Automated evaluation of written discourse coherence using GPT -4 (2023)

Abstract: The popularization of large language models (LLMs) such as OpenAI’s GPT-3 and GPT-4 have led to numerous innovations in the field of AI in education. With respect to automated writing evaluation (AWE), LLMs have reduced challenges associated with assessing writing quality characteristics that are difficult to identify automatically, such as discourse coherence. In addition, LLMs can provide rationales for their evaluations (ratings) which increases score interpretability and transparency. This paper investigates one approach to producing ratings by training GPT-4 to assess discourse coherence in a manner consistent with expert human raters. The findings of the study suggest that GPT-4 has strong potential to produce discourse coherence ratings that are comparable to human ratings, accompanied by clear rationales. Furthermore, the GPT-4 ratings outperform traditional NLP coherence metrics with respect to agreement with human ratings. These results have implications for advancing AWE technology for learning and assessment.

## introduction recent advances in large language models (llms; @xcite , and in particular openai's gpt-4 model @xcite , have led to a paradigm shift with regard to what machines can generate, such as coherent writing. we are now witnessing the potential power and exponential growth of ai in education, though the impact of llms used for educational purposes is still largely unexplored. for instance, applications not intended for educational purposes, such as chatgpt, are being used in educational contexts -everyone with access to the internet can now ask chatgpt to complete writing tasks, from generating outlines and ideas, to summarizing documents, to essay writing. with these novel capabilities, we can see immediate advantages, such as leveraging gpt-4 for instructional purposes (e.g., automatic item generation, see @xcite , and disadvantages (e.g., increased plagiarism, see @xcite . in addition, we are learning about current potential shortcomings of llms (e.g., hallucinations or low-quality content generation) due to miscalibrated expectations of what llms can do or the pitfalls of non-optimized prompt engineering. to further our understanding of one innovative application of ai in education, this paper presents an exploratory evaluation of llms for automated writing evaluation (awe). specifically, it is the first study to our knowledge to examine gpt-4's ability to provide a rating (score) and rationale for one aspect of writing quality -discourse coherence quality -in test-taker written responses to an online, high-stakes writing assessment item. discourse coherence is notoriously challenging to satisfactorily assess using awe, and as such, there is great value in determining whether state-of-the-art ai can be used to improve upon prior options. we believe that the method described in the paper should be generalizable to similar datasets that are publicly available. however, caution in the use of gpt-4 ratings is warranted due to limited reproducibility, the possibility of bias, and limited insight into the underlying processes that determine the ratings. ## background in the field of ai in education, awe is one of the most widely researched and mature areas. awe systems evaluate written text quality @xcite @xcite and are widely used for high-stakes writing assessment and instruction. these systems are informed by theoretical writing subconstructs (i.e., factors contributing to writing quality) described in human scoring rubric criteria such as grammatical accuracy, lexical sophistication, relevance, and discourse coherence. these rubric criteria are developed and used by educational testing organizations for scoring purposes and are often informed by education policy (e.g., common core standards, 2010 and council of europe, 2020). awe systems typically provide a holistic score that indicates the overall quality of writing, given a set of rubric criteria. the performance of these scores (accuracy) is then reported through human-system agreement, a well-studied evaluation measure that is typically quite high on modern systems (e.g., @xcite . in recent years, large language models (and earlier models pretrained on unlabeled text) have been leveraged to good effect in various ways to improve awe performance through the use of "transformers", a type of deep learning neural network. for example, @xcite found that the best awe performance was achieved through a model incorporating linguistic features with the bert language model @xcite . more recently, @xcite explored the capabilities of gpt-3 to holistically rate testtaker essays in the toefl11 corpus @xcite . the researchers showed human-gpt-3 agreement rates to be reasonable (exact agreement 54.33%, adjacent agreement 89.15%). the model's performance was then further improved by combining gpt ratings and a range of lexical, syntactic, and cohesion features, resulting in substantial quadratic weighted kappa (qwk) of 0.61. methodologically, it is important note that in their study, the same prompt was used in all conditions, and this prompt did not include examples or ask for rationales for the ratings. to our knowledge, there have been no similar studies with the newer gpt-4 or with comparing different prompt configurations to elicit ratings. while awe systems show strong performance for holistic scoring, scores for discourse coherence quality alone have been a challenging area of nlp research @xcite @xcite @xcite . although some discourse features can be considered "surface-based," for example, pronoun referents and transition terms used in a text, operationalizing aspects of coherence such as the relationship between ideas is less straightforward and involves labor-intensive annotations or less easily interpretable llm-derived features. in particular, it may be difficult to tell whether llm-generated "analyses" of a text actually reflect the same aspects of writing that superficially similar human-written analyses describe. further complicating coherence assessment is the fact that different disciplines, from linguistics (halliday and hasan, 1976) to cognitive psychology @xcite , to education research @xcite , share slightly different views about how coherence is constructed by readers of a text. however, a common thread is that discourse coherence pertains to the textual continuity or flow of a text, that is, the overall sense of unity and meaning that is conveyed by a text. within the construct of discourse coherence, assessment rubrics often directly or indirectly refer to subconstructs such as clarity (how easy to understand ideas and purpose; readability; and impact of lexis/grammar on coherence); flow (sequence/progression of ideas; use of linking words; and referencing); structure (appropriacy of