You Don't Know My Favorite Color: Preventing Dialogue Representations from Revealing Speakers' Private Personas

Abstract

Social chatbots, also known as chit-chat chatbots, evolve rapidly with large pretrained language models. Despite the huge progress, privacy concerns have arisen recently: training data of large language models can be extracted via model inversion attacks. On the other hand, the datasets used for training chatbots contain many private conversations between two individuals. In this work, we further investigate the privacy leakage of the hidden states of chatbots trained by language modeling which has not been well studied yet. We show that speakers' personas can be inferred through a simple neural network with high accuracy. To this end, we propose effective defense objectives to protect persona leakage from hidden states. We conduct extensive experiments to demonstrate that our proposed defense objectives can greatly reduce the attack accuracy from 37.6% to 0.5%. Meanwhile, the proposed objectives preserve language models' powerful generation ability.

Revisiting Sparse Retrieval for Few-shot Entity Linking

“…Entity linking (EL) bridges the gap between knowledge and downstream tasks (Wang et al, 2023;Dong et al, 2022;Li et al, 2022a). Recent entity linking (EL) methods follow a two-stage "retrieve and re-rank" approach (Wu et al, 2020), and our work focuses on the first-stage retrieval.…”