Distilling ChatGPT for Explainable Automated Student Answer Assessment
Authors: Jiazheng Li
Lin Gui
Yuxiang Zhou
David West
Cesare Aloisi
Yulan He
Venue: EMNLP 2023 Findings (2023)
Keywords: Student answer assessment
Rationale generation
Large language model

Abstract: Providing explainable and faithful feedback is crucial for automated student answer assessment. In this paper, we introduce a novel framework that explores using ChatGPT, a cutting-edge large language model, for the concurrent tasks of student answer scoring and rationale generation. We identify the appropriate instructions by prompting ChatGPT with different templates to collect the rationales, where inconsistent rationales are refined to align with marking standards. The refined ChatGPT outputs enable us to fine-tune a smaller language model that simultaneously assesses student answers and provides rationales. Extensive experiments on the benchmark dataset show that the proposed method improves the overall QWK score by 11% compared to ChatGPT. Furthermore, our thorough analysis and human evaluation demonstrate that the rationales generated by our proposed method are comparable to those of ChatGPT. Our approach provides a viable solution to achieve explainable automated assessment in education

Distilling ChatGPT for Explainable Automated Student Answer Assessment
Jiazheng Li', Lin Gui', Yuxiang Zhou', David West", Cesare Aloisi and Yulan Hel 'Department of Informatics, King's College London, UK
PAQA, UK *The Alan Turing Institute, UK uk
(jiazheng... 1i, lin. 1. gugu, yuxiang zhou, {caloisi, dwest org.uk uk
Abstract
Question: List describe three processes used by cells control the movement of substances
the cell membrane. Student Auswer: Active trunsport is when the movement used with the need of energy. Pasive transport is when the movement used without energy. The sodium potassium pump type of movement that requires energy.
Providing explainable and faithful feedback is crucial for automated student answer assess- ment. In this paper, we introduce novel frame- work that explores using ChatGPT, a cutting- edge large language model, for the concurrent tasks of student answer scoring and rationale generation. We identify the appropriate instruc- tions by prompting ChatGPT with different templates to collect the rationales, where incon- sistent rationales are refined to align with mark- ing standards. The refined ChatGPT outputs enable us to fine-tune a smaller language model that simultaneously assesses student answers and provides rationales. Extensive experiments on the benchmark dataset show that the pro- posed method improves the overall QWK score by 11% compared to ChatGPT. Furthermore, our thorough analysis and human evaluation demonstrate that the rationales generated by our proposed method are comparable to those of ChatGPT. Our approach provides a viable solution to achieve explainable automated as- sessment in education
Existing Approuch: Student Answer Assessment Text Classification
Nedraa Astwrr
Key Eleacois
points X
Rehric
Our Approach: Student Answer Assessment Rationale Generation
Matching with Key Elements
"Active transpart... energy"
require energy
"Pasdive transport... without energy"
Applying Rubric
This student answer matches two key stulers meower dids't award any seurv on ielements
I'Dilision..
explesation incompleic
points
Figure 1: Classification based automated answer as- sessment techniques often rely on black-box models, making the assessment process challenging interpret. Incorporating rationale generation can significantly en- hance the transparency the assessment decisions.
2020) tend to frame the assessment task a classifi- cation problem, which involves training text classi- fiers to predict scores given student answers. How- ever, as shown in Figure 1, the feedback provided in terms of scores not sufficiently detailed for students to identify weaknesses in their answers. Besides, it is challenging for humans to interpret the classifiers' decision maaiin process, making classifiers' assessment results less trustworthy. Researchers have advocated for generating ratio- nales to enhance the interpretability of classifiers. These rationales are natural language explanations that substantiate model predictions (Gurrapu et al., 2023). Often, such strategies necessitate rationale annotations on classification datasets for effective training (Camburu et al., 2018) 8). However, most available datasets in student answer assessments only include score annotations. Providing detailed rationale annotation on existing datasets requires significant domain expert efforts. Furthermore, ra- tional annotations are constrained by the specificity of the information in the dataset, making it difficult
1 Introduction
Student answer assessment is a critical compo- nent of the education process. Prompt and in- sightful answer assessments can enhance students' learning experiences and support their academic growth (Nicol and Macfarlane-Dick, 2006). How- ever, manually providing detailed feedback is time- consuming, and differences in assessment criteria among various evaluators result in inconsisten- cies in the grading process (Weigle, 2002).
Various automated student answer assessment models have been proposed in recent years, mostly built on the Pre-trained Language Models (PLMs) (Devlin et al., 2019), making the assessment pro- cess more efficient and consistent. These ap- proaches (Sung al., 2019; Mayfield and Black, Code available at

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generalise across diverse academic subjects.
best of our knowledge, AERA is the pioneering framework that leverages ChatGPT generate ra- tionales for explainable student answer assessments using more compact language models.
Recent developments on Large Language Mod- els (LLMs), including ChatGPT (Stienmon et al., 2020), have demonstrated impressive capabilities in various Natural Language Processing (NLP) ap- plications. For example, these models have ex- hibited remarkable performance in arithmetic and common sense reasoning while showing their po- tential for performing step-by-step reasoning (Wei et al., 2022). Furthermore, Gilardi et al. (2023) found that using ChatGPT for data annotation out- performs crowd workers with much lower costs, It becomes possible to improve the interpretabil- ity of student answer assessment, by harnessing the capabilities of LLMs without relying expen- sive human annotation processes. However, LLMs' running costs, non-open-souree issues and limited specialization still hinder their applications.
Related Work
Automated Student Answer Assessment Also known as automated essay scoring, where most re- searchers model the problem as a text classification task (Uto, 2021). Early approaches (Alikaniotis et al., 2016; Dong et al., 2017) built deep neural networks shed new light on efficient and consis- tent assessment solutions. Recent advents in PLMs (Devlin et al., 2019; Brown et al., 2020) provide better text representations to develop more accurate PLM-based scoring systems (Mayfield and Black, 2020; Yang et al., 2020). Nevertheless, limited knowledge of the assessment system's 's decision- making process raised concerns about its fairness and usefulness. Alikaniotis al. (2016); Yang al. (2020) tried improve assessment interpretability via attention mechanisms. Filighera al. (2022) annotated a student feedback dataset for more ex- plainable assessment results with feedback.
This paper introduces the AERA (Automated Explainable Student Response Assessment) frame- work, designed to harness ChatGPT as reasoning teacher. The aim is to distil more compact lan- guage model, enabling it produce rationales and enhance interpretability on student answer assess- ment. We first designed several prompt templates with different levels of instruction to examine Chat- GPT's capabilities on student answer assessment and rationale generation. Then, we enhance the quality of rationales with a rationale refinement module. Last, smaller language model is fine- tuned the refined data to perform the answer assessment and rationale generation. Since there are no established automatic metrics to evaluate the correctness rationales without ground truth annotations, we conducted a comprehensive human evaluation, assessing the rationales generated by AERA and comparing them with those generated by ChatGPT. Our experimental results show that, within our designed framework, smaller language model can surpass ChatGPT in terms of assess- ment performance while generating more accurate rationales to explain the assessment decision.
Rationale Generation in Text Classification Generate rationales for text classifiers have gained increasing attention due to concerns in interpretabil- ity (Gurrapu et al., 2023; Li al., 2023a). Re- searchers tried to generate rationales various tasks, including sentiment analysis (Antognini and Faltings, 2021), review classification (Liu et al.. 2019), and natural language inference (Camburu et al., 2018). Those approaches mainly fall into two categories: extractive rationale generation (Lei et al., 2016), where rationales are extracted from the input features; and abstractive rationale gen- eration (Marasovic et al., 2022), where rationales are paraphrased from existing sentences or newly generated. L.MSs showcased the great potential to use their in-context learning ability for abstrac- tive rationale generation (Marasovic et al., 2022), which provides viable solution for our task. In our study, we tackle the interpretability chal- lenge in automated student answer assessments by producing abstractive rationales from ChatGPT and distilling smaller model to perform the same task.
In summary, our contributions are: (1) We pro- posed framework AERA, to distil the rationale generation capability of ChatGPT into smaller language model; (2) We introduced two strategies for ChatGPT refine its rationales independently; (3) Through comprehensive experiments and hu- man evaluation, we show that our method is able to generate high-quality rationales without the need additional annotation for model learning. To the
3 AERA: Automated Explainable Student Response Assessment Framework
Applications of student answer assessment systems built on PLMs have been hindered by concerns

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Radioale Generation Templabee
Datasct
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Step Lang Explainable Stndent Answer Assessment
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Scores
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Figure 2: AERA framework contains three steps: (1) Prompting ChatGPT for rationale generation; (2) Applying rationale refinement strategies improve the quality of the rationales; and (3) Distilling smaller language model for more efficient rationale generation.
about their interpretability. Existing explanation methods built on classification- based assessment systems struggle to provide natural language ex- planations for their decision-making process, thus making their application less useful for education purposes. Additionally, the limited availability of datasets annotated with grading rationales, coupled with the substantial expenses of human annotation, poses significant obstacles to the advancement of rationale generation approaches.
tion Q² Key elements K. that list the expected
key answer elements; Rubrics R, a grading guide used to evaluate the quality of student answers A collection of student answers X = {0;]]=m; and a collection of the corresponding scores Y [ When preparing the key elements and rubric used for assessment, lead examiners will also provide sample answer assessments during standardisation meetings*. We denote those sam- pled student answers, scores and grading rationale as (z', 3/z ry), = 1,2,..., M. For a given stu- dent answer Ii, we use Ús to denote the predicted score and A to denote the generated rationale.
To address the above challenges, we introduce AERA framework, which leverages the in-context learning capabilities of LLMs to generate rationales and fine-tune smaller language models for explain- able student answer scoring, As shown in Figure 2, our approach consists of three main steps: (1) We design various prompt templates according to different levels of reasoning difficulties and instruct ChatGPT to assess student answers while provid- ing rationales. (2) It is important acknowledge that ChatGPT may not be able to assess student answers accurately. To address this limitation, we introduce rationale and data refinement module which aims to enhance the quality and usability of the generated rationales. (3) The generated ratio- nales, despite the presence of noise, can be utilized efficiently fine-tune smaller language models, enabling the generation of plausible rationales for student answer assessment.
We use the following notations to describe the model generation process: X - Y, where the model directly predicts score given student an- swer; X YR, where the model predicts and generates a rationale given student answer, XY - R. where both a student answer and its corresponding score are given to the model gen- erate a rationale. For the rest of the section, we highlighted examples from sample assessment in green and models' output in blue.
3.1 Prompting ChatGPT for Rationale Generation
Recent advances in ChatGPT showcased its great potential to generate rationales on complex reason-
SSoe questions contain tabular data. We leverage Chat- GPT's lable-understanding capability create table descrip- tions from tabular data in A.I.
Problem Setup typical student answer assess-
8 Seee $C detailed questions, key elements rubrics. A standardisation guide from leading exam service.
ment dataset includes five components. The ques-

