Large language models-based metric for generative question answering systems

IAES International Journal of Artificial Intelligence (IJ-AI)
Vol. 14, No. 1, February 2025, pp. 151∼158
ISSN: 2252-8938, DOI: 10.11591/ijai.v14.i1.pp151-158 ❒ 151
Large language models-based metric for generative
question answering systems
Hazem Abdel Azim, Mohamed Tharwat Waheed, Ammar Mohammed
School of Computing and Digital Technologies, ESLSCA Univeristy, Cairo, Egypt
Article Info
Article history:
Received Mar 20, 2024
Revised Aug 13, 2024
Accepted Aug 30, 2024
Keywords:
Evaluation metrics
Generative question answering
Large language models
Likert-scale scoring
Zero-shot prompting
ABSTRACT
In the evolving landscape of text generation, which has advanced rapidly in re-
cent years, techniques for evaluating the performance and quality of the gen-
erated text lag behind relatively. Traditionally, lexical-based metrics such as
bilingual evaluation understudy (BLEU), recall-oriented understudy for gisting
evaluation (ROUGE), metric for evaluation of translation with explicit order-
ing (METEOR), consensus-based image description evaluation (CIDER), and
F1 have been utilized, primarily relying on n-gram similarity for evaluation.
In recent years, neural and machine-learning-based metrics, like bidirectional
encoder representations from transformers (BERT) score, key phrase question
answering (KPQA), and BERT supervised training of learned evaluation met-
ric for reading comprehension (LERC) have shown superior performance over
traditional metrics but suffered from a lack of generalization towards different
domains and requires massive human-labeled training data. The main contribu-
tion of the current research is to investigate the use of train-free large language
models (LLMs) as scoring metrics, evaluators, and judges within a question-
answering context, encompassing both closed and open-QA scenarios. To val-
idate this idea, we employ a simple zero-shot prompting of Mixtral 8x7 B, a
popular and widely used open-source LLM, to score a variety of datasets and
domains. The experimental results on ten different benchmark datasets are
compared against human judgments, revealing that, on average, simple LLM-
based metrics outperformed sophisticated state-of-the-art statistical and neural
machine-learning-based metrics by 2-8 points on answer-pairs scoring tasks and
up to 15 points on contrastive preferential tasks.
This is an open access article under the CC BY-SA license.
Corresponding Author:
Hazem Abdel Azim
School of Computing and Digital Technologies, ESLSCA Univeristy
Cairo, Egypt
Email: hazem.abdelazim@eslsca.edu.eg
1. INTRODUCTION
Question answering (QA), dating back to the seminal work of Hirschman and Gaizauskas [1], has
long aspired to equip computer systems with the ability to furnish accurate and pertinent responses to posed
inquiries, leveraging either predefined context or curated knowledge bases. QA systems are typically decom-
posed into two key components [2]: a retriever and a reader. The retriever’s function is to search among an
extensive collection of passages and retrieve the most relevant passage given the query. The reader’s function is
to comprehend the passage and answer the query from the given passage or set of passages retrieved. The cur-
rent research focuses on the reader component, namely, the reading comprehension (RC) task, and in particular,
different metrics are used to measure the performance of the RC task.
Journal homepage: http://ijai.iaescore.com

152 ❒ ISSN: 2252-8938
Traditional RC-QA systems [3] rely on extractive ”span-based” QA, whether in a closed or open
domain. Span-based extractive QA means giving a passage and a question, and the task of the AI model is
to extract the answer from within the passage in a span [start-end] indices. Accordingly, the metrics used to
evaluate those systems were designed to capture lexical-based similarities between the model answers and the
ground-truth ideal answers created by human annotators. Recent QA systems are generative [4], sometimes
known as ”abstractive” QA, cater for generating a ”semantically” correct answer from within the passage, and
do not necessarily capture a span of answers. More advanced metrics are required to evaluate those generative
responses.
