A ssisted DS : Benchmarking How External Domain Knowledge Assists LLM s in Automated Data Science (2025)

Abstract: Large language models (LLMs) have advanced the automation of data science workflows. Yet it remains unclear whether they can critically leverage external domain knowledge as human data scientists do in practice. To answer this question, we introduce AssistedDS (Assisted Data Science), a benchmark designed to systematically evaluate how LLMs handle domain knowledge in tabular prediction tasks. AssistedDS features both synthetic datasets with explicitly known generative mechanisms and real-world Kaggle competitions, each accompanied by curated bundles of helpful and adversarial documents. These documents provide domain-specific insights into data cleaning, feature engineering, and model selection. We assess state-of-the-art LLMs on their ability to discern and apply beneficial versus harmful domain knowledge, evaluating submission validity, information recall, and predictive performance. Our results demonstrate three key findings: (1) LLMs frequently exhibit an uncritical adoption of provided information, significantly impairing their predictive performance when adversarial content is introduced, (2) helpful guidance is often insufficient to counteract the negative influence of adversarial information, and (3) in Kaggle datasets, LLMs often make errors in handling time-series data, applying consistent feature engineering across different folds, and interpreting categorical variables correctly. These findings highlight a substantial gap in current models’ ability to critically evaluate and leverage expert knowledge, underscoring an essential research direction for developing more robust, knowledge-aware automated data science systems. Our data and code are publicly available [here](https://github.com/jeremyxianx/Assisted-DS).

## introduction recent advancements in large language models (llms) have led to significant progress in automat-* equal distribution. despite these capabilities, a critical aspect of llms for real-world data science remains underexplored: the effective integration and critical evaluation of external domain knowledge. in practice, data scientists do not simply run standard algorithms-they routinely incorporate domainspecific knowledge, solicit feedback from colleagues, and weigh multiple sources of information before finalizing decisions @xcite . such processes are essential for handling the complexities and ambiguities inherent in real-world data tasks. however, current research on llm-driven data science has largely focused on code generation and pipeline execution @xcite , neglecting the crucial role of external expert knowledge in practical applications. existing evaluation benchmarks @xcite @xcite rarely measure how llms process, filter, or critically adopt domain knowledge, especially when such information may be misleading or adversarial. this raises fundamental questions: can llms distinguish between helpful and harmful external domain knowledge in data science workflows? or do they simply adopt all information, risking degraded performance when exposed to adversarial guidance? in this paper, we introduce assistedds (assisted data science), a benchmark designed specifically to measure the capability of llms to leverage domain knowledge effectively and critically for data science. assistedds evaluates llm performance on tabular prediction tasks augmented with diverse domain knowledge bundles, which include ## benchmark curation our benchmark, assistedds, evaluates how effectively and critically llms leverage domain knowledge in automated data science workflows. an overview of our benchmark framework is illustrated in figure 2 . we consider synthetic datasets to have control on domain knowledge, and kaggle datasets to reflect real-world complexities on domain knowledge. for synthetic datasets we generate dataset with transparent generating mechanism, and then where we can define and inject "helpful" hints that boost performance and "adversarial" hints that can cause performance decline. for kaggle datasets we draw on real-world problems from kaggle competitions, pairing each dataset with high-quality community notebooks that embody genuine domain knowledge through feature engineering, data cleaning, and analysis. to mirror realistic scenario for llm's access to domain knowledge, we then designed tasks, which are different bundles of helpful and adversarial domain knowledge, paired with certain prompt template to let llm generate an end-to-end code for submission.csv. then we evaluate based on the code script generated by the llm. below, we elaborate more on why and how we prepare the datasets, domain knowledge, and evaluation metrics, and in section 3.1 we describe the tasks as experimental settings. adversarial hints left: dataset. we use datasets (synthetic or real-world) with defined features (e.g., location, decoration, house age, school quality) and clear train/test splits for tabular prediction tasks. center-left: domain knowledge. for each dataset, we curate bundles of domain knowledge: helpful hints (e.g., "add school quality") and adversarial hints (e.g., "remove location"), reflecting realistic expert guidance and potential pitfalls. center: task bundles. hints are grouped into tasks: helpful-only, adversarial-only, mixed, and so on. each represents different real-world information environments. center-right: llm execution. an llm receives both the data and bundled hints, together with the prompt, and generates a end-to-end code script. right: evaluation. we assess each model's generated code by (i) valid submission rate, (ii) predictive performance (macro-f1, rmse, etc), and (iii) the rates at which helpful or adversarial hints are actually adopted in the generated code. ## experimental studies to test an llm's ability to critically leverage both helpful and adversarial domain knowledge curated as described in section 2.2, we design experiments (tasks) using various combinations of helpful and adversarial hints together with different prompt configurations. the overall design principle is to vary both the proportion of helpful versus adversarial hints and the degree to which prompts inform the llm about the quality of these hints, allowing us to evaluate how llms distinguish and utilize domain knowledge under realistic conditions. ## results and analysis in this section, we present our findings and analysis on the aforementioned tasks. all experiments are conducted with 5 independent llm queries per setting. detailed tables for these results are included in appendix j. llms tend to blindly follow both helpful and adversarial hints the blind adoption of adversarial hints and misleading prompt leads to substantial declines in performance figure 3 displays the impact of hint adoption on predictive performance across different models and task settings. for synthetic datasets, all models exhibit performance gains when following helpful hints. conversely, the uncritical following of adversarial hints leads to marked performance declines-up to -28% for gpt-4o-mini. in the misleading setting for both synthetic and kaggle datasets, all llms evaluated here experience a drop in performance due to blindly following adversarial hints. notably, the human baseline shows no performance decline in any case. these results underscore a fundamental vulnerability: current llms often lack the critical reasoning needed to distinguish beneficial from harmful external knowledge. this limitation poses significant risks in real-world automated data science workflows, where adver- ## conclusion in this work, we introduced assistedds, a comprehensive benchmark for systematically evaluating the capacity of llms to effectively incorporate external expert knowledge into automated data science workflows. our experimental findings highlight crucial limitations in current llms: these models frequently exhibit an uncritical adoption of provided information, especially when exposed to adversarial or misleading content, which can sigdatasets . this chart displays the percentage change in model performance relative to the baseline condition (no hint provided). such change is calculated so that positive value indicates gain in performance. the four tasks represent different bundles of helpful and adversarial hints: helpful-only (only helpful hints provided), adversarial-only (only adversarial hints provided), mixed (both helpful and adversarial hints are provided), and mixed (misleading) (both helpful and adversarial hints are provided, with intentionally misleading prompts). each marker is the performance change averaged over settings per task and model. error bars show 1.96× standard errors. notably, for both synthetic and kaggle datasets, only human baseline persists to have no decline of performance.