Large Language Models Understanding: an Inherent Ambiguity Barrier

要約

大規模言語モデル（LLM）の驚異的な出現以来、世界を理解し、LLMが関与する対話の意味を理解する能力に関して、活発な議論が続いている。思考実験、LLMと人間との逸話的会話、統計的言語分析、哲学的考察などに基づく議論や反論が提案されている。この短い論文では、LLMが驚くほど流暢な対話の意味を理解することを妨げる本質的な曖昧性の障壁を導く思考実験と半形式的考察に基づく反論を紹介する。

要約(オリジナル)

A lively ongoing debate is taking place, since the extraordinary emergence of Large Language Models (LLMs) with regards to their capability to understand the world and capture the meaning of the dialogues in which they are involved. Arguments and counter-arguments have been proposed based upon thought experiments, anecdotal conversations between LLMs and humans, statistical linguistic analysis, philosophical considerations, and more. In this brief paper we present a counter-argument based upon a thought experiment and semi-formal considerations leading to an inherent ambiguity barrier which prevents LLMs from having any understanding of what their amazingly fluent dialogues mean.

arxiv情報

提供元, 利用サービス

arxiv.jp, DeepL