Generally, the current landscape of QA metrics can be categorized into three broad categories:
lexical-statistical metrics, embedding-based metrics, and neural bidirectional encoder representations from
transformers (BERT)-based models [5]. Lexical-statistical metrics are the more conventional metrics used
for several years. They rely on token matching, whether exact match (EM) or relaxed (F1-score), with different
n-gram variants. These metrics include bilingual evaluation understudy (BLEU), recall-oriented understudy for
gisting evaluation (ROUGE), metric for evaluation of translation with explicit ordering (METEOR), consensus-
based image description evaluation (CIDER), EM, and F1-score. BLEU is precision-centric and widely used
in evaluating translation tasks [1]; ROUGE is recall-centric and commonly used in summarization tasks [6].
Although these traditional metrics have provided acceptable performance for span-based extractive QA sys-
tems, they suffer from critical drawbacks as they do not capture semantic features in the tokens.
On the other hand, the semantic capturing aspect has been addressed in the second category of
embedding-based metrics, which utilize token embeddings to provide a more nuanced similarity score and
mitigate the limitations of lexical metrics [7], [8]. While these metrics offer flexibility and improve QA
scoring compared to lexical metrics, they nonetheless encounter challenges adapting to specific contexts due
to their static nature, failing to consider the contextual nuances of tokens within questions or answers [9].
For instance, a word like ”bank” would yield the same static embedding vector in different contexts, such as
”depositing a paycheck in the bank” and ”crossing the river bank”. Those limitations were handled in the third
category: Neural BERT-based models, using different variants of BERT architectures [10], to capture contextu-
alized embeddings, which showed superior performance correlating with human judgments compared to other
categories. Several models were reported recently like BERTscore [8], which relies either on words or contex-
tualized embeddings and cosine similarity to generate a numeric score. Bilingual evaluation understudy with
representations from transformers (BLEURT) [11] is a refined version of BERTscore that empowers augmented
synthesized data to train the model. Another advanced version uses BERT to train the model to learn certain
critical weights for each token, like in key phrase question answering (KPQA) models [9]. The refinement here
is that instead of treating tokens in the model answer and ground truth gold answers equally, they are weighted
based on their importance in answering the question. Standard BERT architecture is followed by a softmax
classifier layer to generate the weights for each token, and those weights are incorporated into conventional
metrics like ROUGE, BLEU, and the BERTscore metric. A BERT-based direct supervised learning approach
adopted by [12], which learns the required rating directly using massive training labelled data. The model is
called learned evaluation metric for reading comprehension (LERC). This model is based on BERT architec-
ture that has undergone fine-tuning based on human judgment scores. LERC takes as input a passage (context),
question, reference, and candidate, and the output score measures the accuracy of the candidate as compared
to the ground truth human judgement. The preceding neural-BERT systems have demonstrated significantly
superior performance to traditional lexical and static embedding metrics, especially within the domains for
which they are trained. However, they are hindered by a complex training procedure, necessitating costly
manual annotation of samples due to their reliance on large amounts of human-annotated data for training.
Additionally, they exhibit limited generalization across various domains, and there is still more room for
improvements on out-of-distribution data, particularly on contrastive pairs [12].
Recently, a fourth category based on using large language models (LLMs) in scoring as a judge shows
significant promise compared to the preceding three categories. Utilizing LLM with carefully crafted prompts
[13] has demonstrated remarkable success in various tasks, both within academic benchmarks [14] and real-
world settings [15]. However, to our knowledge, no published research has yet reported on using LLMs as
a scoring agent for RC tasks in a QA context to mimic the human judgments on a Likert scale and the sim-
pler binary tasks for correct/incorrect answers. Thus, this research’s primary contribution lies in exploring
a fourth category, employing GPT LLMs and zero-shot prompting to assess the correlation between model
scores and human judgments compared to other state-of-the-art QA metrics. We conducted experiments using
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this proposed approach on 10 state-of-the-art datasets [12], [16]–[23], encompassing diverse domains and QA
styles. The first eight datasets feature human scores based on a 5-Likert scale, while the last two consist of
binary openQA datasets. In these latter datasets, the task assigned to the LLM is to determine whether the
answer is correct compared to a gold (ground truth) answer. Through careful prompting, we directed the LLM
to execute the scoring task. This discriminative task is particularly challenging, especially with the 5-Likert
scale judgment, as the LLM must distinctly differentiate between closely labelled categories between 1 and 5.
