In-context Interference in Chat-based Large Language Models
This addresses a limitation in black-box LLM interactions for users who rely on in-context learning, though it is incremental as it builds on known issues.
The study identifies in-context interference in chat-based LLMs, where continuous information flow causes forgetting of previously learned knowledge, reducing performance, and proposes an evaluation benchmark based on the bAbI dataset.
Large language models (LLMs) have had a huge impact on society due to their impressive capabilities and vast knowledge of the world. Various applications and tools have been created that allow users to interact with these models in a black-box scenario. However, one limitation of this scenario is that users cannot modify the internal knowledge of the model, and the only way to add or modify internal knowledge is by explicitly mentioning it to the model during the current interaction. This learning process is called in-context training, and it refers to training that is confined to the user's current session or context. In-context learning has significant applications, but also has limitations that are seldom studied. In this paper, we present a study that shows how the model can suffer from interference between information that continually flows in the context, causing it to forget previously learned knowledge, which can reduce the model's performance. Along with showing the problem, we propose an evaluation benchmark based on the bAbI dataset.