01 January 2025
Chat-based Large Language Models (LLMs) have gained significant attention in the last year due to their impressive capabilities and potential to perform a wide range of tasks. These models have been used in various contexts, and multiple experts have been surprised by their remarkable ability to maintain a fluid conversation, answering questions with information stored in their weights and with information acquired within each session. Despite their ability to hold conversations, the inability to modify model weights in many applications makes the only way to add relevant information is through prompts in the same context. The property to learn and accumulate knowledge of the model without the need to modify the weights is a critical tool for current and future LLMs that have not been thoroughly examined or studied.
In this work, EMERGE partners from the University of Pisa focus on interference in in-context learning, examining how new knowledge affects performance in self-aware robots. The authors propose an evaluation benchmark based on the bAbI dataset to assess the robot’s ability to manage interference, maintain stability, ensure flexible information routing, and facilitate task performance. Addressing these challenges is crucial for improving LLMs’ effectiveness in developing self-aware robots.
Read the paper in the link below.
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