The rest of the paper is organized as follows: in section 2, we discuss the research method, in
subsection 2.1., we discuss the proposed LLM mixtral-based scoring model and subsection 2.2. describes
the datasets employed in this research, providing a foundation for the empirical analysis. The findings from
our empirical analysis across all datasets are presented and discussed in section 3. Finally, section 4 concludes
the paper with a summary of key insights and conclusions drawn from the research.
2. METHOD
In this section, we describe the methodology used in our research to develop and evaluate the proposed
LLM-based metric, as well as the the datasets used for evaluation.
2.1. Large language model-based metric
The proposed metric in our research is based on capitalizing on open-source Mixtral 8x7B LLM
reasoning capabilities as a scoring machine, which we will call LLM-Mixtral. This research answers a
hypothesis about whether simple prompt-based zero-shot open-source LLM can outperform all state-of-the-
art existing metrics we have covered in the previous sections and correlate better with human judgements. We
formally design a prompt containing a question, q, a gold (reference) answer and a model generated answer
AI-generated answer as (1):
ŷ = MLLM(prompt) (1)
The predicted score ŷ is then compared to the corresponding human judjements example of prompt that can be
applied as an input to (12): ”Here is a question, a set of golden answers (split with /), an AI-generated answer.
Can you judge whether the AI-generated answer is correct according to the question and golden answers,
answer Yes or No.”
We used several prompts depending on the task and the dataset used. An example is shown in Figure 1,
to instruct the LLM-Mixtral to generate a human-like judgement on how well the hypothesis candidate answer
is aligned semantically with the ground truth reference answer. The predicted judgment ŷ could be on a Likert-
scale from 1-5 for the first eight datasets or binary judgement (correct/incorrect) for the last two datasets, as
will be explained in the next section.
Figure 1. Zero-shot prompt applied to LLM-Mixtral model
Large language models-based metric for generative question answering systems (Hazem Abdel Azim)

154 ❒ ISSN: 2252-8938
2.2. Datasets used in question answering evaluation
Numerous benchmark datasets are available in the literature for evaluating QA. We selected datasets
that were deployed in the same research setting, by comparing the metric with human judgments mostly on a
Likert scale from 1 to 5 where 5 is the most relevant model answer compared to the gold - ground truth answers.
Datasets utilized in our experiments is summarized in Table 1.
Table 1. Summary of datasets
Dataset Description References
NarrativeQA Benchmark for GenQA metrics, with short answers averaging 4.7 words. [17], [24]
SemEval Used for GenQA metrics, with very short answers averaging 2.5 words. [16], [17]
MS-MARCO Contains human judgments for model-generated answers, known for longer responses. [17]
AVSD Collected human judgments on model responses, with longer and complex answers. [17]
MCScript Evaluates reasoning within stories for children, assessing comprehension skills. [16]
CosmosQA Focuses on commonsense reasoning through everyday blogs, assessing real-world reasoning. [18]
SocialIQA Evaluates social reasoning from knowledge-base passages, focusing on social interactions. [19]
Quoref Assesses coreferential reasoning within Wikipedia articles for language comprehension. [20]
Contrastive pairs Consists of contrastive answer pairs for evaluating models against human judgments. [12]
EVOUNA (NQ, TQ) Aggregates outcomes from various Open-QA models on NQ and TQ datasets. [21]–[23]
3. EXPERIMENTAL RESULTS
We tested our proposed LLM—Mixtral metric on ten different datasets and compared it with all the
methods covered in section 3. We chose Mixtral 7 B because very little research has tackled this problem
using open-source models, and most of the related research in this area used closed GPT models (OpenAI and
Claudera), which are paid services. The second reason is that Mixtral 8x7 B is one of the top performing open
source models [25] on general tasks with relatively fewer parameters than many open source LLMs. Mixtral
notably exhibits superior performance, matching or surpassing Llama 2 70B and GPT-3.5 on public tasks, with
remarkable results in mathematics, code generation, and multilingual tasks. So, we investigate the model’s
performance in this challenging closed specific task of Likert-scale scoring of QA-generated answers versus
human judgments. The third reason is that open source models, for privacy reasons, are more appealing for
some government and private sector enterprises where the criticality of data privacy is very high, and they
prefer to have their data on-premises, which is achievable using open source models.