paragraphing; introducing/concluding; and connection between topics); and effect on reader (naturalness of cohesion; appropriacy of cohesive features; repetitiveness; and helpfulness to reader for understanding the response). ## methods in this section we describe the dataset of test-taker responses and the processes for evaluating them through human and automated means. ## discussion and conclusions this study examined the effectiveness of using gpt-4 for assessing written discourse coherence of test-taker responses on a high-stakes english proficiency test. we found that gpt-4 is able to rate the coherence of writing samples with a good degree of accuracy in terms of agreement with the goldstandard human ratings; regardless of the exact order of the prompt (rating-first or rationale-first), the exact agreement rates @xmath0.5 and the @xmath1.8. prompts eliciting rating-only performed slightly worse, though not significantly so. importantly, all permutations of the gpt-4 prompt greatly outperformed a baseline nlp model composed of traditional coherence features. human-gpt-4 agreement rates could likely be improved with further tailoring of the prompt; for example, based on the qualitative analysis, we might suggest additional guidelines to lower the weighting that gpt-4 assigns to spelling errors as it may be overvaluing their importance. studies such as this one have important implications for the field of awe. there is often a tension between designing features that are easily interpretable but provide limited signal (e.g., the number of discourse markers) versus features which are less clearly aligned with human rubrics but which may provide more predictive power (e.g., perplexity of the response under a language model). the promise of ratings based on gpt-4 is that they may bridge this gap by providing quantitative features which seemingly are based on aspects of language of importance to the language assessment community. in the future we therefore expect to see research in a similar vein which looks at further optimizing prompts to elicit ratings and clear, interpretable rationales, especially for subconstructs of writing which have historically been a challenge to measure through automated means. in using llms in this manner, we could reduce the "epistemic opacity" of awe processes @xcite , that is, modern automated assessment could become less of a black box, thereby improving stakeholder confidence in the results. nevertheless, although these results are encouraging, it is important to recognize that the interpretability promised by generated rationales is limited: gpt-4's rationales may not accurately reflect the process used to assign the ratings. in particular, rationales may present rationalizations for decisions actually grounded in biasing features, as was found to be true of cot explanations in @xcite . rationales should therefore not be treated as offering insight into the process of generating ratings, even when they provide true and relevant information about the response. the fact that rationales do not reflect a "thought process" by gpt does not, however, reduce their value in all contexts. as suggested in @xcite , rationales can support language learning by providing instantaneous feedback. in the context of test takers of the det, rationales such as the ones in this report are particularly useful because they are based on task-and constructspecific rubrics. for example, test takers completing a practice test would greatly benefit from feedback tailored to the writing subconstructs, such as discourse coherence, that will be assessed under operational test conditions. gpt-4 could also then be further beneficially exploited by querying it to produce an improved version of the test taker's own response; in other words, a personalized model answer. figure 4 is an example of one such model answer, revising the response from figure 1 . the same prompt as before was used for generating this revision, with the following amendment: now, write a revised version of the following response with improved coherence according to the rubric. stick closely to the original in content, and do not rewrite too extensively; simply improve the organization and complete unfinished ideas. in this revision, we see that the test taker's ideas are maintained, for example, the benefits of learning about how other cultures eat and dance. in addition, the appropriate use of some discourse markers from the original are left intact. in contrast, key coherence weaknesses from the original are addressed, most notably the lack of development of disadvantages and the language inaccuracies which impacted clarity. there remains some repetitiveness in the revision of language from the task prompt, but this issue did not prevent the revised response from being independently rated a 5 (cefr c1) by both gpt-4 and a human rater. as such, this revision would seem a reasonable goal for this particular test taker. on a broader level, the focus of our study, including the importance of transparency, is in line with the larger field of educational ai application development where responsible ai is a key focus @xcite @xcite @xcite department for science, technology & innovation, 2023). as novel ideas, applications, and research questions emerge around the use of llms for educational purposes, it is essential that research communities investigating the use and impact of ai for education build a research agenda. in light of the need to ensure responsible use of ai in education, researchers need to anticipate and pressure test possible uses of ai for education to ensure fairness. i believe that we can learn about different cultures by using books, watching movies, and tv shows. through these mediums, we can gain insights into how people from various cultures live, eat, dance, and celebrate. there are both advantages and disadvantages to learning about different cultures through books, movies, and tv shows. firstly, the advantages include being able to learn about the morals, codes, and ethics of a particular culture. additionally, we can become familiar with their language and observe the hard work and mentality of the characters portrayed in these mediums. on the other hand, there are also disadvantages to this approach. for example, we may encounter negative stereotypes or misrepresentations of a culture, which can lead to misunderstandings and misconceptions. furthermore, relying solely on books, movies, and tv shows may not provide a comprehensive understanding of a culture, as these mediums often focus on specific aspects or situations. in conclusion, while there are benefits to learning about different cultures through books, movies, and tv shows, it is important to be aware of the potential drawbacks and seek additional sources of information to gain a more accurate and well-rounded understanding of various cultures.