of LL…
OpenAssistant Conversations -- Democratizing Large Language Model Alignment
Andreas Köpf, Yannic Kilcher, Dimitri von Rütte +15
Aligning large language models (LLMs) with human preferences has proven to drastically improve usability and has driven rapid adoption as demonstrated by ChatGPT. Alignment techniq…