3.1. Experiment I: comparison with key phrase question answering metric
We benchmarked LLM-Mixtral against the datasets used in [9]. Based on the LLM - prompt in
Figure 1, the resulting output is parsed to get the Likert scale judgment from 1 to 5. The question,
candidate answer, and ground truth reference are grabbed and applied to the LLM model for each dataset.
The Pearson correlation coefficient is computed for the LLM-Mixtral and human judgments.
As depicted in the results in Table 2, the simple proposed model LLM-Mixtral outperforms all metrics
on average and for 3 out of four datasets. Test sets are used in the comparative study. The lexical metrics are
far behind in terms of correlation with human judgments. The KPQA provides a relatively good correlation
but, on average, is somewhat less than the simple LLM-Mixtral metric.
Table 2. Benchmarking LLM-Mixtral against Lexical and KPQA metrics [18]
Metric MS-MARCO AVSD Narrative-QA Sem-Eval Average
BLEU-1 0.349 0.58 0.634 0.359 0.4805
BLEU-4 0.193 0.499 0.258 -0.035 0.22875
ROUGE-L 0.309 0.585 0.707 0.566 0.54175
METEOR 0.423 0.578 0.735 0.543 0.56975
CIDER 0.275 0.567 0.648 0.429 0.47975
BERTScore 0.463 0.658 0.785 0.63 0.634
BLEU-1-KPQA 0.675 0.719 0.716 0.362 0.618
ROUGE-L-KPQA 0.698 0.712 0.774 0.742 0.7315
BERTScore-KPQA 0.673 0.729 0.782 0.741 0.73125
LLM-Mixtral 0.691 0.749 0.818 0.777 0.75875

3.2. Experiment II: comparison with learned evaluation for reading comprehension metric
We benchmarked LLM-Mixtral on different datasets and different models presented by the authors in
[12]. The results are shown in Table 3. BERT semantic textual similarity benchmark (STS-B) is a BERT-base
model fine-tuned on the sentence similarity task, STS-B [26]. LERC, as described in section 3, is a LERC
based on supervised fine-tuning and a 40k+ dataset. The proposed LLM-Mixtral metric outperformed BERT
STS-B and LERC on average, except for the Quoref dataset. LERC, the second performer after LLM-Mixtral,
was competitive in two datasets, CosmosQA and Quoref. In general, it produced an average correlation of
0.744, while our proposed LLM-Mixtral achieved a moderate correlation of 0.882, a remarkable increase of
14 points.
Table 3. Benchmarking LLM-Mixtral against Lexical and LERC metrics [12]
Narrative QA MCScript CosmosQA Quoref Average
BLEU 1 0.472 0.260 0.670 0.578 0.460
METEOR 0.615 0.502 0.711 0.716 0.611
ROUGE-L 0.495 0.297 0.701 0.604 0.490
BERTScore 0.534 0.194 0.779 0.286 0.447
BERT STS-B 0.686 0.449 0.789 0.750 0.638
LERC 0.738 0.694 0.824 0.741 0.744
LLM-Mixtral 0.884 0.795 0.824 0.735 0.822
3.3. Experiment III: comparing LLM-Mixtral with LERC on out-of-distribution datasets
Although LERC achieved good performance on some of the datasets, investigating the training and test
datasets used in LERC showed that the data is statistically biased, which provides doubt on the generalization
capabilities of LERC. As shown in Figure 2, the distribution of the training and test data has similar biases.
