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Haotian Zhu

4 accepted papers

2026

AutoCodeBench: Large Language Models are Automatic Code Benchmark Generators

ICLR 2026poster

Large Language Models (LLMs) have shown impressive performance across diverse domains, with code generation emerging as a particularly prominent application. However, existing benchmarks designed to evaluate code generation exhibit several critical limitations. First, most rely on manual annotations…

Cited by 0SourcecodeScholar
2025

One Head to Rule Them All: Amplifying LVLM Safety through a Single Critical Attention Head

NeurIPS 2025poster

Large Vision-Language Models (LVLMs) have demonstrated impressive capabilities in tasks requiring multimodal understanding. However, recent studies indicate that LVLMs are more vulnerable than LLMs to unsafe inputs and prone to generating harmful content. Existing defense strategies primarily includ…

Cited by 0SourcecodeScholar
2024

SciPrompt: Knowledge-augmented Prompting for Fine-grained Categorization of Scientific Topics

EMNLP 2024main

Prompt-based fine-tuning has become an essential method for eliciting information encoded in pre-trained language models for a variety of tasks, including text classification. For multi-class classification tasks, prompt-based fine-tuning under low-resource scenarios has resulted in performance leve…