Using Large Language Models to Evaluate Biomedical Query-Focused Summarisation (2024)

Abstract: Biomedical question-answering systems remain popular for biomedical experts interacting with the literature to answer their medical questions. However, these systems are difficult to evaluate in the absence of costly human experts. Therefore, automatic evaluation metrics are often used in this space. Traditional automatic metrics such as ROUGE or BLEU, which rely on token overlap, have shown a low correlation with humans. We present a study that uses large language models (LLMs) to automatically evaluate systems from an international challenge on biomedical semantic indexing and question answering, called BioASQ. We measure the agreement of LLM-produced scores against human judgements. We show that LLMs correlate similarly to lexical methods when using basic prompting techniques. However, by aggregating evaluators with LLMs or by fine-tuning, we find that our methods outperform the baselines by a large margin, achieving a Spearman correlation of 0.501 and 0.511, respectively.

text: Proceedings of the 23rd Workshop on Biomedical Language Processing, pages 236–242 August 16, 2024. ©2024 Association for Computational Linguistics
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heading: 1 Introduction
text: Biomedical question answering (QA) is concerned with building systems that automatically answer biomedical questions posed by humans in natural language (Soares and Parreiras, 2018; Nguyen, 2019). To develop and optimise these systems, we must use metrics that evaluate the quality of their output. Automatic metrics such as ROUGE (Lin, 2004) and BLEU (Papineni et al., 2002) have been shown to correlate poorly with human evaluation (Liu et al., 2016), and human annotations are prohibitively expensive and impractical in the biomedical domain (Pampari et al., 2018; Guo et al., 2006). To rectify this problem, recent research has suggested using medium-sized model-based evaluators and Large Language Models (LLMs). Model-based evaluators such as BERTscore (Zhang et al., 2020) or BLEURT (Sellam et al., 2020) have demonstrated improvements over n-gram based
metrics in various natural language generation (NLG) evaluation contexts such as summarisation and QA (Zhong et al., 2022). However, their evaluation capability is still far below that of humans.
The recent improvement of LLMs for various tasks has fostered research on their use for the evaluation of the performance of text generation tasks such as summarisation and dialogue generation (Liu et al., 2023). In this paper, we experiment with using LLMs to evaluate biomedical query-focused summarisation systems. To the best of our knowledge, this is the first study for such a task. In particular, we compare the correlation between human judgements and LLM-based evaluators for the evaluation of several systems participating in the “ideal answer” task of BioASQ 2021 and 2022 (Nentidis et al., 2022). Our study examines different prompting strategies for such evaluations.

heading: 2 Related Work
text: Fu et al. (2024) indicates that the use of LLMs as reference-free probability-based evaluators yields performance superior to n-gram metrics and modelbased evaluators such as BERTscore, all the while providing a customised and multi-faceted evaluation with no training cost. Probability-based LLM evaluation, however, suffers from issues of robustness which lead to biases and loopholes (He et al., 2023) that impact its efficacy.
LLMs, through prompting, have also been used to evaluate text via Likert scale scoring (a fivelevel scale) (Likert, 1932), leading to greater performance than n-gram and model-based evaluation techniques. However, Chiang and Lee (2023) showed that outputting only a number could be suboptimal but that asking the LLM to explain its rating can lead to an increase in correlation to human ratings. The pairwise ranking also showed promising results (Kotonya et al., 2023), with accuracy outperforming n-gram and model-based evaluators.
LLMs can also leverage emergent abilities which can be used to incorporate in-context learning (ICL) and chain-of-thought (CoT) in their evaluation strategies. In ICL (Xie et al., 2022), a model is provided with input-output examples of a downstream task instead of being trained or fine-tuned on the task. In CoT (Kojima et al., 2022), a complex task is broken down into multiple intermediate steps to improve reasoning in LLMs.
Liu et al. (2023) used GPT-4 to achieve the highest correlation with human evaluations in comparison to other model and n-gram based metrics. Jain et al. (2023), using GPT-3, showed that using few-shot prompting — a small number of examples added to the prompt — can reach or exceed the state-of-the-art on multi-dimensional evaluation and that this is robust to the sampling method of in-context examples whether it be random or representative of the range of scores in the example pool. However, Kotonya et al. (2023) showed using a small LLM (orca-mini-v3-7B), that oneshot prompting doesn’t bring significantly greater results than zero-shot.
The use of LLMs as meta-evaluators who use their reasoning capabilities to combine diverse evaluation techniques has also seen promise. In Shu et al. (2023), various LLMs were given supplementary evaluation metrics like NLI Score (Bowman et al., 2015), BLEURT and probability-based LLM techniques to aid with their judgements, with the meta-evaluation outperforming all individual evaluators.
The above research, however, was not used in query-focused summarisation tasks. This paper is the first that tests the use of LLMs for the evaluation of biomedical query-focused summarisation.