To verify that we applied LERC on totally unseen out-of-distribution data from dataset 1, namely Microsoft
machine reading comprehension (MSMARCO) and audio-visual scene understanding (AVSD). The correlation
results in Table 4 showed a lower performance as expected compared to LLM-Mixtral, which is one of the
critical advantages of using LLM-Mixtral based metric as it’s dataset and domain agnostic, and is not influenced
by a training distribution biases.
Figure 2. Biases in the training and test sets used in LERC
Table 4. Comparison of models LERC and LLM-Mixtral on MS-MARCO and AVSD datasets [17]
Model MS-MARCO AVSD
LLM-Mixtral 0.691 0.749
LERC 0.601 0.621

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3.4. Experiment IV: contrastive scoring task
The experiment was conducted on the contrastive pairs dataset [12]. This dataset assesses the prefer-
ence between two possible answers. The results are summarized in Table 5, with the accuracy of the results
reported. Lexical-based metrics performed poorly, as expected since the contrastive pairs were designed to have
similar token overlap with the reference. On the other hand, the sentence similarity model STS-B outperformed
others, likely because it generalizes beyond token overlap. The LERC model, presented in this research setting,
achieved the best results, with an average accuracy of 80%. Our proposed LLM-Mixtral metric, earned an
impressive average accuracy of 95%. This result supports our hypothesis that LLM-based models outperform
conventional and state-of-the-art models in this scoring task.
Table 5. Results of contrastive pairs experiment on datasets [12]
Metric NarrativeQA MCScript CosmosQA SocialIQA Avg.
BLEU-1 53 54 52 55 53.5
ROUGE-L 53 57 53 53 61.2
METEOR 60 62 57 53 54
BERTScore 70 58 74 62 66
BERT STS-B 70.6 70 59.3 66.6 66.6
LERC 80 87.3 72.6 81.3 80.3
LLM-Mixtral 96 94 96 94 95
3.5. Experiment V: open question answering datasets
The previous experiments were conducted using closed QA, where the answer text is provided within
a given context passage. In the current experiment, we aim to evaluate the metric on a more challenging task on
commonly used OpenQA datasets, namely natural questions (NQ), Trivia question answering (TQ), and event
and opinion understanding in natural language (EVOUNA) datasets. LLM-Mixtral outperformed BERTscore
applied on the same dataset as shown in Table 6, which summarizes the relative performance of LLM-Mixtral
over other state-of-the-art models. The best-performing neural-BERT model is chosen for each subset of the
ten datasets used in the experimentation. The incremental difference between the proposed LLM-Mixtral
model and the best-performing neural-BERT model ranges from 2.7 points to 8.4 points on the answer-pairs
scoring task and 14.7 points on the contrastive answer-pairs task.is selected on each subset of the ten datasets
used in the experimentation. The incremental difference between the proposed LLM-Mixtral model and the
best-performing neural-BERT model ranges from 2.7 points to 8.4 points on the answer-pairs scoring task and
14.7 points on the contrastive answer-pairs task.