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ing tasks, such as arithmetic computation. How- ever, student answer assessment is complex decision-making process education involving various reasoning phases (Bejar, 2012). The main challenges for an assessment task include finding the valid key elements stated in the student's an- swer and deciding a proper score range that applies to the answer. To the best of our knowledge, there is scarce work researching the viable prompting strategy for student answer assessment with Chat- GPT. Following the taxonomy from Karmaker and Feng (2023), we propose three prompt templates for rationale generation at different reasoning lev- els to explore ChatGPT's reasoning capability and identify the prompt leading to more accurate assess- ment results. We begin with the Simple Instruction template, and progressively reduce the level of rea- soning difficulty by incorporating more elaborate natural language instructions or patterns extracted from assessment samples into the template.
[Rubric]: <R>
[Student answer]: <x,> Carefully read the [Question], [Key Elements], and [Rubric], then compare Student answer] with the [Key Elements], and apply the [Rubric] student score. Please be certain to spell out your reasoning so anyone can verify them. Spell out the [Key Elements that the [Student answer] matches, and also spell out which rule in the [ Rubr ic] is applied.
derive the
(free form of is and 9:)
Listing 2: Complex Instruction Prompt Template
Compared with the Simple Instruction template, the Complex Instruction template offers additional guidance in the assessment process, thereby reduc- ing the level of difficulty.
Example Instruction Although ChatGPT has demonstrated impressive reasoning capabilities in understanding natural language instructions, it faces some limitations when employing zero-shot based templates such as the aforementioned Simple and Complex Instructions. Specifically, it tends to generate free-form rationales that require addi- tional annotations for score and rationale extrac- tion. In addition, it also suffers from hallucination problems. Previous research (Brown et al., 2020; Marasovic et al., 2022; OpenAl, 2023; Karmaker and Feng. 2023) has shown the benefits of few-shot based templates, they allow output formatting through examples, eliminating the need for annota- tions for score extraction. Furthermore, leveraging the patterns from demonstration examples, LLMs can achieve better performance. To this end, we proposed a X + Y R example instruction prompt, utilizing the sample answer assessments obtained from standardisation as demonstration examples, for generating properly formatted rationales:
Simple Instruction We first use a simple X + YR prompt instruction that only contains a single question to ask ChatGPT to elaborate the reason for its scoring process:
[Question]: <Q>
[Key Elements]: <K>
[Rubric]: <R>
[Student answer]: <E,> what score should this Student answer get and why?
(free form of f, and (:)
Listing Simple Instruction Prompt Template
Given the intricate nature of the student answer assessment task, this prompt presents the highest level of difficulty. ChatGPT needs to plan its as- sessment cycle, understand the meaning of key elements and the rubric, and appropriately execute the assessment to match the student answer with the key elements and apply the rubric for scoring and rationale generation.
[Question]: <Q> [Key Elements]: </>>
[Rubric]: <R>
[Student answer]: <r1>>
Previous research suggests that more elaborate natural language prompt in- struction may improve the reasoning capabilities of LLMs (Karmaker and l'nng 2023; Brown et al., 2020). Therefore, we design a more detailed X + YR prompt instruction that clearly outlines the functionality of key elements and the rubric and provides clear guidance on how apply them in student answer assessment:
Complex Instruction
[score and Rationale]: <z/>; <//>
(assessment examples) [Student answer]: <
[score and Rationale]: </a>; <r'y>
[Student answer]: <t,> [score and Rationale]: <jp; <p->
Listing 3: Example Instruction Prompt Template
3.2 Data & Rationale Refinement
Given the lack of established approach to evalu- ate the correctness of generated rationales without
(question]: <①>
[Key Elements]: <k>

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gold annotation, we follow previous study (Ho al., 2023) by assuming the rationale supports the score if the LLM-predicted answer score is correct. However, it is important to note that ChatGPT can- not guarantee the correctness of all the assessed scores on the whole dataset. Incorrect predictions can arise from two scenarios: (1) The dataset con- tains wrongly labelled score; or (2) ChatGPT's predictions wrong. To address these situations, we introduce refinement strategies to improve the rationale generation's 's success rate.
3.3 Distilling Student Model for Efficient Rationale Generation
Although LLMs have exhibited impressive in- context learning and reasoning capabilities, huge parameter size, non-open source issues, and enor- mous running costs (Independent, 2023; et al., 2023b) make them hard to developed and trained locally. Besides, uncontrollable, occasionally un- expected outputs (e.g. hallucination) render LLMs less practical for real-world student answer assess- ment. Consequently,
propose using ChatGPT- generated rationales fine-tune smaller language model for efficient rationale generation. Unlike pre- vious literature that has focused on knowledge dis- tillation in arithmetic tfain-of-thoupee (Ho et al., 2023; Fu et al., 2023; Magister et al., 2023), student answer assessment is a much more complex reason- ing task based on the input source (e.g. the scope of key elements and the definition of the rubric). We utilise the rationales generated by ChatGPT, as described $3.1, with their quality improved by fixing wrongly labelled data and further refinement outlined in $3.2, as training data for task-specific knowledge distillation. We adopt Long T5 (Guo et al., 2022) our base model as T5 is one of the popular open-source PLM that has been pre-trained with many supervised tasks, including both classifi- cation and generation. Besides, prompt for student answer assessment is relatively long, Long T5 is capable of taking longer input than commonly used base models while maintaining little performance drop. Our fine-tuning process takes Question, Key Elements, Rubric, and student answer as input to predict the score and generate rationale, X + YR. Prompt template used for fine-tuning is follows:
Fixing Wrongly Labelled Data ChatGPT, being a non-deterministic language model, can generate varying outputs with each iteration. We utilise the semantic confidence interval for LLMs outlined by Kuhn et al. (2023) to calculate the uncertainty of scores associated with the generated rationales. Based on our observation, generated rationales Å that correspond to the same assessed score 인: are se- mantically similar. Therefore, the predictive proba- bility of each assessed score is can be represented as: p(ù I4:) Enes p(ùs r4); where S is the set of all occurrences of semantically similar rationales shares the same predicted score.
Through our experiments, demonstrate that gold annotations might be wrong for highly con- fident incorrect assessments made by ChatGPT, when the score difference exceeds one. This ap- proach helps to identify corrupted input data and human labelling errors, ultimately reducing data uncertainty and improving overall data quality.
Prompt for Rationale Refinement Since the X YR prompt cannot guarantee the correctness of the score, we introduce XY + Rt rationale refinement template. This template is based on the Example Instruction prompt template and incorpo- rates a given score as input, LLM can use the score as prior knowledge to locate a proper distribution that generates more accurate rationales:
[Question]: <Q> [Key Elements]: <k> [Rubric]: <R>> [Student answer]: <n,> [Score and Rationale]: <8,>; <p>>
Listing 5: Prompt Template for Fine Tuning
[question]] <Q>
[Key Elements]: <K>
[Rubric]: <R>
Experiments
[Student answer]: <ti>
[score and Rationale]; <t/>; <//>
4.1 Experimental Setup
... (assessseent examples)
[Student answer]: <z'g>
Dataset We employ the Hewlett Foundation: Short Answer Scoring (ASAP-SAS) 5 This dataset encompasses over 23,000 short answer re- sponses from students in grades 7 to 10, including
[score and Rationale]: </>>; <|/>
[Student answer]: <x
[Score and Rationale]: <y,>; <f,>
Listing 4: Rationale Refinement Prompt Template

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Dataset (Subject)
#1 (Science)
#2 (Science)
#S (Biology)
#6 (Biology)
Overall
Method/Model
Acc
Fl
QWK
Acc
FI
QWK
Acc
Fl
QWK
Acc
Fl
QWK
Acc
Fl
QWK
X + Y Fine tumed Text Clussification
66.79
67.54
79.17
54.23
51.53
68.53
84.28
45.82
72.87
88.43
54.76
80.30
73.43
54.91
75
Longformer
74.15
83.75
63.21
78.79
58.02
88.09
77.17
81.11
Longformer-ail
72.59
73.61
83.05
59.08
59.52
76.76
86,23
61.50
82.17
87.59
55.82
82.56
76.37
62.61
81.14
X-
YR CharGPT Prompting
Sinple Instruction
49.19
46.19
$8.69
46.86
43.49
56.11
53.01
41.48
42.76
43.91
29.61
41.14
48.24
40.19
49.68
Complex Instruction
55.30
55.28
65..88
38.82 82
38.33
45.06
71.24
41,26
52.94
70.78
52.06
64.73
59.04
46.73
57.03
Example Instruction
55.66
61.40
63.20
54.02
68.17
68.45
50.39
60.31
64.36
Fine tuned Long Rationalizaiio
AERA(Ours)
63.26
62.90
75.06
43.27
42.35
54.15
83.78
53.29
76.44
89.37
60.38
80.81
69.92
54.73
71.62
w/o Fixing Wrong Labels
52.42
50.24
60.66
40.45
35.29
44,26
66,78
50.76
63.65
68.00
40.94
62.54
55.91
44.31
57.78
w/o Rationale Relinement
$2.06
49.46
58.95
40.85
39.09
49,80
61.65
46.47
60.18
66.94
41.18
62.05
55.38
44.05
57.75
Correct Score Only
56.79
53.95
69.96
35.60
24.02
23.94
78.76
48.79
71.83
84.19
54.93
79.17
63.84
45.92
61.23
Table 1: Comparison of performance across classification baselines and rationale generation approaches. The highest QWK has been highlighted in Bold for fine-tuned models and underlined for LLM inference results.
ten questions spanning subjects such as Science, Bi- ology, English, and Art. We only use four subsets focusing on Science and Biology questions.
key answer elements and rubric, into the input, which suggests that the text classifier may make predictions based on spurious features rather than checking student answer against the key answer elements and applying the supplied rubric. Hence, even though text classifiers may exhibit relatively high-performance scores, there remains concern about the trustworthiness of their outputs.
Baselines We compare our method with three classification setups: BERT: Bert-base-uncant model fine-tuned with student answers as inputs and scores output (Mayfield and Black, 2020); Longformer: saagformer-base-4000 fine-tuned with student answers imput and scores as output; and Longformer-all: tuned with the concatenation of additional informa- tion (question, key elements, rubric) and student answers as input and scores as output.
For assessment and rationale generated from ChatGPT, we observe that the prompting under the few-shot setting (Example Instruction) is supe- rior to the zero-shot settings (Simple & Complex Instruction), which achieved the highest overall per- formance with lower variances across four datasets.
segformer-baas-4400 fine-
Once we identified the viable prompt strategy for rationale generation, we fine-tuned Long T5 on the generated rationale for explainable student answer assessment. Our AERA framework obtained the highest overall performance compared with other rationale generation methods. Although the overall performance does not match that of text classifiers, given the intricate nature of the text generation task, noteworthy performance gains are observed on datasets #5 and #6. surpassing those achieved by the BERT classifier. This shows the benefit of enhancing the transparency of automated student answers assessment by generating rationales.
Evaluation Metric We adopt the Accuracy (Acc) and macro fl score (F1) and Quadratic Weighted Kappa (QWK) evaluate the classification perfor- mance. We use sacreBLEU (Post, 2018) measure the rationales' semantic similarity on the validation set and select the best checkpoint.
We provide detailed dataset description, QWK im- plementation and hyper- parameters setup in $B.1.
4.2 Overall Comparison
Table displays the performance of student answer assessment across three task scenarios: fine-tuned text classification, ChatGPT prompting, and fine- tuned Long T5 for rationale generation.
We conducted ablation studies examine the ef- fectiveness of each component in the Data & Ratio- nale Refinement module in our framework. We find that if we only keep a subset of rationales with cor- rectly predicted scores (Correct Score Only), the performance on datasets #1, #5 and #6 surpasses those achieved when incorporating any of the addi-
For text classification baselines, when compar- ing BERT and Longformer, observe that using model that accommodates longer input text length can improve performance when trained solely on student answers. However, we do not see an im- provement in overall performance when incorpo- rating additional information, such as question,
'See $B.4 for detailed data statistic and other comparisons.