heading: 3 Methodology
text: We use the human judgements of runs participating in the “ideal answers” question answering task of BioASQ (Nentidis et al., 2022). Such “ideal answers” are multi-sentence answers, and therefore the task is about biomedical query-focused summarisation. In particular, training and development is based on systems (runs) participating at BioASQ 20201, whereas testing is on runs participating at BioASQ 20212 and BioASQ 20223. The rationale for using BioASQ 2020 for training is that it is the
1Run MQ1 in each batch submitted by Mollá et al. (2020). 2Run MQ1 in each batch submitted by Khanna and Mollá
(2021). 3Run MQ1 in each batch submitted by Mollá (2022).
most recent year prior to the test data. Resource constraints do not allow us to use all runs participating at BioASQ 2020 for training, or all runs participating at BioASQ 2021 and 2022 for testing.
The human judges are given instructions to evaluate the pair (question, generated answer), given a correct answer, according to four criteria presented in Figure 1. The final score of a (question, generated answer) pair is the average of the 4 criteria. These judgements are provided to us by the organisers of BioASQ. To preserve privacy, we only had access to the judgement of runs submitted by us.
For each automatic evaluation technique, the Spearman correlation (Spearman, 1904) is calculated. In addition, since the LLM-based evaluation generated integer numbers 1 to 5, for each LLM-based evaluation technique, the quadratic kappa, a well-established correlation metric for nominal scales (Cohen, 1968), was also calculated4. BioASQ runs five to six evaluation batches each year. Since there is no guarantee that the same runs participated in all batches, the correlations are computed separately for every batch, and the results reported in this paper are the average correlation. Each batch has approximately 100 (question, generated answer, known answer) triples.
Given that the automatic evaluation by LLMs can vary each time the LLM is run, each batch is evaluated three times and then the evaluation re-
4Quadratic kappa could not be used on the output of the other evaluation techniques we tested because they generate real numbers between 0 and 1.
sults are averaged before computing the correlation with the human judges.
The LLM-based evaluations return independent evaluation scores for each evaluation criterion (Figure 1). Our correlation experiments consequently measure the correlation of each criterion (and average) per the corresponding human criterion (and average). For example, the Precision column of Table 1 shows the correlation of the Precision scores generated by each LLM evaluator for the Precision scores of the human judges.

heading: 4 Experiments
text: We investigated several evaluation strategies, from baseline well-known token-based metrics to the use of LLMs as evaluators in different settings.

heading: 4.1 Token-based Techniques and their Limitations
text: ROUGE-1 and ROUGE-2 (F1), as well as SciBERTscore (Precision, Recall, F1), were tested to attain a baseline correlation level. ROUGE F1 was chosen given its robustness over precision and recall (Mollá and Jones, 2020). Sci-BERT (Beltagy et al., 2019) is a BERT (Devlin et al., 2019) model pretrained on papers from Semantic Scholar, of which 82% are from the biomedical domain. SciBERT was chosen over BERT due to its greater understanding of biomedical terminology.

heading: 4.2 LLMs
text: GPT-3.5 (gpt-3.5-turbo-0125) and GPT-4 (gpt-4-1106-preview) were used as evaluators. All prompts included the question, reference answer(s), a system output, the defined evaluation criteria, and instructions rating responses on a 1-5 integer scale. For all runs, the system prompt was set to “You are a useful evaluator of a biomedical question answering system”, and the temperature and top p were set to 0 and 0.6, respectively, to facilitate reproducibility. Out-of-the-box (OTB) GPTs were tested with the base prompt listed in Figure 2.

heading: 4.2.1 Reason then Score
text: Chain-of-thought (CoT) prompting has demonstrated an increase in performance in various tasks, such as arithmetic and commonsense reasoning (Kojima et al., 2022). We used the variant of CoT called “Reason then Score” (RTS), in which an LLM is asked to explain its reasoning. RTS has emerged as a popular prompting technique (Shen
et al., 2023). In our experiments, we altered the answer reporting format to include an explanation area that instructs the LLM to explain its answers (Figure 3).

heading: 4.2.2 Few Shot
text: LLMs are reported to display an increase in performance when the prompt includes a few examples with their input query (Brown et al., 2020). We provided the LLMs with six examples from BioASQ 2020. Our initial experiments showed that random sampling of examples yielded poor performance. We instead used a percentile-based selection strategy to ensure a wide coverage of the example pool, based on the scores given by the human judges. In
particular, examples with 15th and 80th-percentile scores for recall, precision and readability were included in the prompt.

