@inproceedings{hayashi-2024-reassessing,
    title = "Reassessing Semantic Knowledge Encoded in Large Language Models through the Word-in-Context Task",
    author = "Hayashi, Yoshihiko",
    editor = "Calzolari, Nicoletta  and
      Kan, Min-Yen  and
      Hoste, Veronique  and
      Lenci, Alessandro  and
      Sakti, Sakriani  and
      Xue, Nianwen",
    booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
    month = may,
    year = "2024",
    address = "Torino, Italia",
    publisher = "ELRA and ICCL",
    url = "https://aclanthology.org/2024.lrec-main.1189",
    pages = "13610--13620",
    abstract = "Despite the remarkable recent advancements in large language models (LLMs), a comprehensive understanding of their inner workings and the depth of their knowledge remains elusive. This study aims to reassess the semantic knowledge encoded in LLMs by utilizing the Word-in-Context (WiC) task, which involves predicting the semantic equivalence of a target word across different contexts, as a probing task. To address this challenge, we start by prompting LLMs, specifically GPT-3 and GPT-4, to generate natural language descriptions that contrast the meanings of the target word in two contextual sentences given in the WiC dataset. Subsequently, we conduct a manual analysis to examine their linguistic attributes. In parallel, we train a text classification model that utilizes the generated descriptions as supervision and assesses their practical effectiveness in the WiC task. The linguistic and empirical findings reveal a consistent provision of valid and valuable descriptions by LLMs, with LLM-generated descriptions significantly improving classification accuracy. Notably, the highest classification result achieved with GPT-3-generated descriptions largely surpassed GPT-3{'}s zero-shot baseline. However, the GPT-4-generated descriptions performed slightly below GPT-4{'}s zero-shot baseline, suggesting that the full potential of the most advanced large language models, such as GPT-4, is yet to be fully revealed.",
}

Despite the remarkable recent advancements in large language models (LLMs), a comprehensive understanding of their inner workings and the depth of their knowledge remains elusive. This study aims to reassess the semantic knowledge encoded in LLMs by utilizing the Word-in-Context (WiC) task, which involves predicting the semantic equivalence of a target word across different contexts, as a probing task. To address this challenge, we start by prompting LLMs, specifically GPT-3 and GPT-4, to generate natural language descriptions that contrast the meanings of the target word in two contextual sentences given in the WiC dataset. Subsequently, we conduct a manual analysis to examine their linguistic attributes. In parallel, we train a text classification model that utilizes the generated descriptions as supervision and assesses their practical effectiveness in the WiC task. The linguistic and empirical findings reveal a consistent provision of valid and valuable descriptions by LLMs, with LLM-generated descriptions significantly improving classification accuracy. Notably, the highest classification result achieved with GPT-3-generated descriptions largely surpassed GPT-3{'}s zero-shot baseline. However, the GPT-4-generated descriptions performed slightly below GPT-4{'}s zero-shot baseline, suggesting that the full potential of the most advanced large language models, such as GPT-4, is yet to be fully revealed.
Hayashi, Yoshihiko
Reassessing Semantic Knowledge Encoded in Large Language Models through the Word-in-Context Task