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tional refinement strategies. Although these results show the strong performance brought by rationales with correctly predicted scores, this method may not be universally applicable when the amount of data is limited, as seen in dataset #2. After incorpo- rating the two strategies separately, namely Fixing Wrong Labels & Rationale Refinement, to com- pose updated dataset, we observed a significant performance improvement on dataset #2 due to the availability of more data, However, we see a per- formance drop on #1, #5 and #6 when compared with Correct Score Only, indicating the presence of wrongly labelled data or incorrectly predicted ratio- nales can adversely impact the overall performance. In sum, both the data & rationale refinement com- ponents are essential within our framework to pre- vent data scareity and effectively reduce noisy data.
aptly convey pertinent details and its efficacy in substantiating the student answer assessment.
Overall Analysis The left segment of Figure 3 indicates that AERA-generated rationales consider- ably surpass ChatGPT-gennratte ones across both evaluation criteria. Given the inherent challenge for language models to pinpoint key elements resonat- ing with student answers, it's noticeable that scores tend to be lower for the correctness of matched key elements compared to the rubric application' 's faithfulness for both models.
The right segment of Figure 3 underscores marked inclination among annotators towards AERA-geenratte rationales over ChatGPT's. De- spite LLMs sometimes offering more expansive ex- planations due to their in-context learning prowess, they frequently underperform in accurately gaug- ing student answers relative to our refined model, leading to diminished preference rating.
4.3 Human Evaluation
We carried out two distinet human evaluations for rationales generated by both AERA and ChatGPT?.
In summary, the compact language model dis- tilled using the AERA framework not only outper- forms ChatGPT in student answer assessment but also produces more precise rationales, despite its significantly smaller size compared to ChatGPT.
Evaluation Rutionak Currestnos
Evalustion Human Preference
Profeenze
Prfee ABRA
54%
4.4
Case Studies on Refinement Strategies
12
QuaGiF ABIA
Key Hikenata
Rutric
#1: order to replicate this procedures, you would need to Original Label: High Confident Prediction: 0 Rationale: This response incomplete and does not provide any relevant information.
Figure 3: Visualization of Human Evaluation Results
#2: conclusion trial any Original Label: High Confident Prediction: 0
Rationale Correctness The initial evaluation centred the accuracy of rationales. Annotators evaluated the rationales based on two primary crite- ria: (1) Correctness of matched key elements: Eval- uating whether the rationale correctly identifies key elements mentioned by the student's answer. (2) Faithfulness of rubric application: Reviewing if the used rubric corresponds appropriately with the score assigned to the student's 's answer and the ele- ments identified in the rationale.
Rationale: The student's response incomplete and does not provide valid conclusion or any ways to improve the experimental design and/or the validity of the results.
#1: order to replicate this experiment, you would need to know: how they got the mass the four different samples 2. A list of constants 3. You would have know how much of sample you would place into the container of vinegar and if it's the same for all four materials. Original Label:
High Confident Prediction: 2
Rationale: This response describes two additional pieces of information that would be needed to accurately replicate the experiment: "how they got the samples" and "you would have know how much of a sample you would place into the container of vinegar and if it the same for all four materials." The response also mentions list of constants, but it is not clear what specific additional information is being requested.
of the four different
Preference on Rationale The subsequent evalu- ation was tailored towards annotators' preferences concerning the rationales. Annotators were shown rationales generated by AERA and ChatGPT in randomized order. Their task was choose the ra- tionale they deemed superior, basing their decision on factors such as accuracy and informativeness. The chosen rationale was gauged on its ability to
Table 2: Examples of Incorrectly Labeled Data.
Identification of Incorrectly Labeled Data As shown in Table 2, we discover that highly confident incorrect predictions by ChatGPT may actually be correct using the method outlined in $3.2, suggest-
FFrr a comprehensive evaluation sctup, data statistics, IAA scores, and various breakdown evaluation results, refer to $B.5

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#1: The students data needed to include how much the solution was poored. They also needed find oud if the rocks weighed the same when they started.
Original: point; This response describes one piece of relevant information that would he needed accurately replicate the
experiment: "how much of the solution was poured."
Refined: points; This response deseribes little or no accurate or relevant information from the acid rain investigation.
#5: There four mujor steps of protien synthesis. The first is the mRNA going to the rRNA. Then the rRNA takes the information from the mRNA to the IRNA. Next the iRNA will take the information it has and translate it. Lastly, the IRNA will tuke the information and make protein.
Original: 2 points: The student answer matches two key elements," "..mRRNA going to the rRNA..." and "R,A will take the information and make protein...". However, the other two steps are not described accurately or comprehensibly. Refined: point; The student answer matches only one key element, "...mRNA going to the rRNA..."
#6: 1.-Osmosis is how water gets diffused thru the membrane. 2. 2--Active Transport is where an enzyme opens the cell membrane for an object to come in, and extra energy is needed to assist the object into the Cell.3-Paasive Transport where an enzyme opens the cell, but the object doesn't 't need the extra energy to come in.
Original: 2 points: This student answer matches two key elements, "Osmosis... how water gets diffused thru the membrane" and "Passive Transport... enzyme opens the cell. but object closs'' lt need the extra energy to come in". However, the explanation for "Active Transport.."" " is not entirely accurate, as mentions an enzyme opening the membrane, which is not correct. Also, the explanation doesn't 't match the entire description of active transport.
Refined: points; This student answer matches three key elements, "Osmusis... how water gets diffused", "Active transport... enzyme opens the cell membrane for an object to come in, and extra energy is needed" and "Passive transport... enzyme opens the cell. but the object doesn't it need the extra energy to come in".
Table 3: Improved Rationale Examples Using the Rationale Refinement Strategy.
ing that the data may be noisy mislabelled. For example, in the first two cases, student answers incomplete, possibly due to data corruption. The discrepancy between the human labels and the ac- tual answers highlights the clear mismatch in the original dataset. Besides, we also identify instances that may have been annotated with incorrect lower scores. For instance, the last example in the table clearly covers two key elements based on the rubric (highlighted in orange), but the original score given is 0 point. Such mislabeled data could be difficult detect without manual examination in a text clas- sification setup. The above discoveries from the dataset, which have not been highlighted in previ- ous research, serve as a validation of our concern regarding the presence of inconsistent marking stan- dards in large-scale student answer assessments. Our approach provides feasible solution to auto- matically identifying label inconsistency data corruptions in human-annotated dataset.
influenced by the presence of "Other acceptuble responses" stated in the key elements. Determining which part of the response falls into the "accept- able" category can be challenging for ChatGPT. The other two examples demonstrated common mistakes in human annotations that occurred in the dataset. In these two cases, ChatGPT might have misinterpreted some student descriptions, but the refinement step is able to rectify the mismatches in key elements. However, this strategy cannot be applied if the data contains wrongly labelled instances, as ChatGPT will be forced to generate rationales that may not make sense. Given the above observations, we urge the need for future development of student answer assessment datasets to provide enough examples for key elements. This could help mitigate ambiguous definitions and pro- vide clearer guidelines for key elements, thereby reducing confusion and improving the consistency of the student answer assessment process.
Conclusion
Rationale Refinement As shown in Table 3, we demonstrate that by providing the correct assess- ment points in the prompt, ChatGPT is able to im- prove its generated rationale better aligned with the score provided. For example, in the first case, an incorrect key element was initially identified. How- ever, after the correct score is provided to ChatGPT, the model is able to correctly trace back the appli- cable rubric and thus decides that no key elements were mentioned in the text. We discovered that the original incorrect identification might have been
In this paper, we proposed a framework called AERA, which leverages the in-context learning and reasoning capabilities of ChatGPT for rationale generation in student answer assessment. Our ex- perimental results suggest that although ChatGPT is able to generate free-form rationales with natu- ral language instructions, the example instructed prompt strategy achieves the best performance. We
*We provide further experimental details and comprehen- sive ablation studies in $B.

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further demonstrate AERA can effectively distil smaller language model for efficient rationaliza- tion on automated student answer assessment tasks, without the need for additional human annotation on rationales. Extensive experiments and human evaluation results have validated the efficacy of the refinement module, and our distilled language model can outperform the teacher model in grading while providing reliable rationales. Our approach presents a cost-effective and efficient method for explainable automated student answer assessment.
References
Dimitrios Alikaniotis, Helen Yannakoudakis, and Marek Rei. 2016. Automatic text scoring using neural net- works. In Proc. of ACL.
Diego Antognini and Boi Faltings. 2021. Rationaliza- tion through concepts. In Findings of ACI-IJCNLP.
Issac Bejar. 2012. Rater cognition: Implications for validity. Educational Measurement: Issues and Prac- tice.
Iz Beltagy, Matthew E. Peters, and Arman Cohan. 2020. Longformer: The long-document transformer. ArXiv preprint.
Limitations
This study has several limitations. First, there may be variations in the designs of prompt tem- plates among individuals, and the manual prompt performance can differ across different datasets. Moreover, due to the extensive search space in- volved generating automated prompt text, the auto prompt approach cannot be adequately tested with our current computational resources. Second, although appropriate training has been provided for the annotators, the lack of background in exam assessment among the human evaluation annota- tors may have some impact on the quality of the evaluations. Lastly, we identified a trade-off be- tween interpretabilityy and assessment performance. Given the variations base models and structures, bridging this gap remains challenging at present.
Tom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss. Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel M. Ziegler, Jeffrey Wu. Clemens Winter, Christopher Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei. 2020. Language models are few-shot learners. Proc. of NeurlPS.
Oana-Maria Camburu, Tim Rocktäschel, Thomas Lukasiewicz, and Phil Blunsom. 2018. e-snli: Natu- ral language inference with natural language expla- nations. Proc. of NeurlPS.
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. BERT: Pre-training of deep bidirectional transformers for language under- standing. In Proc. of NAACI-HIL.
Ethics Statement
The dataset utilized in this study an open-source collection of anonymous student responses, and does not contain any sensitive or identifiable in- formation. Although we have not identificd any harmful outputs from ChatGPT in our study, it is worth noting that previous research has observed instances where ChatGPT produced unexpected re- sults. We encourage other researchers to utilize this framework to scrutinize the output generated from specific prompts in ChatGPT that may have the potential to generate harmful information.
Fei Dong, Yue Zhang, and Jie Yang. 2017. Attention- based recurrent convolutional neural network for tomatic essay scoring. In Proc. of CoNLL.
Anna Filighera, Siddharth Parihar, Tim Steuer, Tobias Meuser, and Sebastian Ochs. 2022. Your answer is incorrect... would you like to know why? introducing bilingual short answer feedback dataset. Proc. of ACL.
Yao Fu, Hao Peng. Litu Ou. Ashish Sabharwal, and Tushar Khot. 2023. Specializing smaller language models towards multi-step reasoning. In Proc. of the ICML.
Acknowledgements
Fabrizio Gilardi, Meysam Alizadeh, and Maël Kubli. 2023. Chatgpt outperforms crowd workers for text-annotation tasks. Proceedings of the National Academy of Sciences.
This work was supported in part by the UK En- gincering and Physical Sciences Research Coun- cil (grant no. 1P/T017112/2. EP/V048597/1, EP/X019063/1). JL is funded by PhD scholarship provided by AQA. YH is supported by Turing AI Fellowship funded by the UK Research and Innovation (grant no. EP/V020579/2).
Mandy Guo, Joshua Ainslie, David Uthus, Santiago On- tanon, Jianmo Ni, Yun-Hsuan Sung, and Yinfei Yang. 2022. LongTS: Efficient text-to-text transformer for long sequences. In Findings of the NAACL.

---

Sai Gurrapu, Ajay Kulkarni, Lifu Huang, Ismini Lourentzou, Laura J. Freeman, and Feras A. Batarseh. 2023. Rationalization for explainable nlp: A survey. ArXiv preprint.
Chul Sung. Tejas Indulal Dhamecha, and Nirmal Mukhi. 2019. Improving short answer grading using transformer-based pre-training. In Artificial Intelli- gence in Education: 20th International Conference.
Namgyu Ho, Laura Schmid, and Se- Young Yun. 2023, Large language models are reasoning teachers. In
Masaki Uto. 2021. review of deep- neural automated essay scoring models. Behaviormetrika.
Proc. of ACI.
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, brian ichter, Fei Xia, Ed H. Chi, Quoe V Le, and Denny Zhou. 2022. Chain thought prompting clicits reasoning in large language models. In Proc. of NeurlPS.
The Independent. 2023, How much does chatgpt cost to run?
Shubhra (Santu) Karmaker and Dongji Feng. 2023. Teler: A general taxonomy of l1m prompts for bench- marking complex tasks. ArXiv preprint.
Sara Cushing Weigle. 2002. Assessing Writing. Cam- bridge Language Assessment. Cambridge University Press.
Lorenz Kuhn. Yarin Gal, and Sebastian Farquhar. 2023. Semantic uncertainty: Linguistie invariances for un- certainty estimation in natural language generation. In Proc. of ICLR.
Sarah Wiegreffe, Ana Marasovié, and Noah A. Smith. 2021. Measuring association between labels and free-text rationales. Proc. of EMNLP.
Tao Lei, Regina Barzilay, and Tommi Jaakkola. 2016. Rationalizing neural predictions. In Proc. of EMNLP.
Ruosong Yang. Jiannong Cao, Zhiyuan Wen, Youzheng
Jiazheng Li, Zhaoyue Sun, Bin Liang, Lin Gui, and Yulan He. 2023a. CUE: An uncertainty interpretation framework for text classifiers built on pre-trained language models. In Proc. of UAI.
Wu, and Xiaodong He. 2020. Enhancing automated essay scoring performance via fine-tuning pre-trained language models with combination regression and ranking. In Findings of EMNLP.
Jiazheng Li, Runcong Zhao, Yulan He, and Lin Gui. 2023b. Overprompt: Enhancing chatgpt capabili- ties through efficient in-context learning approach. ArXiv preprint.
Hui Liu, Qingyu Yin, and William Yang Wang. 2019. Towards explainable NLP: A generative explanation framework for text classification. In Proc. of ACL.
Lucie Charlotte Magister, Jonathan Mallinson, Jakub Adamek, Eric Malmi, and Aliaksei Severyn. 2023. Teaching small language models reason. In Proc. ACL.
Ana Marasovie, Beltagy, Doug Downey, and Matthew Peters. 2022. Few-shot self-rationalization with natu- ral language prompts. In Findings of NAACI.
Blijah Mayfield and Alan W Black. 2020. Should you fine-tune BERT for automated essay scoring? In Proceedings of the Fifteenth Workshop on Innovative Use of NLP for Building Educational Applications.
David J Nicol and Debra Macfarlane- -ickk 2006. For- mative assessment and self-regur regulaegdl learning: A model and seven principles of good feedback prac- tice. Studies in higher education.
OpenAl. 2023. Gpt-4 technical report.
Matt Post. 2018. A call for clarity in reporting BLEU scores. In Proceedings of the Third Conference on Machine Translation: Rexearch Papers.
Nisan Stiennon, Long Ouyang, Jeffrey Wu, Daniel M. Ziegler, Ryan Lowe, Chelsea Voss, Alec Radford, Dario Amodei, and Paul F. Christiano. 2020. Lear- ing to summarize with human feedback. In Proc. of NeurIPS.

