PromptAttack: Probing Dialogue State Trackers with Adversarial Prompts
- URL: http://arxiv.org/abs/2306.04535v1
- Date: Wed, 7 Jun 2023 15:41:40 GMT
- Title: PromptAttack: Probing Dialogue State Trackers with Adversarial Prompts
- Authors: Xiangjue Dong, Yun He, Ziwei Zhu, James Caverlee
- Abstract summary: A key component of modern conversational systems is the Dialogue State Tracker (or DST)
We introduce a prompt-based learning approach to automatically generate effective adversarial examples to probe DST models.
We show how much the generated adversarial examples can bolster a DST through adversarial training.
- Score: 25.467840115593784
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: A key component of modern conversational systems is the Dialogue State
Tracker (or DST), which models a user's goals and needs. Toward building more
robust and reliable DSTs, we introduce a prompt-based learning approach to
automatically generate effective adversarial examples to probe DST models. Two
key characteristics of this approach are: (i) it only needs the output of the
DST with no need for model parameters, and (ii) it can learn to generate
natural language utterances that can target any DST. Through experiments over
state-of-the-art DSTs, the proposed framework leads to the greatest reduction
in accuracy and the best attack success rate while maintaining good fluency and
a low perturbation ratio. We also show how much the generated adversarial
examples can bolster a DST through adversarial training. These results indicate
the strength of prompt-based attacks on DSTs and leave open avenues for
continued refinement.
Related papers
- Chain of Thought Explanation for Dialogue State Tracking [52.015771676340016]
Dialogue state tracking (DST) aims to record user queries and goals during a conversational interaction.
We propose a model named Chain-of-Thought-Explanation (CoTE) for the DST task.
CoTE is designed to create detailed explanations step by step after determining the slot values.
arXiv Detail & Related papers (2024-03-07T16:59:55Z) - Injecting linguistic knowledge into BERT for Dialogue State Tracking [60.42231674887294]
This paper proposes a method that extracts linguistic knowledge via an unsupervised framework.
We then utilize this knowledge to augment BERT's performance and interpretability in Dialogue State Tracking (DST) tasks.
We benchmark this framework on various DST tasks and observe a notable improvement in accuracy.
arXiv Detail & Related papers (2023-11-27T08:38:42Z) - CSS: Combining Self-training and Self-supervised Learning for Few-shot
Dialogue State Tracking [36.18207750352937]
Few-shot dialogue state tracking (DST) is a realistic problem that trains the DST model with limited labeled data.
We propose a few-shot DST framework called CSS, which Combines Self-training and Self-supervised learning methods.
Experimental results on the MultiWOZ dataset show that our proposed CSS achieves competitive performance in several few-shot scenarios.
arXiv Detail & Related papers (2022-10-11T04:55:16Z) - In-Context Learning for Few-Shot Dialogue State Tracking [55.91832381893181]
We propose an in-context (IC) learning framework for few-shot dialogue state tracking (DST)
A large pre-trained language model (LM) takes a test instance and a few annotated examples as input, and directly decodes the dialogue states without any parameter updates.
This makes the LM more flexible and scalable compared to prior few-shot DST work when adapting to new domains and scenarios.
arXiv Detail & Related papers (2022-03-16T11:58:24Z) - Robust Dialogue State Tracking with Weak Supervision and Sparse Data [2.580163308334609]
Generalising dialogue state tracking (DST) to new data is challenging due to the strong reliance on abundant and fine-grained supervision during training.
Sample sparsity, distributional shift and the occurrence of new concepts and topics frequently lead to severe performance degradation during inference.
We propose a training strategy to build extractive DST models without the need for fine-grained manual span labels.
arXiv Detail & Related papers (2022-02-07T16:58:12Z) - Adversarial Attacks and Defense for Non-Parametric Two-Sample Tests [73.32304304788838]
This paper systematically uncovers the failure mode of non-parametric TSTs through adversarial attacks.
To enable TST-agnostic attacks, we propose an ensemble attack framework that jointly minimizes the different types of test criteria.
To robustify TSTs, we propose a max-min optimization that iteratively generates adversarial pairs to train the deep kernels.
arXiv Detail & Related papers (2022-02-07T11:18:04Z) - Prompt Learning for Few-Shot Dialogue State Tracking [75.50701890035154]
This paper focuses on how to learn a dialogue state tracking (DST) model efficiently with limited labeled data.
We design a prompt learning framework for few-shot DST, which consists of two main components: value-based prompt and inverse prompt mechanism.
Experiments show that our model can generate unseen slots and outperforms existing state-of-the-art few-shot methods.
arXiv Detail & Related papers (2022-01-15T07:37:33Z) - Improving Longer-range Dialogue State Tracking [22.606650177804966]
Dialogue state tracking (DST) is a pivotal component in task-oriented dialogue systems.
In this paper, we aim to improve the overall performance of DST with a special focus on handling longer dialogues.
arXiv Detail & Related papers (2021-02-27T02:44:28Z) - Improving Limited Labeled Dialogue State Tracking with Self-Supervision [91.68515201803986]
Existing dialogue state tracking (DST) models require plenty of labeled data.
We present and investigate two self-supervised objectives: preserving latent consistency and modeling conversational behavior.
Our proposed self-supervised signals can improve joint goal accuracy by 8.95% when only 1% labeled data is used.
arXiv Detail & Related papers (2020-10-26T21:57:42Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.