Characterizing Similarities and Divergences in Conversational Tones in Humans and LLM s by Sampling with People (2024)

Abstract: Conversational tones — the manners and attitudes in which speakers communicate — are essential to effective communication. As Large Language Models (LLMs) become increasingly popular, it is necessary to characterize the divergences in their conversational tones relative to humans. Prior research relied on pre-existing taxonomies or text corpora, which suffer from experimenter bias and may not be representative of real-world distributions. Inspired by methods from cognitive science, we propose an iterative method for simultaneously eliciting conversational tones and sentences, where participants alternate between two tasks: (1) one participant identifies the tone of a given sentence and (2) a different participant generates a sentence based on that tone. We run 50 iterations of this process with both human participants and GPT-4 and obtain a dataset of sentences and frequent conversational tones. In an additional experiment, humans and GPT-4 annotated all sentences with all tones. With data from 1,339 participants, 33,370 human judgments, and 29,900 GPT-4 queries, we show how our approach can be used to create an interpretable geometric representation of relations between tones in humans and GPT-4. This work showcases how combining ideas from machine learning and cognitive science can address challenges in human-computer interactions.

## introduction conversational tones, the manner and attitude in which a speaker communicates, is essential to human communication @xcite . effective communication relies on people's understanding of conversational patterns and tones, and their ability to promptly react to them @xcite . inability to do so results in the content of conversation being "lost-in-translation" between speakers of different languages and cultures @xcite . notably, while traditionally the study of conversational tones involved only humans, the increasing prevalence of large language models (llms) in everyday decision-making, especially their conversational ("chat") variants, renders the study of conversational tones in llms necessary for human alignment @xcite @xcite . developing tools for effectively characterizing conversational tones in humans and llms is hence essential for the development of human-centered ai, human-computer interaction research, and cognitive sciences (figure 1a ). background: conversation research. in conversation research, some literature engages explicitly with the composition and usage of conversational attitudes and linguistic markers @xcite @xcite . a wider array of literature uses conversational analysis to investigate other dynamics that can affect the content of conversation, such as turn-taking @xcite and face-saving @xcite , as well as other cross-cultural semantic differences that can lead to different behavior within the same conversational tone, such as refusal @xcite , shame @xcite , and politeness @xcite . on the other hand, the introduction of large language models, especially its chatbot applications, brings attention to the alignment of conversational behavior in llms with that of human ideals @xcite , which can potentially contribute to the alignment of llms' perception and production of conversational tones with those of humans. being able to effectively fine-tune llms also creates new opportunities to generate text style- how sarcastic is the following sentence: figure 1: summary of our approach. a: problem statement. b: the sampling with people paradigm that aims to collect a representative sample of conversational tones and sentences. c: a quality-of-fit rating procedure that allows us to obtain vector representations of conversational tones with respect to their usage context. d: a geometric representation of the shared embedding space across elicited domains (human, gpt). e: as an application of our obtained data, we benchmark a selection of popular unsupervised cross-domain alignment methods. challenge: biased apriori taxonomy. however, the domain of conversational tones is, like emotion @xcite @xcite and color @xcite , an instance of grounded semantics @xcite . while all participants observe the same stimulus (e.g., an emotional recording, a solid color, or in our case a sentence), people may use different words or labels to describe it (in our case conversational tones such as "polite", "excited", and "grateful") which makes it difficult to study grounded semantics at scale and especially across multiple languages or cultures. one challenge is that studying grounded semantics often involves adopting a predefined taxonomy, typically sourced from previous studies and curated by investigators (e.g.; colors @xcite ; facial emotion @xcite ; musical emotion perception @xcite , concepts such as animal terms @xcite ; sentiment of news items @xcite prosody @xcite . notably, many machine learning datasets also suffer from the same limitation of using a predefined list of using a predefined list of stimuli that can be outdated or unrepresentative of the correspond modality. examples of such datasets span through realms of: object images @xcite , visual scenes @xcite , sounds @xcite , video and its categorizations @xcite , facial expressions @xcite . this strategy is prone to researcher bias, potentially skewing the findings away from an accurate representation of labels as they occur in the real world and within a given culture @xcite . challenge: biased stimulus set. another challenge that almost all studies faced when studying grounded semantics is that they may use a constrained set of stimuli to be annotated (e.g. emotion @xcite @xcite @xcite ; object recognition and similarities @xcite ; word-associations @xcite ; musical perception @xcite ; facial expression @xcite ; prosody @xcite ). this introduces researcher bias, as curating the stimuli may influence the elicited labels, which we outline using the following example. imagine an experiment where a particular semantic