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A Further Framework Details
only provides the training and test sets, we created development set by partitioning the training set in an 8:2 ratio. The detailed train, development, and test splits are shown in Table A1.
A.1 Tabular Data Transformation
Some questions in our dataset contain tabular data, which poses challenge for smaller language mod- cls in terms of inputting and understanding struc- tured data. To address this issue, shown in Figure AI, leverage ChatGPT's dable-understanding ca- pability to create table descriptions from the tabular data and verify the description correctness by hav- ing ChatGPT generate a table based on the descrip- tion". Notably, we found that all the tabular data in our dataset could be accurately reconstructed based on the description generated by ChatGPT. Conse- quently, we replaced all the tabular data from the question part of our prompts with the correspond- ing generated descriptions.
Subset
#1
#2
#5
#6
# Train
1,338
1,023
1,436
1,438
# Dev
334
255
359
359
# Test
557
426
598
599
Table A1: Dataset statistics.
Quadratic Weighted Kappa Implementation Quadratic Weighted Kappa, a widely used metric in evaluating the agreement between two raters in student response assessment, is defined as:
Σx 1i=1 E Eラ= Wiy Eng
Wi Ois
K =
(1)
where I is the score set, is the weighted matrix, calculates as: Wig = matrix and E being the k & expected value matrix.
(f 11
(k-1)7- O k & histogram
Hyperparameter Settings We utilized the Ope- nAl API with the gpt-3. model ver- sion 23 Mar 2023 for the generation of Sim- (/Coomllex/Eamppp instruction-based rationales. Default parameters were retained, with the tem- perature parameter set to 1.0. For our fine-tuning experiments, deployed NVIDIA A100 80G graphics cards. The AERA fine-tuning procedure adopted the Long-t5-tglobal-largg as the foun- dational model. Training for the rationale genera- tion (RG) task was executed with a batch size of 8 over 30 epochs, while the text classification (TC) task used a batch size of 16 across the same number of epochs. We selected learning rates of le-5 for the TC task and le-4 for the RG task, implementing weight decay of 0.01. To ensure robust perfor- mance metrics, each configuration was executed thrice for RG and five times for TC, using random seeds of 210, 102, 231, 314, and 146.
Figure Al: Demonstration using ChatGPT for tabular data and table description transformation.
B Further Experimental Details and Discussions
B.1 Experimental Setup
Dataset In this paper, we employ the Hewlett Foundation: Short Answer Scoring (ASAP-SAS) 10 This dataset encompasses over 23,000 short answer responses from students in grades 7 to 10, including ten questions spanning subjects such Science, Biology, English, and Art. Expert hu- man raters have manually scored each response on a scale 0-2 or 0-3, based on predefined rubrics. Instead of focusing on assessment on the grammat- ical or writing side of the student responses, we are more interested in response assessment on STEM- related guestions. Therefore, we only selected four subsets (#1, #2, #5 and #6) relating Science and Biology from the ASAP-SAS datasets. We didn't 't include other subsets since they are either focused on English and Art or contain multi-modal data (e.g. Graphs) in the question that is difficult to be fed into language models. As the original dataset
Model Implementation We utilized the Hug- gingFace Transformer library for the implementa- tion of models such as BERT (Devlin et al., 2019), Longformer (Beltagy et al., 2020), and L5n (Guo al., 2022).
"HuggingFace Transformer
'"Given the extensive nature of the content within questions. key elements, and rubrics, the combined length with student responses typically exceeds 1,024 tokens. Consequently, our experiments employ models specifically designed to manage inputs from longer documents.
"Obtained with ChatGPT version 13th 2023. "https:/ /waggll..ggeeeeeeeeeeee...eeeela com/ htt.aa//l...cccotttttoto///aaaaa /aapo//////assssssaaaassasssssss

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B.2 Faithfulness of ChatGPT-Gennrraee Rationales w.r.t its Predicted Scores
questions, key elements, rubrics, student answers. and ChatGPT-generrtee rationales (using the Ex- ample Instruction template) as input, and outputs ChatGPT-predicted scores. Conversely, X + Y is tuned under the same classification setting but omits the rationale from the input.
To the best of our knowledge, there is no estab- lished automated evaluation method for assessing the quality of ChatGPT-gennrran rationales. We proposed to design proxy check to verify the faith- fulness of the ChatGPP-gennrated rationale with respect its predicted student answer assessment scores, which can be represented as R Y. We gathered the outputs produced by ChatGPT on our dataset and fine-tuned a text classifier to predict the score de using the generated rationale A as input. In this process, we did not perform any filtering. That is, some of the ChatGPT-predicterr answer scores may be wrong. Our purpose is to establish proxy check if the ChatGPT-generater rationales in- deed faithful explanations of its predicted answer scores. As shown in Table A2, we observe a strong correlation between the CharGGT--ennerted ratio- nales and its predicted corresponding scores across all four datasets. This finding suggests that the ChatGPT-generateer rationales could be considered somewhat faithful explanations of its predicted assessment scores.
Contrary to the consistency findings from (Wiegreffe et al., 2021), where results trended toward O, we noted positive disparities between acc( X + Y) and acc(X + Y), as evident in the table's 's final row. This implies that rationales generated by ChatGPT, utilizing the Example In- struction template, enhance label prediction, pecially for datasets #1, #2, and #5. While the accuracy difference for dataset #6 less than O, there's s marked improvement in F1 and QWK metrics. This suggests that incorporating rationales into the input bolsters class sensitivity and aligns more closely with gold label scores.
In summary, across all datasets, the performance uptick indicates that ChatGPT-produced rationales exhibit commendable quality in simulatability tests. However, dataset #6's outcomes hint that solely focusing on accuracy for evaluations might not be ideal for tasks with nuanced class sensitivity, such as student answer assessment.
#1
#2
#5
#6
Acc
98.69
87.08
98.15
93.10
Fl
98.77
83.47
97.18
95.47
B.4 Results by Fine- Line-Tuning Long T5 on Filtered ChatGPT Outputs
QWK
99.07
90.48
97.87
93.36
Table A2:
Predictive performance on score classification output by ChatGPT using its generated rationales.
In Table A4, we present the statistics of the train- ing set after filtering out instances where ChatGPT predicts wrong answer scores. We observe that when using the Simple Instruction, ChatGPT pre- diets correct answer scores for less than half of the instances. However, with the Complex Instruc- tion, there a notable increase in the number of instances where ChatGPT outputs correct answer scores. Interestingly, the Example Instruction does not yield improvements for dataset #1, #5, and #6. But it enables ChatGPT to predict more correct answer scores for the dataset #2.
Simulatability of ChatGPT-Generated B.3 Rationales w.r.t its Predicted Scores
Wiegreffe et al. (2021) proposed a rationale qual- ity evaluation method based on the association be- tween generated rationale and the predicted label to evaluate the free-text rationale quality. Simulata- bility, instead of relying on the word-level overlap, assesses the ability of generated rationale pre- diet the label by measuring the difference in task performance when the rationale is provided as in- put compared to when is absent:
In Table A5, we show the results by fine-tuning Long T5 on the filtered CharGPT outputs. Consis- tent with ChatGPT's s inference performance, the fine-tuned Long T5 also exhibits performance im- provement when trained on the filtered ChatGPT outputs produced using Complex or Example In- sutrctions compared Simple Instruction. Inter- estingly, although the amount of data is reduced for subsets #1, 45, and #6 when using the Example Instruction compared to Complex Instruction as
ace( RR > O) acc(T + O)
(2)
We conducted an experiment to evaluate the sim- ulatability of rationales generated by ChaiGPT, as detailed Table A3. In this context, X >> Y de- notes a generative classification setting fine-tuned on the Long T5 model. It takes into account

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Dataset (Subject)
#1 (Science)
#2 (Science)
#5 (Biology)
(Biology)
Method/Model
Acc
F1
QWK
Ace
F1
QWK
QWK
Acc
F1
QWK
- XR +
69.96
70.84
82.09
56,57
53.12
57.83
85.62
58.94
79.53
89.20
62.86
83.19
80.91
77.53
85,70
82.39
80.52
87.61
87.34
82.56
88.89
88.48
76.83
89.58
aR +V)
X + Y)
+10.95
+25.82
+1.72
-0.72
Table A3: Analysis on ChatGPT-generated Rationales' Simulatability.
Subset
#5
#6
B.5.2
Human Evaluation Results
# Train
1,338
1,023
1,436
1,438
IAA Results
We use Cohen Kappa for IAA anal-
Simple Instruction
627
412
761
692
ysis.
Complex Instruction
695
407
1,051
1,016
1 Po K
Exumple Instruction
689
477
968
987
1 1 Pe
Table A4: Statistics of the training set after filtering out incorrect ChatGPT-predicted answer scores.
where Po is the relative observed agreement among raters (identical to accuracy), and Pe the hypo- thetical probability of chance agreement, using the observed data to calculate the probabilities of cach observer randomly seeing each category. Our IAA results in Table A7 show that the annotators exhib- ited moderate agreement on the correctness of key elements and the faithfulness of rubric, while they fairly agreed on the preference of rationales.
shown in Table A4, the overall performance is the best for the fine-tuned Long T5 models. We have conducted error analysis on the ChatGPT-gennratee outputs and found that the hallucination problem could be significantly reduced by providing demon- stration examples in the Example Instruction (More discussions can be found in BB.6). For this reason, we decided to use the Example Instruction all our subsequent experiments.
More Detailed Evaluation Results We present brcakdown of cvaluation results for both human evaluation tasks, showing the percentage of correct- ness selections by each annotator in Table A8, and by each subset in Table A9.
B.5 Human Evaluation Details
this section, we provide further details and set- tings on our human evaluation experiments.
B.6 Analysis of ChatGPT Hallucinations
B.5.1
Evaluation Setup
In this section, discuss various hallucination cases observed in the ChatGPT-generated ratio- nales under the zero-shot setting, i.e., using either Simple Instruction or Complex Instruction as de- scribed in $3.1 and without supplying any demon- stration examples. Table A10 demonstrates cases of inconsistent and inaccurate assessments, which can be grouped into five types: (1) Incorrect scoring scale. Despite providing clear 0-3 integer score rubric, ChatGPT occasionally generates rationales that include incorrect score caps, such or 12, or even fractional score scales possibly stem from its knowledge base. (2) Inconsistent assessment. Some rationales display completely contradictory scores in two different places in the rationale text. (3) Uncertain score prediction. In some cases, ChatGPT may ignore the marking rubric and out- puts uncertain scores such as */-2 points". (4) Fac- tual mistake. We observed instances where the matched key elements identified the generated rationales were never mentioned by the student's answer or included the key answer elements. (5)
Data Selection We randomly selected 10% of instances from the run with the highest QWK. Among the sampled data, we further selected 20% for the purpose of calculating the Inter-Annotator Agreement (IAA) score. The detailed statisties of the total sampled data are shown in Table A6.
Annotator Two annotators selected for the evaluation process. Both evaluators are PhD stu- dents with computer science backgrounds and have received training on the evaluation schema and the use of the annotation platform. Each assigned task took about hours to complete, and the annotators were paid fairly at a rate S21.83/hour.
Evaluation Platform As shown in Figure A2, our evaluation is built with 13 The labels are designed to indicate whether an option is con- sidered correct or incorrect based on its selection non-selection.
"htttss//ggthhin wttp:/ttwww.oommbccccno///aaaaae

