Generative AI can inject bias and error at every stage of the model lifecycle (Mehrabi et al. 2021, Suresh & Guttag 2019). Consequently, for any researcher who chooses to use these tools, the primary task is not simply to generate outputs, but to continuously and rigorously validate them. This challenge is not new to the world of computational text analysis. Echoing a foundational mandate from the field of "text as data," the core principle famously articulated by Justin Grimmer and his colleagues is more relevant than ever: validate, validate, validate (Grimmer & Stewart 2013, 3). Every model output should be treated as a hypothesis requiring verification, not a conclusion to be accepted. This principle is explicitly reinforced by the platforms themselves through persistent interface warnings, such as “double-check responses.”
Validation is the process of measuring a model's output against a "ground truth"—the objective reality it is supposed to reflect. For some tasks, this is simple. However, in many research areas, such as classifying a text's political leaning or detecting toxicity, the "truth" is not an objective fact but a normative or contested judgment. The labels used to train and evaluate models in these areas are not pure data; they are theory-laden measurements, reflecting the definitions and potential biases of the human annotators who created them (Paullada et al. 2021). Treating these labels as an unquestionable "truth" risks building a model that is excellent at replicating the specific biases of its training data (Fabris et al. 2022). Therefore, a robust validation strategy must always explicitly justify its choice of benchmark or label schema and transparently report its known limitations.
Since a perfect ground truth is often unavailable, researchers rely on benchmarks. However, benchmarks are tools, not oracles. High scores on popular benchmarks can be misleading due to dataset artefacts and reliability issues. A robust validation practice is multi-metric and multi-scenario, making the trade-offs between accuracy, fairness, and efficiency visible. Frameworks like HELM (Holistic Evaluation of Language Models) are exemplary here, emphasising breadth over a single headline number (Liang et al. 2022). Researchers should complement standard benchmarks with task-specific behavioural tests and stress tests to probe a model's true capabilities.
Beyond general benchmarking, researchers must guard against several specific, persistent model flaws. A primary concern is hallucination, where models generate fluent but false content; the default stance should be adversarial, requiring researchers to cross-check claims (Ji et al. 2023). Another issue is benchmark contamination, where a model's score is inflated because its training data improperly included parts of the test set, necessitating audits and provenance checks (Deng et al. 2023). Finally, non-determinism challenges reproducibility, as outputs can vary even with identical settings. Robust validation therefore requires running multiple trials and reporting score distributions to quantify this uncertainty (Song et al. 2024).
References:
1. Deng, Chunyuan, Yilun Zhao, Xiangru Tang, Mark Gerstein & Arman Cohan. 2023. Investigating data contamination in modern benchmarks for large language models. arXiv preprint arXiv:2311.09783. Available at: https://arxiv.org/abs/2311.09783 ^ Back
2. Fabris, Alessandro, Stefano Messina, Gianmaria Silvello & Gian Antonio Susto. 2022. Algorithmic fairness datasets: the story so far. Data Mining and Knowledge Discovery, 36(6): 2074–2152. ^ Back
3. Grimmer, Justin & Brandon M. Stewart. 2013. Text as data: The promise and pitfalls of automatic content analysis methods for political texts. Political Analysis, 21(3): 267–297. ^ Back
4. Ji, Ziwei, Nayeon Lee, Rita Frieske, Tiezheng Yu, Dan Su, Yan Xu, Etsuko Ishii, Ye Jin Bang, Andrea Madotto & Pascale Fung. 2023. Survey of hallucination in natural language generation. ACM Computing Surveys, 55(12): 1–38. ^ Back
5. Liang, Percy, Rishi Bommasani, Tony Lee, Dimitris Tsipras, Dilara Soylu, Michihiro Yasunaga, Yian Zhang, et al. 2022. Holistic evaluation of language models. arXiv preprint arXiv:2211.09110. Available at: https://arxiv.org/abs/2211.09110 ^ Back
6. Mehrabi, Ninareh, Morstatter, Fred, Saxena, Nripsuta, Lerman, Kristina & Galstyan, Aram. 2021. A survey on bias and fairness in machine learning. ACM Computing Surveys (CSUR), 54(6): 1–35. ^ Back
7. Paullada, Amandalynne, Inioluwa Deborah Raji, Emily M. Bender, Emily Denton & Alex Hanna. 2021. Data and its (dis) contents: A survey of dataset development and use in machine learning research. Patterns, 2(11). ^ Back
8. Song, Yifan, Guoyin Wang, Sujian Li & Bill Yuchen Lin. 2024. The good, the bad, and the greedy: Evaluation of LLMs should not ignore non-determinism. arXiv preprint arXiv:2407.10457. Available at: https://arxiv.org/abs/2407.10457 ^ Back
9. Suresh, Harini & Guttag, John V. 2019. A Framework for Understanding Sources of Harm throughout the Machine Learning Life Cycle. arXiv preprint arXiv:1901.10002. Presented at EAAMO 2021: Equity and Access in Algorithms, Mechanisms, and Optimization. Available at: https://doi.org/10.48550/arXiv.1901.10002 ^ Back
