Large language models (LLMs) are demonstrating remarkable capabilities across
various tasks despite lacking a foundation in human cognition. This raises the
question: can these models, beyond simply mimicking human language patterns,
offer insights into the mechanisms underlying human cognition? This study
explores the ability of ChatGPT to predict human performance in a
language-based memory task. Building upon theories of text comprehension, we
hypothesize that recognizing ambiguous sentences (e.g., "Because Bill drinks
wine is never kept in the house") is facilitated by preceding them with
contextually relevant information. Participants, both human and ChatGPT, were
presented with pairs of sentences. The second sentence was always a garden-path
sentence designed to be inherently ambiguous, while the first sentence either
provided a fitting (e.g., "Bill has chronic alcoholism") or an unfitting
context (e.g., "Bill likes to play golf"). We measured both human's and
ChatGPT's ratings of sentence relatedness, ChatGPT's memorability ratings for
the garden-path sentences, and humans' spontaneous memory for the garden-path
sentences. The results revealed a striking alignment between ChatGPT's
assessments and human performance. Sentences deemed more related and assessed
as being more memorable by ChatGPT were indeed better remembered by humans,
even though ChatGPT's internal mechanisms likely differ significantly from
human cognition. This finding, which was confirmed with a robustness check
employing synonyms, underscores the potential of generative AI models to
predict human performance accurately. We discuss the broader implications of
these findings for leveraging LLMs in the development of psychological theories
and for gaining a deeper understanding of human cognition.

通过测试 ChatGPT 在语言记忆任务中对人类表现的预测能力，研究发现 ChatGPT 和人类的表现有惊人的一致性，尽管它们的内部机制可能存在显著差异，这一发现强调了生成型人工智能模型在准确预测人类表现方面的潜力。

机器心理学：大型语言模型预测人类记忆

Towards a Psychology of Machines: Large Language Models Predict Human  Memory

Resolving semantic ambiguity has long been recognised as a central challenge
in the field of machine translation. Recent work on benchmarking translation
performance on ambiguous sentences has exposed the limitations of conventional
Neural Machine Translation (NMT) systems, which fail to capture many of these
cases. Large language models (LLMs) have emerged as a promising alternative,
demonstrating comparable performance to traditional NMT models while
introducing new paradigms for controlling the target outputs. In this paper, we
study the capabilities of LLMs to translate ambiguous sentences containing
polysemous words and rare word senses. We also propose two ways to improve the
handling of such ambiguity through in-context learning and fine-tuning on
carefully curated ambiguous datasets. Experiments show that our methods can
match or outperform state-of-the-art systems such as DeepL and NLLB in four out
of five language directions. Our research provides valuable insights into
effectively adapting LLMs for disambiguation during machine translation.

通过利用大型语言模型，我们研究了在机器翻译中解决语义歧义的能力，并提出了两种改进方法，通过上下文学习和在精心策划的歧义数据集上的微调，我们的方法在五种语言方向中有四种能够匹敌或胜过 DeepL 和 NLLB 等最先进的系统，为将大型语言模型有效地用于歧义消解的机器翻译提供了有价值的见解。