S em E val-2024 Task 2: Safe Biomedical Natural Language Inference for Clinical Trials (2024)

Abstract: Large Language Models (LLMs) are at the forefront of NLP achievements but fall short in dealing with shortcut learning, factual inconsistency, and vulnerability to adversarial inputs. These shortcomings are especially critical in medical contexts, where they can misrepresent actual model capabilities. Addressing this, we present SemEval-2024 Task 2: Safe Biomedical Natural Language Inference for Clinical Trials. Our contributions include the refined NLI4CT-P dataset (i.e. Natural Language Inference for Clinical Trials - Perturbed), designed to challenge LLMs with interventional and causal reasoning tasks, along with a comprehensive evaluation of methods and results for participant submissions. A total of 106 participants registered for the task contributing to over 1200 individual submissions and 25 system overview papers. This initiative aims to advance the robustness and applicability of NLI models in healthcare, ensuring safer and more dependable AI assistance in clinical decision-making. We anticipate that the dataset, models, and outcomes of this task can support future research in the field of biomedical NLI. The dataset, competition leaderboard, and website are publicly available.

## introduction large language models (llms) excel in numerous natural language processing (nlp) tasks, as evidenced by their state-of-the-art achievements @xcite . despite these advancements, llms are prone to several critical vulnerabilities. these include a tendency towards shortcut learning, which may compromise their learning process and accuracy @xcite @xcite figure 1 : the goal of nli4ct is to predict the relationship of entailment between a statement and a ctr premise @xcite . in this task, we introduce a set of perturbations (nli4ct-p) applied to the statements to test the semantic consistency and faithfulness of nli models. 2018). additionally, they exhibit factual inconsistencies @xcite and are sensitive to changes in word distributions @xcite , data transformations @xcite @xcite , and adversarial attacks @xcite . these issues are particularly concerning as they may lead to an overestimation of llms' capabilities in practical applications, a risk that is notably significant in fields requiring high reliability, such as healthcare @xcite . clinical trials play a pivotal role in evaluating the efficacy and safety of novel treatments, thereby significantly contributing to the progress of experimental medicine @xcite . clinical trial reports (ctrs) document the methodologies and outcomes of these trials, serving as a foundation for healthcare professionals to devise and administer experimental therapies. however, the sheer volume of ctrs, exceeding 400,000 and continually growing @xcite , renders it impractical for a manual comprehensive analysis of all pertinent literature in treatment planning @xcite . in this context, natural language inference (nli) @xcite emerges as a viable solution, facilitating the large-scale interpretation and synthesis of medical evidence. this approach effectively bridges the latest research findings with clinical practice, thereby supporting the delivery of personalized care @xcite . previously, we created the multi-evidence natural language inference for clinical trial reports (nli4ct) dataset, detailed in @xcite . this dataset, enriched with clinical trial reports (ctrs) and expert-annotated statements for entailment and contradiction, exemplified in figure 1 , served as the foundation for organizing "semeval-2023 task 7: multi-evidence natural language inference for clinical trial data". while the preceding version of nli4ct spurred the creation of models based on large language models (llms) @xcite @xcite that demonstrated commendable performance (i.e., f1 score ≈ 85%), deploying llms in sensitive areas like real-world clinical trials mandates additional scrutiny. this necessitates the invention of new evaluation frameworks that allow thorough behavioural and causal analysis @xcite . in pursuit of these goals, we present the latest iteration of our dataset, nli4ct-p, an extension of the original nli4ct with data perturbations. moreover, we provide a comprehensive analysis of the systems that participated in "semeval-2024 task 2: safe biomedical natural language inference for clinical trials" a task conducted using the nli4ct-p dataset. this initiative aims to improve our understanding of llms behaviour and advance evaluation methodologies for clinical natural language inference (nli). the task is structured around the systematic application of controlled interventions, each designed to investigate specific semantic and numerical inference challenges typical of clinical nli (see our efforts aim to significantly contribute to the crafting of more dependable and insightful evalu-original statement: the primary trial intervention protocol lasts a total of 14 days. ## dataset the premises used in the nli4ct dataset (jullien et al., 2023a) are derived from 1,000 publicly accessible, english-language breast cancer clinical trial reports (ctrs) published on clinicaltrials.gov a resource managed by the u.s. national library of medicine. this dataset complies with the health insurance portability and accountability act (hipaa) privacy rule. the original nli4ct collection includes 2,400 expert-annotated statements, premises and associated labels. these are distributed across training, testing, and development sets in a 70/20/10 ratio. we have advanced the methodology of the previous nli4ct dataset by incorporating interventions to create a contrast set, enabling a systematic behavioural and causal analysis of models evaluated in the competition. this enhanced version is referred to as nli4ct-p (perturbed). the construction of the contrast set involves four semiautomated, controlled interventions applied to the statements from the nli4ct test and development set. it's important to note that the specifics of these interventions were kept undisclosed until the completion of the competition's testing phase on january 31st 2024. ## evaluation semeval-2024 