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Hoge Trasatis
Start extorton
Progress
[Quection]
describe mapor protein
Elementa):
mRKA cults nuelous via nuclear pore.
endoplasmic
12/23 0.
MRNA bases are read in triplets called codens (by IRNA).
complementary (U+A, C+0) anticocion
complementary the mRNA.
The corresponding amino scids on the other end of the 1RNA are bonded to adjacent IRNAA's amino adids SRNA
consepannding smino acid added
Amina
together make protein beg ming with START the site (Initarion))
Amino aciés continue be Inked until STOP codan read on miNN the sile (elongation termination).
IMarking Rubrid]
points: elements,
points: Three key elements:
elements;
points: Other.
[Studint Answer) raamammadda
Please scence the accuracy of the rubric application and the validity of koy elements for the generated ratirnle:
[aationaiae a points, The student snamer is incomplete and dows not match any key elements.
Progress
Dakest
[Question]
Starting with mRNA leaving the nucieus, and desoribe four major steps involved in protein synthesis.
[eyy E Elements]
MRKA
through the eytoptaom the rhoosoe enters mugh andopiasmic reticutimm.
mikk banes read triplats called codons (by (INA).
carrying the complementary (U+A, C4G) anticodon recognizas the complementary the milii. The curnesponding aois other and of URNA bonded educcent (RHAA's amino wcide
correspanding amino
Amino acidis are linked together make protein beginning with START codon in the site (initittio).
continue to
(elangution termination..
IMarking Rubriee:
3 points: Four key eloments:
points: key elements:
point: One two elements
points:
Ancwedfthe
information to tell the cell what codon needs bring complete the chain Once that is done the IRNA cends the
[lationale
[Rationals 83 point, The student ansover matches only one bey elament "..miINIA hoaves the nucious....
Figure A2: Screenshots of the annotation platform for both human evaluation tasks.

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Dataset (Subject)
#1 (Science)
#2 (Science)
#5 (Biology)
#6 (Biology)
Overall
Method/Mudel
Acc
111
QWK
Ave
F1
QWK
Acc:
F11
QWK
Azc
F1
QWK
Acc
F11
QWK
Simple Instruction
43.39
32.39
40.01
23.71
9.97
0.69
68.56
32.82
45.29
79.69
37.14
64.95
53.84
28.08
37.74
Complex Instruction
47.16
38.36
54.48
40.61
29.3
38.3
79.21
42.14
61.63
85.70
43.26
67.73
63.17
38.27
35,51
Example Instruction
56.79
55.95
69.96
35.60
24.02
23.94
78.76
48.79
71.83
84.19
54.93
79.17
63.84
45.92
61.23
Table A5: Evaluating the performance of Long T5 models that have been fine-tuned using rationales generated by ChatGPT prompt with other templates.
Subset
#1
#2
#5
#6
#all
Ann
Ann 2
Total
10% Sampled Duplicate IAA Total
43
60
60
219
Human Evaluation on Rationale Correctness
9
44
Key Flements on AERA
0.86
0.80
0.83
52
263
Rubric AERA
0.96
0.92
0,94
Instances for Rationale Correctness Instances for Rationale Preference
S26
Key Elements ChatGPT
0.60
0.38
0.52
263
Rubric ChatGPT
0.86
0.87
0.86
Human Evaluation on Rationale Preference
Table A6: The statisties of the sampled data for human evaluation.
Prefer AFRA
0.57
0.50
0.54
Prefer CharGPT
0,24
0.22
0.23
No Preference
0.17
0.28
0.23
Tasks
IAA Score
Table A8: A breakdown of evaluation results by each annotator.
Correctness of Key Elements 0.4579 0.5056 Rationalc Preference
Faithfulness Rubric
0.3276
#2
#6
Table A7: Inter-Anmotaoor Agreement results.
Human Evaluation on Rationale Correctness
Key Elements on AERA
0.92
0.72
0.90
0.73
Vague rationale. We observed that zero-shot gen- erated rationales often provide vague or irrelevant explanations for student response, which may not be helpful for feedback and could be difficult to understand.
Rubric on AERA
1.00
0.78
1.00
0.93
Key Elements on ChatGPT
0.48
0.51
0.53
0.54
Rubric on ChatGPT
0.91
0.85
0.96
0.73
Human Evaluation on Rationale Preference
Prefer AERA Prefer ChatGPT No Preference
0.56
0.48
0.56
0.53
0.16
0.29
0.24
0.26
In contrast, using the Example Instruction prompt by supplying some demonstration exam- ples guides ChatGPT to follow structured for- mat for rationale generation and answer scoring. Moreover, instructions that oriented towards examples help ChatGPT to rely less on its knowl- edge base and instead utilise information from the provided resources such as key answer clements and marking rubric. Our analysis reveals that the hallucination problem can be partly alleviated by using the Example Instruction prompt with demon- stration examples. Consequently, we have chosen the Example Instruction prompt our primary ra- tionale generation method.
0.27
0.23
0.21
0.21
Table A9: A breakdown of evaluation results by each subset.
as demonstrated in the examples provided in the prompt, that a score is given first, followed by a rationale explaining the scoring decision.
The refinement of the training data, which in- volved cleaning and correcting some inaccurately generated rationales by providing the actual answer scores input to ChatGPT, has led to stronger correlation between Long T5-generated rationales and the predicted scores. On the contrary, the ChatGPT-gennratee results for #1, #2, and #6 ex- hibit minor discrepancies due to over- wer-hnaching or under-matching certain key elements.
B.7 Example Rationales Generated using AERA vs. ChatGPT
Table A13 shows example rationales generated us- ing the student model, Long T5, in comparison with those generated by the teacher model, ChatGPT. We observe that both the Long T5- and ChatGPT- generated results follow the same structured format
We also noticed small number of mistakes in the Long T5-generated results, primarily at- tributable to the students' vague descriptions, mak- ing it difficult for the language model to compare the answers with the key elements. Additionally,

---

Incorrect scoring scale:
2020), the test performance achieves the highest with all the demonstration examples included.
answer should receive point out of 5. answer should receive 1.5 points out of
B.9 Investigate the Generalizability of AERA
Overall, this student answer receives score of out of (0+0+1+1)) the answer does not accurately and completely...
We wanted to demonstrate that our approach is applicable in a wide range scenarios. To do this, we conducted an ablation study called "leave one out", training our framework on three subsets and testing it the left subset. The results, shown in Table A12, indicate that our framework not only evaluate student answers based the trained question, key elements and rubric; but also generalize well beyond unseen datasets.
Inconsistent assessment:
Score: point This student answer ... Therefore, the answer is not relevant to the question and should receive score of points.
Uncertain score prediction:
Therefore, this answer would receive score of 1-2 points out of
B.10 Rationale Generation from Other LLMs
Factual mistake:
This section presents an example from the #5 to demonstrate that our prompting strategy is still ef- fective for models other than CharGPT, such as Bard or FlanT5. During the experiment design phase, we primarily focused on ChatGPT due to its robust capabilities and cost-effectiveness. The largest open-sourced model We experimented with was the LLaMA-2 70B. However, as shown in Ta- ble A 14, the model struggled produce coherent rationales and often repeated the marking rubrics in its response.
this Student answer includes three of the key elements: selective permeability, passive trans- port, and facilitated diffusion
Vague rationale:
the answer demonstrates some understanding of protein synthesis but is missing several key elements and contains some inaccuracies.
Table A10: ChatGPT hallucination examples from the rationales generated using either Simple or Complex Instruction under the zero-shot setting.
Demo amount
Scores
Acc
F1
QWK
some questions include rubrics such as "other ac- ceptable responses", which are particularly chal-
0,1,2.3,3
79.97
54.66
71.48
4
0,1,2,3
77.96
40.75
68.63
lenging for language models to assess, given their lack of domain-specific background knowledge.
0,1,2
74.62
48.75
66.24
0,1
72.62
42.82
54.90
In summary, our distilled Long T5 model demon- strates a strong capability to assess student re- sponses and generate accurate rationales. De- spite the occasional errors and challenges posed by vague student answers and certain rubrics, the model's 's overall performance is promising for ap- plications in educational settings.
67.45
39.30
57.26
Table A11: Ablation study on the number of demonstra-
tions #6.
Train Sets
Test Sets
Acc
F1
QWK
#2, #5, #6
#1
57.99
56.05
71.20
B.8 Explore the Influence of Number of Demonstration Examples
#1, #5, #6
#2
44.84
40.34
58.42
#1, #2, #6
#5 84.95
39.49
63.02
In this section, have performed an ablation study on the influence of test performance by the number demonstrations provided to ChatGPT this experiment, we gradually reduced the number of demonstration examples included in the prompt to find out the influence on the performance. As we present the #6 in Table A11, aligned with observations reported in prior work (Brown et al.,
#1, #2, #5
#6
85.14
54.10
75.21
Table A12: "leave one out" experiment for method gen- cralizability.
"ChhttPPT version 3 Aug 2023.

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#1 Student Answer: To replicate the group's experiment, the procedure would have to state how much vinegar was poured into the containers. Also, they should specify what kinds of containers they were using as this could affect the results, In the procedure, they also failed to tell what 4 samples were used in the experiment, a key variable to it's success. Score: 3
Long T5: 3 points; This response describes three additional pieces of information that would be needed to accurately replicate the experiment: "how much vinegar was poured into the containers.. kinds of containers they were usd what 4 samples were used in the experiment.,"
ChatGPT: 2 points; This response describes two additional picces of information that would be needed to accurately replicate the experiment: "how much vinegar was poured into the containers." and "what 4 samples were used in the experiment., It also hints at a potential third piece of information necded: "what kinds of containers were used.."
#2 Student Answer: a. Based on the data, the strectchiest polymer plastic of the four is plastic type B, because the both trials, it stretched the most (22 and 23 mm). b. 1.) Add more trials to increase accuracy of the results. 2.) Repeat the experiment with heavier weights to see if affects the results. Score: 2 Long T5: 2 points; The student provides an acceptable conclusion based on the data: th...the stretchiest polymer plastic of the four is plastic type B....." One correct way to improve the experimental design and/or the validity of the results is given: "Add more trials increase accuracy of the results.." The second improvement given, "...trying the experiment with heavier weights to see if it affects the results.," is too vague and does not receive credit.
ChatGPT: points: The student provides an acceptable conclusion based the data: "h.. stretchiest polymer plastic of the four is plastic type B...," Two correct ways to improve the experimental design and/or the validity of the results are given: "1.) Add more trials to increase accuracy of the results....." and "2.) Repeat the experiment with heavier weights
if it affects the results.."
#5 Student Answer: IRNA will transfer it out. Score:
Long T5: 0 points; The student answer does not match any key clements given.
ChatGPT: 0 points; The student answer does not provide any coherent or relevant information on the steps involved in protein synthesis.
Osmosis, Is the movement of water across the membrain. Endocitosis, is the movement of things in the cell. Exocitosis, is the movement of things out of the cell. Score: 1 Long T5: point; This student answer only matches one key element, "Osmosis... movement of water". The other two concepts are incorrect or incomplete.
#6 Student Answer:
ChatGPT: 2 points; This student answer matches two key elements, "Osmosis... movement of water across the membrane" and "Endocytosis... movement of things into the cell" but didn't include an explanation for "Exocytosis".
Table A13: Examples of AERA generated rationales compared with ChatGPT results.
C
Prompt Details
2.Pour vinegar in each of four separate, but identical, containers.
this section, we provide the full detail of the question, key elements and rubric we used in the prompt for each dataset. We highlighted table de- scriptions generated via Tabular Data Transforma- tion mentioned in SA.1 in orange.
3. Placace a sample one material into one container and label. Repeat with remaining samples, placing single sample into single container. 4.After 24 hours, remove the samples from the containers and rinse each sample with distilled water.
C.1 Subset #1
5.Allow the samples to sit and dry for 30 minutes. 6. D.Derrmine the mass of each sample.
[Question]:
A group of students wrote the following procedure for their investigation.
The students's data are recorded the table below.
A table contains four columns: Sample, Starting
Procedure:
Mass (g), Ending Mass (g), Difference in Mass (g).
I. D.etermine the mass of four different samples.
The sample for the first row is Marble, with 9.8

