Large Language Models (LLMs) have gained widespread global adoption, showcasing advanced linguistic capabilities across multiple of languages. There is a growing interest in academia to use these models to simulate and study human behaviors. However, it is crucial to acknowledge that an LLM's proficiency in a specific language might not fully encapsulate the norms and values associated with its culture. Concerns have emerged regarding potential biases towards Anglo-centric cultures and values due to the predominance of Western and US-based training data. This study focuses on analyzing the cultural representations of emotions in LLMs, in the specific case of mixed-emotion situations. Our methodology is based on the studies of Miyamoto et al. (2010), which identified distinctive emotional indicators in Japanese and American human responses. We first administer their mixed emotion survey to five different LLMs and analyze their outputs. Second, we experiment with contextual variables to explore variations in responses considering both language and speaker origin. Thirdly, we expand our investigation to encompass additional East Asian and Western European origin languages to gauge their alignment with their respective cultures, anticipating a closer fit. We find that (1) models have limited alignment with the evidence in the literature; (2) written language has greater effect on LLMs' response than information on participants origin; and (3) LLMs responses were found more similar for East Asian languages than Western European languages.

Reflection is a metacognitive skill that’s essential to creative discovery. As we design interactive technologies for reflection, how might we measure the impact of our designs? In this paper, we develop a coding scheme to explore reflective moments in the speech and language of young children during child-computer interaction. Using cross-disciplinary theories — from the learning sciences to cognitive neuroscience — we define and describe 13 reflective processes occurring within Baumer’s 3 conceptual dimensions of reflection. We then use this framework to measure the impact of a child-robot storytelling interaction with twelve children ages 4–5, and offer developmentally-appropriate transcript examples for each of the 13 reflective processes. This coding scheme provides a practical tool for exploring the impact of our designs on reflection, and can be used to guide design iteration.

The field of natural language processing (NLP) has grown over the last few years: conferences have become larger, we have published an incredible amount of papers, and state-of-the-art research has been implemented in a large variety of customer-facing products.However, this paper argues that we have been less successful than we should have been and reflects on where and how the field fails to tap its full potential.Specifically, we demonstrate that, in recent years, subpar time allocation has been a major obstacle for NLP research.We outline multiple concrete problems together with their negative consequences and, importantly, suggest remedies to improve the status quo.We hope that this paper will be a starting point for discussions around which common practices are -or are not -beneficial for NLP research.

Neural networks have long been at the center of a debate around the cognitive mechanism by which humans process inflectional morphology.This debate has gravitated into NLP by way of the question: Are neural networks a feasible account for human behavior in morphological inflection?We address that question by measuring the correlation between human judgments and neural network probabilities for unknown word inflections.We test a larger range of architectures than previously studied on two important tasks for the cognitive processing debate: English past tense, and German number inflection.We find evidence that the Transformer may be a better account of human behavior than LSTMs on these datasets, and that LSTM features known to increase inflection accuracy do not always result in more human-like behavior.

Many NLG tasks such as summarization, dialogue response, or open domain question answering focus primarily on a source text in order to generate a target response.This standard approach falls short, however, when a user's intent or context of work is not easily recoverable based solely on that source texta scenario that we argue is more of the rule than the exception.In this work, we argue that NLG systems in general should place a much higher level of emphasis on making use of additional context, and suggest that relevance (as used in Information Retrieval) be thought of as a crucial tool for designing user-oriented text-generating tasks.We further discuss possible harms and hazards around such personalization, and argue that value-sensitive design represents a crucial path forward through these challenges.