C on S i DERS -The-Human Evaluation Framework: Rethinking Human Evaluation for Generative Large Language Models (2024)

Abstract: In this position paper, we argue that human evaluation of generative large language models (LLMs) should be a multidisciplinary undertaking that draws upon the insights from disciplines such as user experience research and human behavioral psychology to ensure that the experimental design and results are reliable. The conclusions from these evaluations, therefore, must consider factors such as usability, aesthetics and cognitive biases. We highlight how cognitive biases can conflate fluent information and truthfulness, and how cognitive uncertainty affects the reliability of rating scores such as Likert. Furthermore, the evaluation should differentiate the capabilities and weaknesses of increasingly powerful large language models - which requires effective test sets. Scalability of human evaluation is also crucial to wider adoption. Hence, to design an effective human evaluation system in the age of generative NLP we propose the ConSiDERS-The-Human evaluation framework consisting of 6 pillars - Consistency, Scoring Criteria, Differentiating, User Experience, Responsible, and Scalability.

## introduction generative tasks in natural language processing (nlp) have to rely on human evaluation, as the current set of automated metrics does not correlate well with human judgment @xcite . human evaluation tends to be expensive and difficult to repeat or reproduce @xcite . even more importantly, an all-too-common scenario tends to be that the evaluation method is fundamentally misaligned with the problem statement @xcite . in the age of generative large language models (llms) with increasing capabilities that can generate fluent content to even fool humans @xcite , ensuring that the human evaluation is set up appropriately to measure the right aspects and reach the right conclusions is crucial. in this position paper, first, we argue that to design and interpret the results of human evaluation accurately, the evaluation pipeline needs to be human-centric in the age of generative ai, accounting for human evaluators and their cognitive biases. the field of user experience (ux) takes into account the emotional states of a user, a.k.a. how a user feels @xcite . it is a well-known fact in ux that users tend to be heavily influenced by aesthetic aspects, while actual function or usability aspects take a second place when users perceive a system as useful, leading to the notion "what is beautiful is useful" @xcite @xcite . aesthetics also extends to language. factors such as fluency can affect the evaluation, outweighing the actual content or substance @xcite . studies in human-computer interface (hci), cognitive, and social psychology have demonstrated that processing fluency -the ease with which information is perceived and processed in the human mind -has a positive effect on evaluation @xcite @xcite . current stateof-the-art (sota) llms tend to be quite fluent and produce content that is easy to read and understand, and as a result, users can conflate fluency and usefulness. therefore, we need to closely examine our human evaluation before reaching conclusions such as -the llm can perform @xmath0. nlp evaluation procedures, therefore, at the very least must delineate style vs. substance. secondly, the effectiveness of the test set in measuring the capabilities of a model is critical, as ineffective test sets cannot adequately evaluate these models, a common theme that has surfaced in many leader-boards and public data sets @xcite . hence, in this position paper, we make the following contributions: ## considers-the-human framework 3.1 user experience cognitive biases play a key role in how humans judge or rate the system. despite this, there is little reporting of the influence of these biases in nlg tasks with human evaluation @xcite . in this section, we make the case as to why ux and the psychology of cognitive biases are crucial components of human evaluation in nlp. since ux and the psychology of cognitive biases are entire fields on their own, it is impossible to cover all the details in this paper. we highlight the significant impact of cognitive biases on human evaluation of nlg tasks that can lead to incorrect conclusions. processing fluency -the ease with which information is processed by the human mind @xcite -affects factors such as perceived truthfulness and usefulness of statements. these lessons from psychology also apply to nlg human evaluation, where the human evaluation strategy needs to isolate the effects of linguistic fluency vs. aspects such as factual correctness. as presented in the introduction section, the notion "what is beautiful is useful" @xcite also extends to language, where information that is presented in an easy-to-process manner can be perceived as true @xcite . to be clear, we are not calling for linguistic fluency and coherence to be trivialized. on the contrary, we highlight how powerful its influence is on human judgment and evaluation. in the following section 3.1.1, we provide a few tips on how to isolate such effects in nlg evaluation. ## conclusion & moving forward we presented the considers-the-human evaluation framework to keep up with the increasing capabilities of llms. we highlight the effects of human emotions and cognitive biases on evaluation, given how commonly the perception-based metrics are used to evaluate aspects such as truthfulness. we, hence, encourage researchers in nlp to collaborate with their counterparts in ux, hci & psychology to ensure that the evaluation measures the right things the right way and the results are interpreted accurately. we also call for further research in critical areas -including curating effective test sets, scalability of human evaluation, and responsible ai components such as privacy, bias, robustness, and safety considerations, when evaluating increasingly powerful & ubiquitous generative llms.