DFKI - NLP at S em E val-2024 Task 2: Towards Robust LLM s Using Data Perturbations and M in M ax Training (2024)

Abstract: The NLI4CT task at SemEval-2024 emphasizes the development of robust models for Natural Language Inference on Clinical Trial Reports (CTRs) using large language models (LLMs). This edition introduces interventions specifically targeting the numerical, vocabulary, and semantic aspects of CTRs. Our proposed system harnesses the capabilities of the state-of-the-art Mistral model (Jiang et al., 2023), complemented by an auxiliary model, to focus on the intricate input space of the NLI4CT dataset. Through the incorporation of numerical and acronym-based perturbations to the data, we train a robust system capable of handling both semantic-altering and numerical contradiction interventions. Our analysis on the dataset sheds light on the challenging sections of the CTRs for reasoning.

## introduction over the last decade, natural language processing (nlp) has seen significant advancements, beginning with the introduction of word embeddings @xcite , followed by transformer architectures like bert @xcite , and specialized language models (lms) such as biobert @xcite and pubmedbert @xcite tailored for the biomedical domain. the advent of large language models (llms) like gpt-3 @xcite , commonly known as chat-gpt, has further pushed the boundaries of nlp, showcasing capabilities in diverse nlp tasks and even reasoning. however, llms adapt to shortcut learning easily instead of understanding the task at hand and resorting to shallow lexical heuristics for making a prediction @xcite @xcite . additionally, we have seen generative models like chat-gpt hallucinating, making false claims, and struggling with providing factual information @xcite . tackling these challenges is essential for ensuring the reliable deployment of large language models, particularly in critical fields like biomedicine, where the margin for error must be minimized. the semeval-2024 task 2: safe biomedical natural language inference for clinical trials is focused on improving the understanding and evaluation methodologies for large language models in clinical natural language inference (nli) @xcite . this task targets aspects such as numerical and quantitative reasoning, domainspecific terminology, syntax, and semantics. it aims to analyze models' robustness, consistency, and faithfulness in reasoning within the clinical domain. our approach to this task involved leveraging instruction fine-tuned llms along with an auxiliary model that focuses on "hard" instances to develop a more resilient nli system. "hard" instances refer to those examples in the dataset where the model fails. building on the methodology outlined by @xcite , we assessed the zero-shot performance of various instruction-tuned llms to identify the most effective model. upon selecting the best llm, we introduced an auxiliary module during the fine-tuning process, which emphasized learning "hard" examples. taking inspiration from @xcite , who experimented with various configurations for the auxiliary module and highlighted its substantial impact on the final nli system's performance, we explored various architectures for this auxiliary module. to improve the robustness of the system and address challenges related to numerical reasoning and domain-specific terminology, we introduced numerical and semantic perturbation to the nli4ct dataset and trained our system on these. our system ranked 11th in macro f 1 score, 12th in faithfulness, and 19th consistency out of 31 participants. our final system struggled when dealing with semantic-preserving interventions on the test data yet demonstrated strong performance on semantic-altering interventions. ## background we now provide a description of the shared task, followed by a brief overview of the nli4ct dataset. we then explore existing research, assessing their strengths and limitations while also drawing connections to our proposed method. ## system overview in light of recent advancements in large language models and drawing insights from the results of the semeval 2023 task 7 @xcite , we implemented the approach outlined in the work of @xcite . our approach involved evaluating stateof-the-art llms, including @xcite , llama @xcite , and lemma @xcite , alongside their variants. we experimented with different instructions for each model and subsequently compared their zeroshot performance based on their respective bestperforming instruction (see final instruction template in appendix a.3). mistral emerged as the top-performing model among all others evaluated with the highest f 1 score (0.69). furthermore, we implemented the minmax algorithm @xcite by adding an auxiliary model alongside the mistral model to create a more robust system. this auxiliary model is designed to amplify the loss incurred in input spaces where the mistral model encounters difficulties, effectively directing its focus towards areas of higher loss. to further boost the performance of the system, we pre-finetuned using mednli dataset. additionally, we conducted an error analysis to identify easy and difficult instances in the train set to provide a basis for further research. ## experimental setup training an llm can be both costly and resourceintensive. however, recent advancements in methodologies, such as parameter-efficient fine-tuning (peft), have emerged to reduce the computational cost of fine-tuning @xcite . for fine-tuning the mistral model, we employ a peft method known as low-rank adaption @xcite ). we adopted a similar approach for implementing the auxiliary model as described by @xcite . we experimented with the parameters of the system to obtain an optimal architecture, details of which can be found in appendix a.5.4. ## results during both the fine-tuning and evaluation phases, we observed improvements in the model trained with the minmax algorithm compared to other models. from ## error analysis to understand the model's behavior across different sections, interventions, and labels/relations, we examined the dataset. following the data cartography procedure outlined in @xcite , we replicated their method using the best minmax model trained for three epochs. we collected probability values for the gold label on each epoch and calculated confidence, variability, and correctness values. in figure 3 , the upper region with red data points represents easy-to-learn instances, while the bottom region with blue data points represents hard-tolearn examples. data points with high variability are depicted as ambiguous examples. ## conclusion in this study, we introduced a large language modelbased system designed to address the natural language inference task through text generation. our approach prioritized model robustness, which was achieved by incorporating an auxiliary model that directs the llm to focus on challenging instances in the input space. moreover, we enhanced the system's robustness against adversarial samples by introducing numerical and semantic perturbations to the nli4ct dataset during training. our findings revealed the system's superior robustness against semantic-altering interventions compared to semantic-preserving ones. additionally, through dataset analysis, we identified instances targeting the eligibility section in clinical trial reports as the the easiest to learn but more challenging to predict accurately. conversely, the adverse events section posed greater difficulty in learning but was relatively easier to predict accurately. these findings offer valuable insights for future research on improving the robustness by focusing more on challenging sections of ctrs.