@inproceedings{truong-etal-2023-language,
    title = "Language models are not naysayers: an analysis of language models on negation benchmarks",
    author = "Truong, Thinh Hung  and
      Baldwin, Timothy  and
      Verspoor, Karin  and
      Cohn, Trevor",
    editor = "Palmer, Alexis  and
      Camacho-collados, Jose",
    booktitle = "Proceedings of the 12th Joint Conference on Lexical and Computational Semantics (*SEM 2023)",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.starsem-1.10",
    doi = "10.18653/v1/2023.starsem-1.10",
    pages = "101--114",
    abstract = "Negation has been shown to be a major bottleneck for masked language models, such as BERT. However, whether this finding still holds for larger-sized auto-regressive language models ({``}LLMs{''}) has not been studied comprehensively. With the ever-increasing volume of research and applications of LLMs, we take a step back to evaluate the ability of current-generation LLMs to handle negation, a fundamental linguistic phenomenon that is central to language understanding. We evaluate different LLMs - including the open-source GPT-neo, GPT-3, and InstructGPT - against a wide range of negation benchmarks. Through systematic experimentation with varying model sizes and prompts, we show that LLMs have several limitations including insensitivity to the presence of negation, an inability to capture the lexical semantics of negation, and a failure to reason under negation.",
}

Negation has been shown to be a major bottleneck for masked language models, such as BERT. However, whether this finding still holds for larger-sized auto-regressive language models ({``}LLMs{''}) has not been studied comprehensively. With the ever-increasing volume of research and applications of LLMs, we take a step back to evaluate the ability of current-generation LLMs to handle negation, a fundamental linguistic phenomenon that is central to language understanding. We evaluate different LLMs - including the open-source GPT-neo, GPT-3, and InstructGPT - against a wide range of negation benchmarks. Through systematic experimentation with varying model sizes and prompts, we show that LLMs have several limitations including insensitivity to the presence of negation, an inability to capture the lexical semantics of negation, and a failure to reason under negation.
Truong, Thinh Hung Baldwin, Timothy Verspoor, Karin Cohn, Trevor
Language models are not naysayers: an analysis of language models on negation benchmarks