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Qingyu Zhou

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NeurIPS Conference 2024 Conference Paper

When LLMs Meet Cunning Texts: A Fallacy Understanding Benchmark for Large Language Models

  • Yinghui Li
  • Qingyu Zhou
  • Yuanzhen Luo
  • Shirong Ma
  • Yangning Li
  • Hai-Tao Zheng
  • Xuming Hu
  • Philip S. Yu

Recently, Large Language Models (LLMs) make remarkable evolutions in language understanding and generation. Following this, various benchmarks for measuring all kinds of capabilities of LLMs have sprung up. In this paper, we challenge the reasoning and understanding abilities of LLMs by proposing a FaLlacy Understanding Benchmark (FLUB) containing cunning texts that are easy for humans to understand but difficult for models to grasp. Specifically, the cunning texts that FLUB focuses on mainly consist of the tricky, humorous, and misleading texts collected from the real internet environment. And we design three tasks with increasing difficulty in the FLUB benchmark to evaluate the fallacy understanding ability of LLMs. Based on FLUB, we investigate the performance of multiple representative and advanced LLMs, reflecting our FLUB is challenging and worthy of more future study. Interesting discoveries and valuable insights are achieved in our extensive experiments and detailed analyses. We hope that our benchmark can encourage the community to improve LLMs' ability to understand fallacies. Our data and codes are available at https: //github. com/THUKElab/FLUB.

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AAAI Conference 2018 Conference Paper

Sequential Copying Networks

  • Qingyu Zhou
  • Nan Yang
  • Furu Wei
  • Ming Zhou

Copying mechanism shows effectiveness in sequence-tosequence based neural network models for text generation tasks, such as abstractive sentence summarization and question generation. However, existing works on modeling copying or pointing mechanism only considers single word copying from the source sentences. In this paper, we propose a novel copying framework, named Sequential Copying Networks (SeqCopyNet), which not only learns to copy single words, but also copies sequences from the input sentence. It leverages the pointer networks to explicitly select a subspan from the source side to target side, and integrates this sequential copying mechanism to the generation process in the encoder-decoder paradigm. Experiments on abstractive sentence summarization and question generation tasks show that the proposed SeqCopyNet can copy meaningful spans and outperforms the baseline models.

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