term can be associated with some class of objects (e.g., the term "red" can be used to describe red fruits). if the object class is not included in the predetermined list of objects (e.g., red fruits are not included in the list of objects), then the elicited terms will not include this association (we will conclude that "red" does not describe fruits), and it will be missing from the resulting semantic network. bias in object selection can also occur in more subtle ways where a skew in the distribution of selected objects also skews the distribution of elicited terms, potentially even amplifying the initial bias. furthermore, a large body of cross-cultural researchers suggests that studies should not impose a terminology inherited from the experimenter or even from one group of studied agents (e.g., english speakers) on another agent or group of agents (e.g., speakers of another language or demographics; @xcite @xcite . additional challenges. finally, while some studies advocate for the exclusive use of large textual corpora and the extraction of semantic descriptors via data mining @xcite , this indirect approach raises concerns about its ability to accurately represent the nuances of conversational tones as experienced in everyday human interactions. it is also difficult to rigorously compare humans and llms using such corpora because these same textual corpora are also the basis for llm training. notably, @xcite also studies the problem of aligning semantic networks of different individuals or groups in the context of cross-linguistic and cross-cultural comparisons. it turns out that this is a key part of the machine learning problem of automatic translation @xcite . recent research has focused on aligning semantics in humans with large language models @xcite , with significant applications to designing human-computer interfaces @xcite and ai safety. our approach. in light of these challenges, we propose a method that enables the characterization of conversational tones and their taxonomies in any target human population as well as llms, based on a human-in-the-loop sampling with people (sp) technique @xcite @xcite (figure 1 ). specifically, we propose an iterative procedure in which humans and llms are presented with sentences and are asked to label their conversational tones in an open-ended fashion (figure 1b ). the resulting conversational-tone terms are then presented to a new group of agents who are asked to produce sentences reflecting those conversational tones. this process is then repeated multiple times. with mathematical formalism, this process instantiates a gibbs sampler from the joint distribution of sentences and conversational tones in humans and llms @xcite . given the resulting sample, we derive representative sentences and tone taxonomies of our target population, then have an independent group of human evaluators and llms rate the extent to which each tone matched each sentence (quality-of-fit rating; figure 1c ). we use these to construct a geometric embedding that can be used to evaluate the alignment between human and llm conversational tones (figure 1d ). we show how our approach can be effectively used to reveal divergences in the representation of conversational tones between humans and llms. moreover, we demonstrate how our new dataset and cross-evaluations can be used to benchmark unsupervised cross-domain semantic alignment methods used in existing work, and identify which of these work well for cases in which cross-evaluation is not possible (e.g., in multilingual scenarios; figure 1e ). our method can be generalized to many more psycholinguistic modalities (e.g., sentiment, color), languages, and cultures beyond those involved in this paper. we believe it will help advance both human-machine alignment research as well as cross-cultural research. ## discussion using a cognitive science-inspired sampling with people paradigm, we elicited tones and sentences for both humans and gpt creating a shared dataset of sentences and tones. then, via quality-of-fit ratings, we further showed that the degree of alignment for different tones in humans and gpt varies. tone alignment was high for tones such as "joyful" and "pleased" while it was significantly lower for tones such as "proud" and "apologetic". in a separate experiment, we found that similarity judgments were consistent with the resulting relationship between tones. next, by projecting the rating vectors to a joint semantic space, we found variability in tone proximity, which is explained most by the well-known theoretical dimension of valence @xcite . additionally, we provided a mapping of similar tones across humans and llms. we found cases where multiple human tones (e.g., "grateful", "joyful", "happy") were collapsed to a single llm-proposed tone ("pleased"), as well as in the opposite direction (e.g., gpt's "irritated", "annoyed", and "disappointed" collapsed onto humans' "angry"), suggesting that both distributions ended up involving a hypernym for conversational tone categories. finally, we demonstrate how our data can be used to benchmark methods for semantic alignment. we found that bilingual lexical induction surpasses other (geometric) methods, suggesting that it would be appropriate for applications such as machine translation. our work opens up multiple avenues for future research. first, the same approach can be easily extended to other domains. for example, speakers of different languages and different cultures, as well as different language models. second, our dataset can be used as a training signal for better aligning human and llm conversational tones; for example, via an iterative process of refinements like reinforcement learning @xcite . third, future work could look into whether we can use our alignment maps to predict performance in human-ai communication. more broadly, this work shows how combining approaches from machine learning and cognitive science provides routes for better understanding and resolving challenges in humancomputer interactions.