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E-ConvRec: A Large-Scale Conversational Recommendation Dataset for E-Commerce Customer Service
There has been a growing interest in developing conversational recommendation system (CRS), which provides valuable recommendations to users through conversations.
Defending Against Weight-Poisoning Backdoor Attacks for Parameter-Efficient Fine-Tuning
Motivated by this insight, we developed a Poisoned Sample Identification Module (PSIM) leveraging PEFT, which identifies poisoned samples through confidence, providing robust defense against weight-poisoning backdoor attacks.
Universal Vulnerabilities in Large Language Models: Backdoor Attacks for In-context Learning
Our studies demonstrate that an attacker can manipulate the behavior of large language models by poisoning the demonstration context, without the need for fine-tuning the model.
MNER-QG: An End-to-End MRC framework for Multimodal Named Entity Recognition with Query Grounding
Multimodal named entity recognition (MNER) is a critical step in information extraction, which aims to detect entity spans and classify them to corresponding entity types given a sentence-image pair.
Modularized Interaction Network for Named Entity Recognition
Although the existing Named Entity Recognition (NER) models have achieved promising performance, they suffer from certain drawbacks.
