3 papers
cs.CL2025
Effect of Document Packing on the Latent Multi-Hop Reasoning Capabilities of Large Language Models
Gabriele Prato, Shagun Sodhani, Alessandro Sordoni +1
The standard practice for training large language models involves packing multiple documents together to optimize computational efficiency. However, the impact of this process on t…
cs.CL2025
Do Large Language Models Know How Much They Know?
Gabriele Prato, Jerry Huang, Prasanna Parthasarathi +2
Large Language Models (LLMs) have emerged as highly capable systems and are increasingly being integrated into various uses. However, the rapid pace of their deployment has outpace…
cs.CL2025
Small Encoders Can Rival Large Decoders in Detecting Groundedness
Istabrak Abbes, Gabriele Prato, Quentin Fournier +4
Augmenting large language models (LLMs) with external context significantly improves their performance in natural language processing (NLP) tasks. However, LLMs struggle to answer…