---

Given this student answer from #5 with the Example Instruction prompt template: "I. tRNA tranfers it outside the nucleus to be matched with ribosome2. The ribosome latches on to amino acid.3. The codoms match up4. It creates protein."
ChatGPT: 1 point: The student answer only matches one key element, "...tRNA transfers outside the nucleus to be matched with ribusome....
GPT-4: point: The student answer matches two key elements, "The codons match up" and "It creates protein".
Google Bard: The student answer matches only two key elements, 'IRNA tranfers it outside the nucleus be matched with ribosome' and "The codons match up', so the score is point. Here are the four major steps involved in protein synthesis, starting with mRNA leaving the nucleus: Initiation: mRNA leaves the nuelcus and binds ribosome in the cytoplasm. The ribosome then scans the mRNA for start codon (AUG), Elongation: A iRNA molecule with an amino acid attached to binds the ribosome at the site. The anticodon of IRNA matches the start codon on the mRNA. The amino acid is then added to the growing polypeptide chain. The ribosome then moves one codon down the mRNA. Termination: The ribosome continues read the mRNA until reaches a stop codon. A release factor binds the stop codon and the polypeptide chain is released from the ribosome. Folding and modification: The newly synthesized polypeptide chain is folded into its correct shape. It may also be modified by the addition of chemical groups. The student unswer does not mention the initiation step, which is critical step protein synthesis. Therefore, the answer is not complete and the score is point.
Flan TS XXI: (Exceeded max input sequence length) point; The student answer matches one key clement, "...IRNAA tranfers outside the nucleus be matched with ribosome2. The ribosome latches an amino acid3. The codons match up4. It creates protein.".
LLaMA 2 70B Chat HF: Sure, here's the scoring rubric for the student answers: [Student Answer]: 1. iRNA tranfers it outside the nucleus to matched with ribosome2. The ribosome latches on to an amino acid.3. The codons match up4. It creates protein. [Rationale and Score]: The student answer matches two key elements: "tRNA tranfers it outside the nucleus he matched with a rihosome" (step 1) and "It creates protein" (step 4).* However, the student answer does not mention the other two key elements: "mRNA bases are read in triplets called codons (by rRNA)" (step 2) and "Amino acids are linked together to make a protein beginning with START codon in the P site (initiation)" (step 3). Score: points Here's the corrected scoring rubric:* points: Four key elements;* points: Three key elements; point: One key clement;* points: Other.
puints: Two key clements;*
Table A14: Example output for answer assessment and rationale generation from other popular LLMs.
Starting Mass, 9.4 Ending Mass and -0.4 for Difference in Mass.
in each container.
You need to know what materials to test.
The sample for the second row is Limestone, with 10.4 Starting Mass, 9.1 Ending Mass and -1.3 for Difference in Mass.
You need to know what size/surface area of materials should be used.
You need to know how long each sample was rinsed in distilled water.
The sample for the third row is Wood, with 11.2 Starting Mass, 11.2 Ending Mass and 0.0 for Difference in Mass.
You need to know what drying method to use. You need know what size/type of container use.
The sample for last row Plastic, with 7.2 Starting Mass, 7.1 Ending Mass and -0.1 for Difference in Mass.
Other acceptable responses.
After reading the group's 's procedure, describe what additional information you would need in order to replicate the experiment.
[ubbicc]:
points: The response describes three additional
pieces of information that would be needed to ac- curately replicate the experiment;
Make sure to include at least three pieces of information.
2 points: The response describes two additional pieces of information that would be needed to curately replicate the experiment;
KKy Elements]:
Needed Information: You need to know how much vinegar was used in each container.
1 point: The response describes one additional
piece of information that would be needed to aceu- rately replicate the experiment;
You need to know what type of vinegar was used
point: The response describes little or no accurate

---

relevant information from the acid rain investi- gation.
Different polymer plastics have different stretch- ability (and are therefore suited for different applications).
C.2 Subset #2
A reasonable conclusion cannot be drawn due to procedural errors.
[Question]:
Other reasonable conclusions
A student performed the following investigation to test four different polymer plastics for stretchabil- ity.
Experimental Design Improvements:
Provide the before and after measurements for length (Did the samples all start out the size?).
Procedure:
1. Take sample of one type of plastic, and measure its length.
Make sure the samples are all of the same thickness.
2. Tape the top edge of the plastic sample to a table so that is hanging freely down the side of the table.
Variations in thickness could have caused varia- tions in stretchability.
Perform additional trials.
3. Attach a clamp to the bottom edge of the plastic sample.
Some of the samples have similar stretchability (A and C, B and D).
4. Add weights to the clamp and allow them to hang for five minutes.
Two trials may not be enough conclusively state that one is more stretchable than the other. Indicate how many weights were added to the clamps (Was it the same number for each sample?). Other acceptable responses
5. Remove the weights and clamp, and measure the length the plastic types.
6. Repeat the procedure exactly for the remaining three plastic samples.
7. Perform a second trial (T2) exactly like the first trial (T1).
The student recorded the following data from the investigation.
The table shows the amount of stretch (in millime- ters) for four different types of plastic, labeled as A, B, C, and D. when subjected to two different stretching forces, labeled as and T2.
[Rubric]:
3 points: The response draws valid conclusion supported by the student's 's data and describes two ways the student could have improved the experi- mental design and/or the validity of the results; points: The response draws a valid conclusion supported by the student's data and describes one way the student could have improved the experi- mental design and/or the validity of the results. -or- The response describes two ways the student could have improved the experimental design and/or the validity of the results but fails draw or incorrectly draws conclusion from the student's data;
For plastic type A, it stretched 10mm under T1 and 12mm under T2.
For plastic type B, it stretched 22mm under Tl and 23mm under T2.
For plastic type C, it stretched 14mm under Tl and 13mm under T2.
Lastly, for plastic type D, stretched 20mm under both and T2.
a. Draw a conclusion based on the student's data. b. Describe two ways the student could have improved the experimental design and/or validity of the results.
point: The response draws valid conclusion sup- ported by the student's data but fails to describe, or incorrectly describes, how the student could have improved the experimental design and/or the valid- ity of the results. -or-
[Key Elements]:
Conclusions:
Plastic sample B has more stretchability than the other polymer plastics.
The response describes way the student could have improved the experimental design and/or the validity of the results but fails draw or incorrectly draws a conclusion from the student's s data.;
Plastic sample has the least amount of stretcha- bility compared to the other polymer plastics.
Not all polymer plastics have the same stretchabil- ity.
points: The response provides little or no correct information from the polymer investigation.

---

C.3 Subset #5
to move a substance across the cell membrane. and/or a substance moves from an area of low high concentration, or against the concentration gradient.
[Question]:
Starting with mRNA leaving the nucleus, list and describe four major steps involved in protein synthesis.
Pumps are used to move charged particles like sodium and potassium ions through membranes using energy and carrier proteins.
[Key Elements]:
Membrane-assisted transport occurs when the membrane of the vesicle fuses with the cell membrane forcing large molecules of the cell as in exocytosis.
mRNA exits nucleus via nuclear pore.
mRNA travels through the cytoplasm to the ribo- enters the rough endoplasmic reticulum.
mRNA bases are read in triplets called codons (by rRNA).
Membrane-en Membranc-assisted transport occurs when molecules are engulfed by the cell membrane as endocytosis.
IRNA carrying the complementary (U=A, C+G) anticodon recognizes the complementary codon of the mRNA.
Membrane assisted transport occurs when vesicles are formed around large molecules as in phagocy- tosis.
The corresponding amino acids on the other end of the tRNA are bonded to adjacent tRNA's amino acids.
Membrane-assine assisted transport occurs when vesicles are formed around liquid droplets as in pinocytosis. Protein channels or channel proteins allow for the movement specific molecules or substances into or out of the cell.
A new corresponding amino acid added to the IRNA.
Amino acids are linked together to make protein beginning with a START codon in the P site (initiation).
[Rubric]:
Amino acids continue to be linked until STOP codon is read on the mRNA in the A site (elonga- tion and termination).
3 points: Three key elements;
points: Two key elements;
1 point: One key element:
points: Other.
[Rubric]:
3 points: Four key elements;
2 points: Three key elements;
point: One or two key elements;
0 points: Other.
C.4 Subset #6
[Question]:
List and describe three processes used by cells to control the movement of substances across the cell membrane.
[Key elements]:
Selective permeability is used by the cell mem- brane to allow certain substances to move across. Passive transport occurs when substances move from an area of higher concentration to an area of lower concentration.
Osmosis is the diffusion of water across the cell membrane.
Facilitated diffusion occurs when the membrane controls the pathway for partiele to enter leave a cell.
Active transport occurs when cell uses energy