Table 6. Comparative analysis of best performing neural BERT models with LLM-Mixtral
Datasets Model Avg. performance LLM-Mixtral Difference
MS-MARCO, AVSD, NarrativeQA, SemEval ROUGE-L-KPQA 73.15 75.87 2.72
CosmosQA, MCScript, NarrativeQA, Quoref LERC 74.44 82.2 7.76
NaturalQuestions BERTScore 80.84 88.2 7.36
TriviaQA BERTScore 85.28 93.68 8.4
Contrastive pairs Datasets (CosmosQA, MCScript, NarrativeQA, SocialiQA)
LERC 80.3 95 14.7
4. CONCLUSION
This study explored applying LLMs as an evaluation metric for QA tasks. Our inquiry has resulted
in a more profound comprehension of the capabilities of LLMs in assessing, adjudicating, and appraising
the performance of QA systems in both closed and open domains. We conducted extensive experiments on
ten datasets, comparing our proposed LLM-Mixtral metric with existing methods on QA tasks. The results
indicated the superiority of LLM-Mixtral in providing accurate evaluations of answer quality. It outperformed
traditional lexical metrics, neural BERT-based models, and KPQA approaches. Mixtral 8x7 B, a simple LLM-
based metric, showcased higher correlations with human judgments compared to more sophisticated state-of-
the-art statistical and neural machine-learning-based metrics. It reached an impressive Pearson correlation of
over 80%. Human judgments in evaluating answer pairs achieved accuracy rates exceeding 95% in contrastive
scoring. This superior performance across a diverse range of datasets and models underscores the potential of
LLMs in QA evaluation. Our adopted metric exhibited versatility in open-domain QA experiments, specifically

on NQ and TQ datasets. It achieved results closer to human judgments and outperformed over-relaxed lexical
matching metrics, bridging the gap between automated scoring and human assessment. The correlation with
human judgments on these datasets reinforced the effectiveness of LLM-Mixtral, positioning it on par with
GPT-3.5 and outperforming state-of-the-art neural BERT-based models like BERTScore. Our findings open
new horizons for applying LLMs in QA evaluation, offering a complementary approach to traditional and
neural-based metrics. This research marks a crucial step in pursuing more accurate and effective QA evaluation
methods. Some key benefits of using LLM-based metrics over state-of-the-art metrics include customizability,
multifaceted evaluation, and train-free capabilities. These features enable us to create a metric that can flexibly
perform the judgment task across various datasets without requiring a learning process while still achieving
competitive performance. LLM-based metrics are more domain agnostic than most machine learning BERT-
based techniques, which showed a distribution domain - bias when correlating with human judgments.
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BIOGRAPHIES OF AUTHORS
Hazem Abdelazim is currently a Professor of AI and ML and Dean at ESLSCA Univer-
sity’s School of Computing and Digital Technology. He has been locally and internationally recog-
nized for his achievements . He was awarded an ‘Invention Achievement Award’ from IBM in 1991,
the First Scientific Innovation Prize for Arab Scientists (1993), State Excellence and encouragement
Award (1995), and MBA Director’s Cup (2003) from MSM, Netherlands. His journey included aca-
demic positions at Cairo University, AUC, and UAE University, and professional positions as an IBM
Research Scientist, and Director of research at Microsoft. His research interests are generative arti-
ficial intelligence (AI), LLM, information retrieval, and NLP. He has 35+ publications. He can be
contacted at email: hazem.abdelazim@eslsca.edu.eg.
Mohamed Tharwat Waheed graduated from the Department of Electronics and Commu-
nication, Faculty of Engineering, Cairo University in 2006. He received the M.Sc. degree in using
reinforcement learning in mobile communication in 2017. He completed his Ph.D. with a focus on the
applications of AI/ML in the Telecom industry at Cairo University. In addition to his industry role as
a Subject Matter Expert in the technology domain at Vodafone, Egypt. He is a research and teaching
doctor at ESLSCA University School of Computing and Digital Technologies. He is also an IEEE
Senior Member. His research interests span a diverse spectrum, including IoT in smart cities, 5G,
autonomous driving, AI/ML in mobile communication, and the implementation of generative AI in
domain-specific tasks. He can be contacted at email: mohamed.mohamed-waheed@vodafone.com.
Ammar Mohammad earned his bachelor’s and master’s degrees in computer science
from Cairo University, Egypt, and obtained his Ph.D. in computer science from the University of
Koblenz-Landau, Germany, in 2010. He has previously served as a researcher and research fellow
with the AI Research Group at the University of Koblenz-Landau. Currently, he holds the position of
a professor of computer science at both Cairo University and MSA University in Egypt. His research
interests encompass machine and deep learning techniques, methods, algorithms, and applications
across various domains. He can be contacted at email: ammar@cu.edu.eg.

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