U ni B uc at S em E val-2024 Task 2: Tailored Prompting with Solar for Clinical NLI (2024)

Abstract: This paper describes the approach of the UniBuc team in tackling the SemEval 2024 Task 2: Safe Biomedical Natural Language Inference for Clinical Trials. We used SOLAR Instruct, without any fine-tuning, while focusing on input manipulation and tailored prompting. By customizing prompts for individual CTR sections, in both zero-shot and few-shots settings, we managed to achieve a consistency score of 0.72, ranking 14th in the leaderboard. Our thorough error analysis revealed that our model has a tendency to take shortcuts and rely on simple heuristics, especially when dealing with semantic-preserving changes.

## introduction clinical trials are prospective studies that aim to compare the effectiveness of an intervention against a control group in clinical patients @xcite . clinicaltrials.gov 1 hosts more than 480,000 clinical trials, making it challenging to analyze and extract information from them manually. natural language inference has emerged as a valuable tool for interpreting evidence from clinical trials @xcite . the second task of semeval 2024 focuses on improving the understanding of clinical trial data through the second edition of nli4ct (natural language inference for clinical trials) @xcite . this challenge is specifically designed to test the natural language inference capabilities of large language models (llms) and their ability to understand clinical text. the data used for the challenge was carefully annotated by clinical domain experts, and semantic interventions were performed to evaluate the safety and robustness of the models. ## related work recent work on clinical trial analysis includes detecting contradictions in medical publications @xcite , automating eligibility assessment with llms @xcite , and assessing model hallucinations and reasoning capabilities in healthcare settings @xcite . the previous semeval nli4ct task @xcite included a similar entailment task, with most submissions leveraging pre-trained language models. a small minority of approaches used ontologies and rule-based algorithms. few of the language model-based approaches include preprocessing of the data prior to using it as input to the models. in our approach for the current task, we also attempt solutions based on both discriminative and generative language models, and in addition perform preprocessing of the input clinical trial data before feeding it to the models, such as summarization. while most of the best performing systems in last year's task employ some in-domain pre-training on medical data, we find that, from the models we experimented with, general llms perform as well as or better than medical ones. this could be explained by their larger size and instruction tuning techniques, but also because of the recent advances in general llms. however, we do not perform an exhaustive comparison of the two kinds of models (we use few medical llms in our experiments), so a definitive conclusion can not be drawn on this comparison. the nli4ct dataset @xcite is a unique benchmark dataset for natural language inference (nli) in the clinical domain that contains data from clinical trial reports (ctrs). in contrast, the mednli @xcite dataset is another benchmark dataset for nli in the clinical domain, but it only contains clinical notes. to ensure that nli models are robust and safe, the organizers of the nli4ct task perform semantic-preserving and semantic-altering interventions of the hypotheses. according to @xcite , nli models for clinical trials require not only biomedical reasoning but also numerical reasoning capabilities, as ctrs contain a large amount of quantitative information. in this regard, we conduct experiments using solar 10b @xcite , which was trained on question-answer pairs from the mathematical domain to enhance its mathematical capabilities. ## data and task description the data used for this task is comprised of 1,000 breast cancer ctrs collected from clinicaltrials.gov with 24,000 entailment relations annotated by clinical domain experts @xcite . each ctr is comprised of 4 sections: eligibility criteria, intervention, results and adverse events. each sample from the nli4ct dataset consists of the ctr premise (one of the 4 sections of the ctr), a statement, and an entailment label (entailment or contradiction). the premise can refer to only one ctr in the single type instance, or to a primary and a secondary trial in the comparison type. the purpose of the current task is to evaluate the consistency of models and their ability to per-form faithful reasoning @xcite . for this purpose, different semantic altering (contradiction rephrasing and numerical contradiction) or semantic preserving interventions (paraphrase, numerical paraphrase and definition) have been conducted on the evaluation data. the nli4ct dataset consists of 1,700 entailment relations in the training split, 200 in dev, and 5,500 in the test split. to assess the performance of the models in the shared task, two metrics have been proposed: faithfulness and consistency @xcite , besides f1-score. faithfulness measures the model's ability to adjust its predictions accurately after a semantic-altering intervention. consistency measures the capacity of the system to predict the same label for both the original and contrast statements, in the case of semantic-preserving interventions. ## methodology in this section, we present the different approaches used to predict the entailment relations. ## error analysis and discussion in this section, we analyze the types of errors made by the solar model according to the provided metrics based on each intervention target. ## conclusions and future work in this paper, we presented our approach for the semeval 2024 task 2 @xcite aimed at understanding large language models behavior in clinical contexts. we explored several types of models and prompting techniques in order to determine whether fine-tuning is more feasible than zero-shot or few-shot prompting in a limited resource setting. our findings suggest that, while llms exhibit remarkable clinical nli capabilities at a surface level, the proposed metrics and interventions uncover a tendency of the models to take shortcuts and rely on simple heuristics, especially when faced with semantic-preserving changes. we intend to investigate further methods of evaluating the reliability of large language models in future work. to address the inherent weak numerical reasoning of our model (and all generative models), a promising strategy is to offload complex mathematical hypotheses to a specialized model like xval @xcite . this approach involves representing numbers as individual digits (e.g., 123 becomes ["1", "2", "3"]), replacing them with a generic [num] token, and scaling the token according to the original numerical value. their results showed a 70-fold improvement over standard models. we can extend this strategy to create an ensemble where other weaknesses of our model (like rephrases) are offloaded to specialized models.