Reviews:
[{"review_id": "j0r0IsU2n1", "reviewer": "Reviewer_JJVy", "summary": "", "questions": "", "limitations": "", "rating": 3, "confidence": 4, "soundness": 2, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thank you for your review.\n\n**Re Weak Experiments:** \n\n“*Big performance gap between classification model and AERA.*”\n\nAs outlined in the 4.2 Overall Comparison and illustrated in Table 1, our observations reveal that the Longformer classifier exhibits superior performance exclusively with student answers (Longformer), in contrast to cases where student answers are coupled with essential assessment details like questions, key elements, and rubrics (Longformer-all). This outcome suggests that a text classifier-based answer assessment system falls short of simulating human grading behaviours and potentially suffers from spurious correlation. The empirical cases discussed in Section 4.4 also point to the potential corruption of some data or inconsistencies among annotators. Thus, the text classifier-based assessment system inflates its apparent effectiveness.\n\nTherefore, we are motivated to develop a more interpretable assessment system due to this lack of trustworthiness. It is important to note, however, that our framework, as declared in the introduction, does not aim to outperform the existing classifier approach in terms of raw performance. Our focus centres on reframing the current approach to answer assessment by enhancing interpretability through the generation of rationales.\n\nLastly, it's worth noting that the latest ChatGPT version, as of 05 August 2023, has demonstrated significant improvement across various metrics. This progression underscores the potential for AERA to bridge the current performance differential.\n\n“*Experiment on other datasets where ChatGPT demonstrations are not available…*”\n\nOur objective is to develop interpretable student assessment systems to make the application more trustworthy. Nonetheless, due to the absence of a comprehensively annotated dataset offering feedback on student responses related to science, we find it necessary to produce rationales through distillation. This process mandates the utilisation of LLM's demonstrations, as comparing few-shot LLM’s results and fine-tuned smaller language model results have been frequently employed in your suggested literature as well.\n\nUnlike the filtering strategy employed in previous endeavours, the refinement approach within AERA *did not exclude* any instances of ChatGPT demonstrations from the training set. The evaluation result is solely based on the test set, which remained untouched during the training phase. As a result, the test outcomes present an equitable comparison, with both ChatGPT and AERA solely engaged based on inference.\n\n**Re Lack of Novelty:** \n\nWhile previous attempts have involved fine-tuning a smaller model using rationales generated from a larger one, our achievement lies in being the pioneers of a workable solution for explainable student answer scoring. \nAs highlighted in [EMNLP 2020, 2020], we suggest that our work has made both scientific and engineering contributions:\n\n*Scientific contributions*: \n1. Our straightforward yet effective refinement strategy has notably enhanced the performance of the distilled model by leveraging LLM’s in-context learning capability. \n2. Our framework stands out as the first to improve interpretability using LLMs to generate rationales for assessing student answers. \n\n*Engineering contributions*: \n1. Our refinement strategy offers a novel means of identifying corrupted data through an assessment of LLM's predictive uncertainty, which hasn’t been explored in prior works to the best of our knowledge. This strategy significantly mitigates the impact of noisy data annotations, which can hardly be discovered via traditional classification approaches. \n2. Our framework simulates human marking behaviours, and framework workflow can be easily adapted to real-world examination scenarios.\n\nFor the suggested related literature, we have cited [3] and discussed it in our paper, and we will discuss the others in the revised version.\n\n**Reference**:\nEMNLP 2020 (2020) How to write good reviews, EMNLP 2020. Available at: https://2020.emnlp.org/blog/2020-05-17-write-good-reviews (Accessed: 28 August 2023).", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "* The paper focuses on an important and a long-standing application of NLP, that of explainable short answer grading.\n\n* The proposed distillation method is simple and the use of a bigger model for generating explanations to overcome data scarcity issue in short answer grading is interesting. I also liked the refinement prompt, that conditions on the label to improve the rationales. The ablations also show that it helps improve the explanations from ChatGPT.\n", "reasons_to_reject": "* **Weak Experiments**: The paper compares their distilled model AERA (that generates both scores and rationales) to (1) ChatGPT (which also generates both), and (2) some classification models that only output scores. In my opinion, the conclusions drawn from these baselines are weak. The classification models, while they do not generate rationales, obtain an F1 score which is about 9-10 points better than the AERA model (64 vs 55). I understand the accuracy-explainability tradeoff but this is quite a significant drop. On the other hand, comparison to ChatGPT is also somewhat unfair when the experiments are performed on the same dataset for which the model has been tuned. ChatGPT is a zero-shot model while AERA is fine-tuned on a lot of filtered demonstrations from ChatGPT. So the paper needs to experiment on other datasets, for which ChatGPT demonstrations are not available/used to show that their distilled model generalizes.\n\n* **Lack of Novelty**: While the application of short answer grading may be new, the idea of fine-tuning a smaller model with the explanations of a larger model is not novel. The work should at least discuss these papers and talk about how their method differs from these existing distillation papers (and there are quite a lot of them). Listing a few below.\n\n[1] Teaching small language models to reason. Magister et al. 2023\n[2]  Specializing smaller language models towards multi-step reasoning. Fu et al. 2023\n[3] Large language models are reasoning teachers. Ho et al. 2023", "questions_to_authors": "", "paper_topic_and_main_contributions": "The paper develops a distillation method for Short Answer Grading by fine-tuning a smaller model on ChatGPT explanations. The method consists of three steps: (1) generating rationales from ChatGPT, (2) refining those explanations, and (3) fine-tuning a Longformer T5 model on the refined explanations (and labels). The paper conducts experiments on a short answer grading dataset, specifically, ASAP-SAS to show that the distilled model outperforms ChatGPT and generates explanations that are comparable to ChatGPT.", "excitement": 3, "reproducibility": 4, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {}, "extra_text": {}}, {"review_id": "P08YkFQLR9", "reviewer": "Reviewer_PxdN", "summary": "", "questions": "Question A: In the example instruction setting and Prompt for Rational Refinement setting, how many demonstration examples were used within the few-shot setting for ChatGPT?\n\nQuestion B: Following the above question. Did you experiment with varying the number of demonstration examples and subsequently compare the performance results?\n\nQuestion C: The dataset (ASAP-SAS) you used doesn't include the rationale for each score. What are the ground truths for evaluating the generated rationales by Long T5 and ChatGPT? \n\nQuestion D: How much are the running costs of the whole evaluation with ChatGPT API? ", "limitations": "", "rating": 3, "confidence": 3, "soundness": 4, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thank you for your insightful review, and acknowledge our contribution to the education domain.\n\n**Re Reject 1:** \n\n“*The rationale for choosing the ASAP-SAS dataset is not clear…*” \n\nTo the best of our knowledge, a science student answer dataset with publicly available rationales explaining human-assigned scores does not currently exist. This serves as the driving force behind our proposed AERA framework. This framework aims to refine the somewhat noisy rationales generated by ChatGPT, using them to train a smaller model capable of both assessing answers and generating rationales. Our selection of the ASAP-SAS dataset is underpinned by a few key reasons:\n\n- We place particular emphasis on evaluating answers in scientific subjects, and the ASAP-SAS dataset conveniently encompasses four subsets that revolve around Science and Biology questions. \n- The problem structure within ASAP-SAS, wherein student responses are paired with key answer components and then assessed using a grading rubric to determine the ultimate score, closely mirrors the human scoring process employed in real-world scenarios. This likeness suggests that methodologies trained on this dataset can be readily adapted to real examination systems.\n\n**Re Reject 2:** \n\n“*Comparison with varying the number of demonstration examples…*” \n\nWe didn’t make a comparison with varying the number of demonstration examples. We agree to add an ablation study on how different amounts of demonstrations may affect the instruction performance. The analysis of the influence of few-shot performance on the number of demonstrations and selection of demonstrations has been widely researched by [Brown, Tom, et al 2020; Zhang, Zhuosheng, et al 2022], and a common agreement is that more demonstration examples that cover a wide range of scenarios provide better few-shot performance. As mentioned in Re Reject 1, the examination standardisation meeting will provide a few benchmark student answers from all score ranges. Therefore, we expect the user of our framework to adopt as many of those examples as possible. We have conducted the comparison of few-shot performance on the number of demonstration examples in the reply to **Question B**.\n\n**QuestionA:** “*How many demonstration examples were used within the few-shot setting for ChatGPT?*”  \n\nThis varies by how many demonstrations are available from the ASAP-SAS dataset. There are 8 examples for #1 and #2, 6 examples for #5, and 5 examples for #6. Those samples cover all score ranges.\n\n**Question B:** “*experiment with varying the number of demonstration examples*” \n\nFollowing your advice, we have performed an ablation study with the latest ChatGPT version on the influence of the number of demonstration samples on #6, as shown in the table below:\n| Number of demonstrations | ChatGPT Version | Scores for demonstrations | Accuracy | F1 | QWK |\n|---|---|---|---|---|---|\n| 5 | 23 Mar 2023, reported in the paper | 0,1,2,3,3 | 68.45 | 50.39 | 64.66 |\n| 5 | 3 Aug 2023 | 0,1,2,3,3 | 79.97 | 54.66 | 71.48 |\n| 4 | 3 Aug 2023 | 0,1,2,3 | 77.96 | 40.75 | 68.63 |\n| 3 | 3 Aug 2023 | 0,1,2 | 74.62 | 48.75 | 66.24 |\n| 2 | 3 Aug 2023 | 0,1 | 72.62 | 42.82 | 54.90 |\n| 1 | 3 Aug 2023 | 0 | 67.45 | 39.30 | 57.26 |\n\nSimilar to observations reported in prior work [Brown, Tom, et al., 2020], the performance drops with fewer demonstration examples. We also observed the same trend on other subsets.\n\n**Question C:** “*What are the ground truths for evaluating the generated rationales by Long T5 and ChatGPT*” \n\nThere is no ground truth rationale. So, we hired human annotators to evaluate the faithfulness of the rationale on both Long T5 and ChatGPT-generated outputs.\n\n**QuestionD:** “*the running costs of the evaluation with ChatGPT API*” \n\nThe majority of the experiments were carried out before the ChatGPT token prices were reduced. One run for a single dataset cost around $3.33.\n\n**References:**    \nBrown, Tom, et al. \"Language models are few-shot learners.\" Advances in neural information processing systems 33 (2020): 1877-1901.    \nZhang, Zhuosheng, et al. \"Automatic chain of thought prompting in large language models.\" arXiv preprint arXiv:2210.03493 (2022).", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "1. The proposed framework has the potential to greatly influence the education field by making student answer assessments more efficient, consistent, and interpretable. This could lead to improved teaching and scoring mechanisms and enhance learning outcomes, potentially revolutionizing the way educators approach grading and feedback.\n\n2. The paper is appreciated for its clear and fluent writing style. The authors effectively communicate the overall idea, experiment setting, and results, which helps understanding thoroughly of their proposed method and findings.", "reasons_to_reject": "1. The rationale for choosing the ASAP-SAS dataset is not clear, especially given that this dataset lacks rationales for each score, which might limit the evaluation of the generated rationales.\n\n2. There is no clear mention of comparison with varying the number of demonstration examples in the experiments, which could provide valuable insights into the model's performance.", "questions_to_authors": "Question A: In the example instruction setting and Prompt for Rational Refinement setting, how many demonstration examples were used within the few-shot setting for ChatGPT?\n\nQuestion B: Following the above question. Did you experiment with varying the number of demonstration examples and subsequently compare the performance results?\n\nQuestion C: The dataset (ASAP-SAS) you used doesn't include the rationale for each score. What are the ground truths for evaluating the generated rationales by Long T5 and ChatGPT? \n\nQuestion D: How much are the running costs of the whole evaluation with ChatGPT API? ", "paper_topic_and_main_contributions": "This paper introduces a novel framework - Automated Explainable Student Response Assessment (AERA), for automated student answer assessment, which utilizes ChatGPT, a cutting-edge large language model, for both scoring student answers and generating rationales. The approach entails prompting ChatGPT with different templates to obtain rationales, which are then refined to align with standard marking guidelines in cases of inconsistency. The refined outputs from ChatGPT are used to fine-tune a smaller language model - Long T5, which simultaneously evaluates student responses and provides corresponding rationales. The paper demonstrates through extensive experiments on a benchmark dataset that their method improves the overall Quadratic Weighted Kappa (QWK) score by 11% compared to ChatGPT alone. Further, a thorough automatic and human evaluation show that the rationales generated by the proposed method are of comparable quality to those produced by ChatGPT. AERA offers a practical solution for achieving explainable and automated assessment in the field of education.", "excitement": 3, "reproducibility": 4, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {}, "extra_text": {}}, {"review_id": "qcsmvq7eEB", "reviewer": "Reviewer_FgJs", "summary": "", "questions": "A)\tLines 186-189: Would this prompting strategy work for models other than ChatGPT? Moreover, what if the underlying model/interface of ChatGPT changes?\n\nB)\tLines 333-337: I am somewhat skeptical of this approach, especially with the cited prior study only being a pre-print. What is the intuition or justification behind this approach? And why do you think it will hold here? \n\nC)\tLines 352-353: Again, I am skeptical of this claim. Do you have any justification for why this should be the case?\n\nD)\tLines 428-429: Why are only four subsets used?\n\nE)\tTable 1: This table only reports the classification performance, correct?\n\nF)\tTable 1: Was significance testing performed for the comparisons?\n\nG)\tLines 824-839: For reproducibility, it would be helpful to include the learning rate, stopping criteria, warm-up, etc. Moreover, it may be better to do a more thorough hyperparameter search for fairer model comparison.\n\nH)\tAppendix Section B2: My concern here is that this doesn’t necessarily tell us about the quality of the rationale or the semantic relation between the rationale and the score. Hypothetically, the rationale could just be the score itself, which would result in high performance reported in A2. Is there any additional justification beyond just the prediction of the score given the rationale?\n\nI)\tAppendix Section B6: How frequently was this observed to happen?", "limitations": "", "rating": 3, "confidence": 2, "soundness": 4, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thank you for your review.\n\n**Re A):** Indeed, the prompting strategy still works for models other than ChatGPT, such as Bard or FlanT5. Our experimentation centred around ChatGPT primarily due to its robust capabilities and cost-efficiency during the experiment design phase. As highlighted by Kaplan, Jared, et al. (2020), larger language models may generally have better performance, and ChatGPT is larger than other open-source LLMs in our experiment phase. The largest open-sourced model we’ve tried was the LLaMA 2 70B Chat model. However, the model struggled to generate coherent rationales in their entirety and tended to repeat the marking rubrics in its response.  \n\nHere are the assessment results generated by various models on the following student answer with the Example Instruction prompt “*1. tRNA tranfers it outside the nucleus to be matched with a ribosome2. The ribosome latches on to an amino acid.3. The codons match up4. It creates protein.*”\n\n- **ChatGPT**: 1 point; The student answer only matches one key element, “...tRNA transfers it outside the nucleus to be matched with a ribosome...”.\n- **GPT-4**: 1 point; The student answer matches two key elements, \"The codons match up\" and \"It creates protein\".\n- **Google Bard**: The student answer matches only two key elements, ‘tRNA tranfers it outside the nucleus to be matched with a ribosome’ and ‘The codons match up’, so the score is 1 point.\nHere are the four major steps involved in protein synthesis, starting with mRNA leaving the nucleus:        \nInitiation: mRNA leaves the nucleus and binds to a ribosome in the cytoplasm. The ribosome then scans the mRNA for a start codon (AUG).    \nElongation: A tRNA molecule with an amino acid attached to it binds to the ribosome at the A site. The anticodon of the tRNA matches the start codon on the mRNA. The amino acid is then added to the growing polypeptide chain. The ribosome then moves one codon down the mRNA.    \nTermination: The ribosome continues to read the mRNA until it reaches a stop codon. A release factor binds to the stop codon and the polypeptide chain is released from the ribosome.    \nFolding and modification: The newly synthesized polypeptide chain is folded into its correct shape. It may also be modified by the addition of chemical groups.    \nThe student answer does not mention the initiation step, which is a critical step in protein synthesis. Therefore, the answer is not complete and the score is 1 point.\n\n- **Flan T5 XXL** *(Exceeded max input sequence length)*: 1 point; The student answer matches one key element, “...tRNA tranfers it outside the nucleus to be matched with a ribosome2. The ribosome latches on to an amino acid3. The codons match up4. It creates protein.”.\n\n- **LLaMA 2 70B Chat HF**: Sure, here's the scoring rubric for the student answers:    \n[Student Answer]: 1. tRNA tranfers it outside the nucleus to be matched with a ribosome2. The ribosome latches on to an amino acid.3. The codons match up4. It creates protein.    \n[Rationale and Score]:    \nThe student answer matches two key elements: \\\"tRNA tranfers it outside the nucleus to be matched with a ribosome\\\" (step 1) and \\\"It creates protein\\\" (step 4).* However, the student answer does not mention the other two key elements: \\\"mRNA bases are read in triplets called codons (by rRNA)\\\" (step 2) and \\\"Amino acids are linked together to make a protein beginning with a START codon in the P site (initiation)\\\" (step 3).    \nScore: 2 points   \nHere's the corrected scoring rubric:* 4 points: Four key elements;* 3 points: Three key elements;* 2 points: Two key elements;* 1 point: One key element;* 0 points: Other.\n\n**Re B):** The cited prior study has been accepted at the ACL 2023 conference. Due to the absence of verified ground truth rationale data, we posit that the generated rationale supports the assigned score. As far as we are aware, there exists no evaluation metric capable of concurrently assessing both the rationale and the predicted score. Consequently, we proceed with the assumption that the rationale underpins the score, aligning with the prevalent trend observed. Human evaluation and correlation analysis are subsequently provided to validate this assumption as shown in paper 4.3, Appendix B.2, B.3 and B.5, respectively.\n\n**Re C):** We implemented the NLI-based semantic equivalence pipeline from the mentioned prior work. While the majority of the rationales with the same assessed score are categorised as “entail” or “neutral”, it is noteworthy that the remaining instances labelled as “contradict” do not truly exhibit contradictory characteristics. Here are some examples of rationales labelled as “contradict” by the pipeline:\n\n- **Pair 1:**    \n1 point; This response describes one additional piece of information that would be needed to accurately replicate the experiment: “identify the control.” However, the other two pieces of information mentioned, the type of material and the unit of measurement for mass, were already provided in the procedure.    \n1 point; This response describes one additional piece of information that would be needed to accurately replicate the experiment: “identify the control.” However, the other two pieces of information mentioned are not necessary to replicate the experiment as the type of material and the unit of measurement are already provided in the procedure.\n\n- **Pair 2:**    \n2 points; This response describes two additional pieces of information that would be needed to accurately replicate the experiment: “how much vinegar you would put the samples in” and “how long do you rinse the samples for.” However, the second point is unclear and could benefit from clarification.     \n2 points; This response describes two additional pieces of information that would be needed to accurately replicate the experiment: “how much vinegar you would put the samples in” and “how long do you rinse the samples for.” However, the second point is already answered in step 4 of the procedure, so it is not a necessary additional piece of information.\n\nTherefore, we decided to group the noise rationales by the assessed score.\n\n**Re D):** We focus on those Science subsets that offer key answer elements, and are accompanied by rubrics, thus rendering the assessment process a clear reasoning task. Conversely, English and Arts questions from the dataset adopt an Open-ended format and include multi-modal data, which require additional knowledge to make objective judgements. \n\n**Re E):** Yes, the table only reports classification performance since we don’t have the gold rationale data. Optimal checkpoints for rationale generation models are selected based on their BLEU score in comparison to the ChatGPT-generated rationales. Rationales are evaluated by human annotators.\n\n**Re F):** Yes, significance testing was conducted for the comparisons. Specifically, we performed one-tailed t-tests to assess the performance of AERA in comparison with various ablation distilled baselines. The findings are summarized as follows:\n\nThe P-value comparing AERA and the 'w/o Fixing Wrong Labels' baseline is 0.002, indicating a statistically significant improvement with AERA.\n\nSimilarly, the P-value when comparing AERA to the 'w/o Rationale Refinement' baseline is 0.011, again signifying that AERA exhibits a significant improvement over models that do not incorporate this specific refinement strategy.\n\nFor the comparison between AERA and ChatGPT, the P-value is 0.1455. The relatively higher P-value can be attributed to ChatGPT's underperformance in test #2, as elaborated upon in our \"Overall Comparison\" section.\n\nLastly, the P-value for the comparison between AERA and the 'Correct Score Only' baseline stands at 0.1080.\n\nIn summary, AERA generally displays statistically significant improvements over models excluding specific refinement strategies. However, for certain comparisons like AERA vs. ChatGPT and AERA vs. 'Correct Score Only', the improvements, though noteworthy, did not reach conventional levels of statistical significance.\n\n**Re G):** We used learning rates of 1e-5 and 1e-4 for the classification and generation tasks respectively. We set a weight decay of 0.01. We didn’t apply any warmup or stopping criteria. All the runs were concluded within the specified epoch count, and the best checkpoints were chosen by the BLEU score. We will conduct a more thorough  hyperparameter search as you suggested. Regarding your reproducibility concerns, we will release both the source code and all the query results from the ChatGPT for the sake of ensuring reproducibility.\n\n**Re H):** The generated output of our model follows a format of \"x point(s); (explanation based on key elements and rubric)\", which we have defined in the method part as \"$\\hat{y}_i$; $\\hat{r}_i$\". In the experiment outlined in Appendix B.2, we removed the assessed score ($\\hat{y}_i$), retaining solely the rationales ($\\hat{r}_i$) following the semicolon. This step ensures that the classifier is entirely precluded from direct access to the score.\n\n**Re I):** These cases are studied based on manually picked 45 hallucination cases from 6542 complex instruction queried results.\n\n**Re** *Typos Grammar Style And Presentation Improvements*\n \nThank you for your advice. We will adjust the figure size and correct the sentence in the revised version.\n\n**References:**    \nKaplan, Jared, et al. \"Scaling laws for neural language models.\" arXiv preprint arXiv:2001.08361 (2020).", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "The strengths of this paper are as follows:\n\n-\tA demonstration of the ability to distill large language model (ChatGPT) for training smaller language models in the domain of education.\n\n-\tPositive results for distillation, refinement, and improvement over the original ChatGPT model.", "reasons_to_reject": "The weaknesses of this paper are as follows:\n\n-\tThe use of ChatGPT, with it being a closed-source, commercial product, makes it difficult to replicate/reproduce findings and brings into question the generality of the first two steps of the AERA framework. I would suggest repeating the study while using an open-source LLM for replicability and reproducibility, if possible.\n\n-\tA lack of significance testing for the qualitative results.\n\n-\tThe treatment of quality measurement for generated rationales for the second stage (refinement) is a bit confusing.", "questions_to_authors": "A)\tLines 186-189: Would this prompting strategy work for models other than ChatGPT? Moreover, what if the underlying model/interface of ChatGPT changes?\n\nB)\tLines 333-337: I am somewhat skeptical of this approach, especially with the cited prior study only being a pre-print. What is the intuition or justification behind this approach? And why do you think it will hold here? \n\nC)\tLines 352-353: Again, I am skeptical of this claim. Do you have any justification for why this should be the case?\n\nD)\tLines 428-429: Why are only four subsets used?\n\nE)\tTable 1: This table only reports the classification performance, correct?\n\nF)\tTable 1: Was significance testing performed for the comparisons?\n\nG)\tLines 824-839: For reproducibility, it would be helpful to include the learning rate, stopping criteria, warm-up, etc. Moreover, it may be better to do a more thorough hyperparameter search for fairer model comparison.\n\nH)\tAppendix Section B2: My concern here is that this doesn’t necessarily tell us about the quality of the rationale or the semantic relation between the rationale and the score. Hypothetically, the rationale could just be the score itself, which would result in high performance reported in A2. Is there any additional justification beyond just the prediction of the score given the rationale?\n\nI)\tAppendix Section B6: How frequently was this observed to happen?", "paper_topic_and_main_contributions": "This paper proposes the AERA framework, which is meant to help both score student answers and generate score rationales. The framework consists of three steps: (1) prompt ChatGPT to generate rationales, (2) refine the generation step to enhance the rationale quality, and (3) fine-tune a smaller LLM using the generated rationales. The authors find that their framework is selected over ChatGPT in rationale correctness and preference, and that the distilled model is mostly better than ChatGPT regarding score prediction.", "excitement": 3, "reproducibility": 3, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {}, "extra_text": {"Typos_Grammar_Style_and_Presentation_Improvements": "-\tFigure 2: The text in this figure is very small and therefore difficult to read without zooming in. Would it be possible to make the text or figure larger?\n\n-\tLines 895-898: Is the sentence “Indicating that …” supposed to be a part of the previous sentence?"}}]

