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Mingdao Liu

4 accepted papers

2026

VisionWebDev: A Hierarchical Benchmark for Visual Website Development with Agent Verification

ICML 2026spotlight

Recent advances in large language models have improved the capabilities of coding agents, yet systematic evaluation of complex, end-to-end website development remains limited. To address this gap, we introduce \benchname{}, a hierarchical benchmark for visual website development, spanning from stati…

Cited by 0SourceScholar
2025

Scaling Speech-Text Pre-training with Synthetic Interleaved Data

ICLR 2025poster

Speech language models (SpeechLMs) accept speech input and produce speech output, allowing for more natural human-computer interaction compared to text-based large language models (LLMs). Traditional approaches for developing SpeechLMs are constrained by the limited availability of unsupervised spee…

Cited by 2SourcePDFScholar
2024

AgentTuning: Enabling Generalized Agent Abilities for LLMs

ACL 2024findings

Open large language models (LLMs) with great performance in various tasks have significantly advanced the development of LLMs. However, they are far inferior to commercial models such as ChatGPT and GPT-4 when acting as agents to tackle complex tasks in the real world. These agent tasks employ LLMs…

2023

Unsupervised Road Anomaly Detection with Language Anchors

ICRA 2023poster

Road anomaly detection is critical to safe autonomous driving, because current road scene understanding models are usually trained in a closed-set manner and fail to identify unknown objects. What's worse, it is difficult, if not impossible, to collect a large-scale dataset with anomaly annotations.…

Cited by 23SourcecodeScholar