Papers by Roberto Dessi
Robustness of Named-Entity Replacements for In-Context Learning (2023.findings-emnlp)
Copied to clipboard
Saeed Goodarzi, Nikhil Kagita, Dennis Minn, Shufan Wang, Roberto Dessi, Shubham Toshniwal, Adina Williams, Jack Lanchantin, Koustuv Sinha
| Challenge: | Modern large language models perform in-context learning, where query- answer demonstrations are shown before the final query. |
| Approach: | They propose to use in-context learning to prompt queries before they are answered . they find that the choice of demonstrations can affect model performance . |
| Outcome: | The proposed model performance improves on named entity replacements across three reasoning tasks and two popular LLMs. |
Emergent Language-Based Coordination In Deep Multi-Agent Systems (2022.emnlp-tutorials)
Copied to clipboard
| Challenge: | Pre-trained deep networks are the standard building blocks of modern AI applications. |
| Approach: | This tutorial will introduce deep net emergent communication and discuss current shortcomings . participants will implement and analyze two emergentic communication setups from the literature . |
| Outcome: | The presentation will cover various topics from the present and recent past, as well as discussing current shortcomings and suggest future directions. |