On the Tip of the Tongue: Analyzing Conceptual Representation in Large Language Models with Reverse-Dictionary Probe

• URL: http://arxiv.org/abs/2402.14404v2
• Date: Mon, 26 Feb 2024 11:40:45 GMT
• Title: On the Tip of the Tongue: Analyzing Conceptual Representation in Large Language Models with Reverse-Dictionary Probe
• Authors: Ningyu Xu, Qi Zhang, Menghan Zhang, Peng Qian, Xuanjing Huang
• Abstract summary: We use in-context learning to guide the models to generate the term for an object concept implied in a linguistic description. Experiments suggest that conceptual inference ability as probed by the reverse-dictionary task predicts model's general reasoning performance.
• Score: 36.65834065044746
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Probing and enhancing large language models' reasoning capacity remains a crucial open question. Here we re-purpose the reverse dictionary task as a case study to probe LLMs' capacity for conceptual inference. We use in-context learning to guide the models to generate the term for an object concept implied in a linguistic description. Models robustly achieve high accuracy in this task, and their representation space encodes information about object categories and fine-grained features. Further experiments suggest that the conceptual inference ability as probed by the reverse-dictionary task predicts model's general reasoning performance across multiple benchmarks, despite similar syntactic generalization behaviors across models. Explorative analyses suggest that prompting LLMs with description$\Rightarrow$word examples may induce generalization beyond surface-level differences in task construals and facilitate models on broader commonsense reasoning problems.

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