task 2 is devised as a binary classification challenge, with the macro f1-score being utilized to gauge the foundational performance of the participating systems. this evaluation is conducted on the original nli4ct test set, serving as a control metric, rather than on the nli4ct-p test set, which contains exclusively perturbed statements. although the macro f1 score is instrumental in measuring overall model performance by highlighting precision and recall across various classes, it inherently lacks the capability to fully capture the sophisticated understanding and reasoning skills essential for effective natural language inference (nli). specifically, the f1 score does not assess a model's capacity to adjust to subtle semantic shifts or evaluate the resilience of its predictions when faced with interventions that either modify or maintain the semantic integrity of statements. this gap highlights the necessity for more advanced metrics capable of offering deeper insights into a model's interpretative and reasoning proficiency. in response to this need and inspired by recent advancements in causal analysis within the nlp domain @xcite , we introduce two novel evaluation metrics aimed at examining the causal effects of interventions on model performance. faithfulness gauges the degree to which a system's predictions are both accurate and grounded in the correct rationale. intuitively, this is estimated by measuring the ability of a model to correctly adjust its predictions when exposed to interventions that modify the meaning (semantic altering) of the statement. specifically, for a set of n statements x i in the contrast set (c), alongside their corresponding original statements y i and the model predictions denoted as f (), faithfulness is quantified using the consistency assesses a system's capability to generate identical outcomes for semantically equivalent inputs. this measure evaluates whether a system can uniformly predict the same label for both the original and contrast statements under interventions that do not alter the semantic content (semantic preserving) of the statements. the key aspect here is the uniformity in representing semantic concepts across different statements, irrespective of the correctness of the final prediction. for n statements x i in the contrast set (c), alongside their original counterparts y i , and model predictions f (), consistency is determined as follows: the macro f1 score provides a foundational benchmark for basic model performance, serving as a control metric, the core objective of task 2 is towards enhancing model quality and dependability through systematic causal analysis. the pursuit here is not only for high performance in a traditional sense but for models that demonstrate a more reliable and robust application of natural language, reflecting a more nuanced approach to evaluating system capabilities, and allowing for developing safer, ethical, and trustworthy clinical systems. ## results and discussion in system overview papers and for which official leaderboard results have been provided. generally, participants tend to submit the highest-scoring results to the leaderboard, regardless of whether the system achieving these results represents the primary contribution of their paper. this approach ensures that our report reflects the peak performance levels achieved, albeit potentially overlooking the main systems of interest described in the papers. ## related work the landscape of expert-annotated resources for clinical nlp is rich, with notable examples such as the trec 2021 clinical track @xcite , which focuses on information retrieval from ctr data, highlighting eligibility criteria. evidence inference 2.0 @xcite introduces a qa task alongside span selection based on ctr results, while the mednli dataset @xcite ) offers an entailment task using patient medical history notes. these datasets primarily aim to evaluate biomedical language understanding and reasoning. despite neural architectures leading in biomedical nli performance @xcite , challenges remain in quantitative reasoning and numerical operations within nli @xcite . prior works experiment with biomedical pre-training strategies @xcite @xcite , and while exact @xcite automates information extraction from clinical trials, the integration of biomedical and numerical nli effectively remains unaddressed. none of the aforementioned resources provide avenues for meaningful causal analysis, a gap nli4ct-p aims to fill, through the application of targeted interventions and the introduction of novel evaluation metrics. ## conclusion this study introduces the nli4ct-p dataset and provides a comprehensive analysis of submissions to semeval-2024 task 2, underscoring the persistent challenges in clinical natural language inference (nli) despite significant advancements in large language models (llms). the incorporation of faithfulness and consistency metrics further highlights these challenges, shedding light on areas requiring additional focus, if these systems are to meet the requirements for real-world clinical implementation. our key findings reveal that generative models markedly outperform discriminative models, particularly in terms of faithfulness and consistency. the utility of additional data is underscored, especially due to the limited size of the nli4ct-p training set. furthermore, our analysis reveals the substantial impact of prompting strategies on model performance, noting an intriguing preference for zero-shot approaches over few-shot methods. additionally, mid-sized architectures, ranging between 7b and 70b parameters, demonstrate the potential to match or even exceed the performance of larger models (>70b) in f1 scores, faithfulness, and consistency, while being more resource and cost-effective. conversely, models with fewer than 7b parameters face difficulties in achieving comparable results. we plan to perform a further analysis of the submitted systems' performance at an intervention level, identifying specific areas of weakness, such as numerical reasoning or handling longer premises, to refine and enhance clinical nli systems further.