heading: 4.2.3 Fine-tuning
text: We also experimented with fine-tuned LLMs. In particular, we fine-tuned GPT-3.5 (gpt-3.5-turbo) using the same prompting format as OTB.

heading: 4.2.4 LLMs as Meta Evaluators
text: Inspired by work by Shu et al. (2023), we experimented with the use of LLMs as meta-evaluators of 3 different evaluators. In particular:
1. To aid the LLM in scoring repetition, we provided it with a “Repscore” which took the number of unique words in a response and divided it by the total number of words.
2. Smog score (Laughlin, 1969), which looks at the number of polysyllabic words and sentences in a text, was used to aid the scoring of readability.
3. Finally, the output from the fine-tuned GPT3.5 model was also included to aid with the scoring of all dimensions.
Testing was done primarily on GPT-4, since GPT-3.5 showed a very limited capability in reasoning with other scores.

heading: 4.2.5 Pairwise Ranking
text: LLMs have also shown potential in comparative assessment (Liusie et al., 2024). We conducted pairwise ranking, where the LLM evaluator (gpt41106-preview) was given the output of two systems,
the question, and was asked to rank them. We gave the LLM a CoT of the form Score then Reason as shown in Figure 4. We used accuracy and Cohen’s kappa5 to evaluate the performance.

heading: 5 Results and Discussion
text: Table 1 shows the results of all experiments except pairwise ranking, and Table 2 shows the results of the experiments with pairwise ranking.
Similar to previous work, we find that tokenbased methods perform worse than LLMs in general (Liu et al., 2023). Possible causes of the relatively poor performance of token-based approaches is, as mentioned by Hanna and Bojar (2021), that ROUGE is unable to incorporate information on context and semantic meaning, whereas Sci-BERTscore is less sensitive to errors in text, especially if the candidate is lexically or stylistically similar, and both are insensitive to negation. These limitations have increasingly larger impacts on abstractive over extractive systems, making evaluating outputs using these metrics potentially unreliable. Still, Table 1 shows that the token-based methods achieve a correlation with the human judgements of precision and readability that is comparable to that of some of the LLM approaches.
5This is the standard Cohen’s kappa, not quadratic kappa, since now the score is not a nominal scale.
GPT-3.5 with basic prompting displays similar performance to token-based metrics when using prompt engineering techniques such as CoT and few shot. When fine-tuned, GPT-3.5 attains a much higher correlation with humans than the tokenbased metrics. GPT-4 as a meta-evaluator achieves very similar results to the fine-tuned model. More testing is needed to be done on GPT-4 to see if prompt engineering leads to even better results.
Few-shot prompting performed the worst out of the LLM-based methods. In our preliminary experiments, we observed that the performance of the few-shot approach varied, with some batches increasing their correlations and others decreasing. This was in contrast with the performance of the fine-tuned approach, which had a lower variation across batches. This suggests that the examples chosen for few-shot could be more suited to certain batches. A promising future direction is to incorporate a dynamic selection of examples.

heading: 6 Conclusions
text: We compared the use of traditional evaluation metrics (ROUGE, BERTScore) with the use of LLMs for the evaluation of query-focused summarisation of biomedical questions. For this, we used system runs that participated in BioASQ challenge, and computed correlation between automatic evalua-
tions and human judgements. Our experiments show that, while LLMs with basic prompting do not outperform ROUGE or BERTScore, approaches that use fine-tuning or that combine LLMs with additional scorers, significantly improve correlation with human judgements.

heading: 7 Limitations
text: Due to limitations of resources and the availability of few runs, we have not experimented with a wide range of outputs of differing characteristics. Our training and test data used runs that were performed in the middle to the top range of systems participating in BioASQ. Therefore, the quality of the evaluators has not been tested on poor-quality runs. As a consequence, even though the results presented here should be valid to evaluate medium to highquality systems, we cannot guarantee that the quality of the evaluations applies to poor-performing systems.

heading: 8 Ethical Considerations
text: The human judgements were obtained from the organisers of BioASQ. To ensure the privacy of these judgements, we only had access to judgements of past runs submitted by the authors of this paper, and the review judgements were anonymous.
Even though our results show a better correlation with human judgements than other automatic evaluation metrics, there is still room for improvement, and the evaluation results might not be reliable enough for applications requiring high-quality output systems and high-quality evaluation.

heading: Future Work
text: A promising future direction is to incorporate a dynamic selection of examples