Author rebuttal:

Meta-review: This paper introduces a novel framework called Automated Explainable Student Response Assessment (AERA), which is capable of distilling ChatGPT as a reasoning teacher to fine-tune a smaller language model, generating score rationales, and improving the interpretability on student answer assessment tasks. The proposed method consists of three steps: (1) generating rationales using various prompt templates with ChatGPT, (2) refining output explanations, and (3) fine-tuning a Long T5 model on these refined explanations and labels. In addition, as part of this process, the quality of rationales is enhanced with a rationale refinement module. The authors run extensive experiments, which show that a smaller language model can surpass ChatGPT in terms of assessment performance while generating more accurate rationales to explain the assessment decisions. Specifically, the proposed method improves the overall Quadratic Weighted Kappa (QWK) score by 11% compared to ChatGPT alone, and a human evaluation confirms that the rationales generated by the new method are of comparable quality to those produced by ChatGPT.

All reviewers highlight **a number of strengths and merits** of this paper:
1. *Motivation*: Reviewer JJVy mentions that the paper focuses on an important and a long-standing application of NLP. In addition, Reviewer PxdN believes that the proposed framework has the potential to influence the education field by making student answer assessments more efficient, consistent, and interpretable.
2. *Methodology*: Reviewer JJVy finds the proposed distillation method simple but effective and the rationale refinement module interesting. Reviewer FgJs agrees with this view.
3. *Evaluation*: Reviewers JJVy and FgJs also find ablation studies useful and informative. 
4. *Clarity of writing and presentation* is listed among the reasons to accept by Reviewer PxdN.

At the same time, reviewers identify **further weaknesses of the current submission** and areas for improvement: 
1. *Concerns about the choice of the dataset* are expressed by reviewers JJVy and PxdN (please see their reviews for more details). While the ideal dataset for this task may not yet exist, these concerns should be addressed in the revision.
2. *Concerns about the experimental setup*: Reviewer JJVy has posed a number of questions about the setup and comparison of the proposed model to the baselines. The authors have tried to address some of these concerns – these answers should be incorporated into the revision. For instance, there does seem to be a trade-off between the performance of the models and their ability to provide rationales, and while it is the latter that was the main focus of this work, this trade-off should be addressed in the paper. This may require comparison to more baselines. In addition, presence of spurious correlations, which contribute to certain baselines' performance, is currently hypothesized in the paper – this needs to be supported by stronger evidence and possibly additional experiments. Please also see further suggestions on the experimental setup from Reviewer PxdN.
3. *Concerns about the use of ChatGPT* are expressed, to various extent, by all reviewers. Reviewer FgJs, in particular, suggests running experiments with open-source LLMs for replicability and reproducibility. The question of generalizability of the findings is also raised by more than one reviewer.
4. *Other suggestions on the presentation of evaluation results* (such as inclusion of significance testing) are expressed by Reviewer FgJs.
5. *Missing references* are also suggested by Reviewer JJVy.
Decision: Accept-Findings