H um BEL : A Human-in-the-Loop Approach for Evaluating Demographic Factors of Language Models in Human-Machine Conversations (2024)

Abstract: While demographic factors like age and gender change the way people talk, and in particular, the way people talk to machines, there is little investigation into how large pre-trained language models (LMs) can adapt to these changes. To remedy this gap, we consider how demographic factors in LM language skills can be measured to determine compatibility with a target demographic. We suggest clinical techniques from Speech Language Pathology, which has norms for acquisition of language skills in humans. We conduct evaluation with a domain expert (i.e., a clinically licensed speech language pathologist), and also propose automated techniques to complement clinical evaluation at scale. Empirically, we focus on age, finding LM capability varies widely depending on task: GPT-3.5 mimics the ability of humans ranging from age 6-15 at tasks requiring inference, and simultaneously, outperforms a typical 21 year old at memorization. GPT-3.5 also has trouble with social language use, exhibiting less than 50% of the tested pragmatic skills. Findings affirm the importance of considering demographic alignment and conversational goals when using LMs as public-facing tools. Code, data, and a package will be available.

## introduction demographic factors like age and gender impact the words we use @xcite and, more broadly, the way we interact and communicate with each other @xcite . moreover, these same factors carry over influence into our conversations with machines. age group, in particular, impacts the way we converse with household dialogue systems like alexa @xcite , conversational agents for health information access @xcite , and intelligent systems for interactive tutoring @xcite . ultimately, to effectively communicate, dialogue systems must adapt and align with the pragmatic skills, semantic understanding, and common sense of their target demographic. despite this, there is limited work on evaluating demographic factors, and in particular, demographic alignment in human-machine conversations. to fill this gap, we propose the novel humbel evaluation framework, foot_0 which measures demographic alignment of language models (lms) with a target user demographic for the first time. while our framework is general, we pay particular attention to modern lms to support the rapid development of these technologies as public-facing tools. in detail, humbel proposes a human-in-the-loop evaluation protocol which collaborates with a field of clinical experts (speech language pathologists) that have already actively studied demographic factors in human-human communication for over 98 years @xcite . these clinical experts administer language exams and compare to normative data (from large, human patient populations) to determine whether a patient aligns with a target demographic (e.g., their peers). humbel works by collaborating with these domain-experts to administer these same tests to a language model (lm), so key differences between lms and human sub-populations are revealed (figure 1 ). to com-plement our human-in-the-loop clinical exams, we also propose a novel statistical test and a suite of existing statistical techniques to confirm clinician findings at scale. while humbel is generally applicable to any (categorical) demographic features, we focus this study on age demographics. most importantly, our evaluation of lm alignment with different age categories can be used to examine robustness in matching conversation applications, but as a side-effect, our techniques are also able to assign a typical human age-equivalent to an lm for a specific language skill. 2 to demonstrate humbel, we evaluate openai's suite of gpt-3.5 models. our key findings quantify gaps in commonsense knowledge (about noun relationships), social language use, and inference skills compared to adult human populations. further, we find inconsistency in language skills compared to normal human development: failures in social and inferential capability are akin to error patterns of a typical 3 year old at worst or 15 year old at best, while success at recollection surpasses a typical 21 year old. results highlight the potential for human-machine miscommunication, when the demographic factors of conversation are ignored. hereinafter, we introduce humbel, contributing: 1. ( § 2.1) protocols for evaluation of demographic factors in lms by domain experts, using clinical exams and detailed clinician error analyses; 2. ( § 2.2) statistical tools to complement clinical techniques at scale via novel statistical tests for demographic alignment and error analysis; 3. ( § 3) comprehensive evaluation of a modern lm (gpt-3.5) using our aforementioned techniques; 4. ( § 3.3) comparison of gpt-3.5 with other modern lms, using our statistical tools; 5. and code, data, and a python package for future researchers to easily apply our tests. 3 2 the humbel framework: human age based evaluation of language models humbel consists of two evaluation protocols. the first (preferred) protocol describes techniques to administer a clinical exam to a lm via prompting, so that results can be carefully analyzed by a clinically licensed speech language pathologist. the second describes automated alternatives, which are easier to conduct more frequently and at scale. ## conclusion we present humbel, which evaluates demographic factors of conversation in language models by using novel clinician-in-the-loop statistical techniques. our framework moves beyond measuring superficial coherence of language models, instead working towards a human-explainable way to test lms for language use and context relevance @xcite , and to compare this language use to the human subpopulations that interact with these models. for example, our techniques provide insight on the utility of lms for inference, information-extraction, and social applications. while the focus of this paper has been on conversational applications -e.g., understanding the communication gaps that may persist between lms and specific human populations -a number of other applications of this framework are also realistic. for one, our tests can establish connections between human development and lms (e.g., to build cognitive models), which may benefit diverse research communities in studying language disorders in humans. moreover, testing alignment between lms and human populations may be useful in evaluation of simulated worlds @xcite to explore how well lms play specific roles. while more interdisciplinary work is needed, we also hope our techniques can be extended to other factors, like in cross-cultural human-machine communication. we make the code and data of our framework publicly available, so future researchers can make use of our suite of automated statistical techniques, and protocols for clinician evaluation.