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Xueqing Liu

7 accepted papers

2024

Contextual Bandits with Budgeted Information Reveal

AISTATS 2024poster

Contextual bandit algorithms are commonly used in digital health to recommend personalized treatments. However, to ensure the effectiveness of the treatments, patients are often requested to take actions that have no immediate benefit to them, which we refer to as pro-treatment actions. In practice,…

Cited by 5SourcePDFScholar
2024

HateModerate: Testing Hate Speech Detectors against Content Moderation Policies

NAACL 2024findings

To protect users from massive hateful content, existing works studied automated hate speech detection. Despite the existing efforts, one question remains: Do automated hate speech detectors conform to social media content policies? A platform’s content policies are a checklist of content moderated b…

2024

Learning the Optimal Policy for Balancing Short-Term and Long-Term Rewards

NeurIPS 2024poster

Learning the optimal policy to balance multiple short-term and long-term rewards has extensive applications across various domains. Yet, there is a noticeable scarcity of research addressing policy learning strategies in this context. In this paper, we aim to learn the optimal policy capable of effe…

Cited by 0SourcePDFScholar
2024

VulLibGen: Generating Names of Vulnerability-Affected Packages via a Large Language Model

ACL 2024long

Security practitioners maintain vulnerability reports (e.g., GitHub Advisory) to help developers mitigate security risks. An important task for these databases is automatically extracting structured information mentioned in the report, e.g., the affected software packages, to accelerate the defense…

2022

TestAug: A Framework for Augmenting Capability-based NLP Tests

COLING 2022main

The recently proposed capability-based NLP testing allows model developers to test the functional capabilities of NLP models, revealing functional failures for models with good held-out evaluation scores. However, existing work on capability-based testing requires the developer to compose each indiv…

2021

An Empirical Study on Hyperparameter Optimization for Fine-Tuning Pre-trained Language Models

ACL 2021long

The performance of fine-tuning pre-trained language models largely depends on the hyperparameter configuration. In this paper, we investigate the performance of modern hyperparameter optimization methods (HPO) on fine-tuning pre-trained language models. First, we study and report three HPO algorithm…