Noot Noot at S em E val-2024 Task 7: Numerical Reasoning and Headline Generation (2024)

Abstract: Natural language processing (NLP) modelshave achieved remarkable progress in recentyears, particularly in tasks related to semanticanalysis. However, many existing benchmarksprimarily focus on lexical and syntactic un-derstanding, often overlooking the importanceof numerical reasoning abilities. In this pa-per, we argue for the necessity of incorporatingnumeral-awareness into NLP evaluations andpropose two distinct tasks to assess this capabil-ity: Numerical Reasoning and Headline Gener-ation. We present datasets curated for each taskand evaluate various approaches using both au-tomatic and human evaluation metrics. Ourresults demonstrate the diverse strategies em-ployed by participating teams and highlight thepromising performance of emerging modelslike Mixtral 8x7b instruct. We discuss the im-plications of our findings and suggest avenuesfor future research in advancing numeral-awarelanguage understanding and generation.

## introduction natural language processing (nlp) models have achieved impressive performance on a wide range of semantic analysis tasks in recent years. however, the majority of benchmarks used to evaluate these models, including past semeval shared tasks, have focused predominantly on lexical and syntactic understanding, with little emphasis on numerical reasoning abilities. in this paper, we argue that comprehending and reasoning with numerical values expressed in text is vital for robust language understanding, and should be an integral part of nlp evaluations going forward @xcite . we demonstrate across several application scenarios that a lack of numeracy can undermine model performance and result in erroneous output. as an illustration, fine-grained sentiment analysis, as explored in semeval-2017 task 5, relies heavily on distinguishing subtle differences in sentiment intensity. anticipating a 30% stock price increase implies a markedly more positive outlook than a 3% rise. without accounting for the differential impact of these numbers, sentiment analysis models may fail to capture such nuances. similarly, in legal judgment prediction settings like semeval-2023 task 6, sentencing decisions can hinge on numerical quantities -stealing @xmath010. clinical inference use cases such as semeval-2023 task 7 also require sensitivity to numbers like blood pressure readings, where contrasts between 121 mmhg and 119 mmhg could indicate notably different health outlooks @xcite . these examples highlight the limitations of current benchmarking paradigms in evaluating true language comprehension. accordingly, we propose that new numerically-grounded nlp tasks be developed to test numerical reasoning capacities. recent work has begun exploring this direction, but substantial efforts are still needed to build robust models that demonstrate human-like numeracy. we outline a potential experimental framework and novel dataset for this purpose in the following sections. ## dataset task 3 @xcite of our study comprises two distinct subtasks: numerical reasoning and headline generation. we describe the dataset for each subtask separately below: ## methods zero-shot prompting is an effective method for these news headline tasks because it allows the model to apply its generalized language understanding capabilities to novel tasks without extensive fine-tuning. the model can deduce the appropriate responses based solely on the instructions and examples provided in the prompt. the prompts are carefully engineered to provide the model with clear guidelines and context. for the numerical reasoning task, the prompt poses the incomplete headline as a question and asks the model to fill in the blank with only a numerical value. this focuses the model on extracting and inferring the relevant number from the article text. similarly, the headline generation prompt provides the news article as context and directly instructs the model to generate a headline summarizing the key information. the simplicity of these prompts allows the model to use its innate language skills to produce fitting responses without needing gradient-based training on the specific tasks. furthermore, the varied topics and contexts in the dataset require the model to adapt its numerical and summarization strategies across different situations. this tests the model's ability to generalize based on the prompt instructions, rather than overfitting to biases in a narrow dataset. the broad applicability demonstrated through zero-shot prompting highlights the versatile reasoning capacity gained through the model's pretraining. overall, zero-shot prompting is an elegant and effective approach for this study, as it allows assessment of the model's intrinsic skills at numerical deduction and text summarization when provided suitable prompts. the prompt formulation is key to eliciting successful performance without taskspecific fine-tuning. ## conclusion in this study, we explored the performance of various approaches for two distinct tasks: numerical reasoning and headline generation. across both tasks, we observed a range of performances among the participating teams, indicating the diversity of techniques and strategies employed. for numerical reasoning, the top-performing teams demonstrated high accuracy and effective reasoning capabilities, leveraging a combination of techniques to achieve superior results. notably, the utilization of advanced models and fine-tuning methodologies played a crucial role in enhancing performance on this task. in headline generation, both auto and human evaluations highlighted the effectiveness of certain teams in generating accurate and engaging headlines. teams employing sophisticated natural language processing techniques, such as advanced neural models and feature engineering, exhibited superior performance in generating headlines that resonated well with both automated evaluation metrics and human judges. furthermore, the introduction of mixtral 8x7b instruct model showcased promising capabilities in both tasks. despite not being fine-tuned specifically for the tasks at hand, the mixtral model demonstrated competitive performance, particularly in headline generation. this suggests the robustness and versatility of the mixtral 8x7b instruct model in understanding and generating natural language content across diverse domains and tasks. overall, our findings underscore the importance of leveraging state-of-the-art models and techniques in tackling complex natural language processing tasks. additionally, the emergence of pretrained models like mixtral 8x7b instruct offers promising avenues for future research and development, as they provide strong baselines and require minimal fine-tuning to achieve competitive performance across various nlp tasks.