# Confronting LLMs with Traditional ML: Rethinking the Fairness of Large Language Models in Tabular Classifications

arXiv: https://arxiv.org/abs/2310.14607 · HTML: https://mlsanity.com/paper/2310.14607/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Placed in Reasoning benchmarks and evaluation, one of 17 directions in LLM reasoning.
- Builds on 27 of its 31 references that are in the map.
- First posted on arXiv 23 Oct 2023.

## Placed in

- LLM reasoning / [Reasoning benchmarks and evaluation](https://mlsanity.com/lr/reasoning-benchmarks-and-evaluation/)

## Builds on (most cited first, 27 of 27)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [On the Opportunities and Risks of Foundation Models](https://mlsanity.com/paper/2108.07258/) (2021)
- [Finetuned Language Models Are Zero-Shot Learners](https://mlsanity.com/paper/2109.01652/) (2021)
- [Rethinking the Role of Demonstrations: What Makes In-Context Learning Work?](https://mlsanity.com/paper/2202.12837/) (2022)
- [Calibrate Before Use: Improving Few-Shot Performance of Language Models](https://mlsanity.com/paper/2102.09690/) (2021)
- [Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings](https://mlsanity.com/paper/1607.06520/) (2016)
- [What Makes Good In-Context Examples for GPT-3?](https://mlsanity.com/paper/2101.06804/) (2021)
- [A General Language Assistant as a Laboratory for Alignment](https://mlsanity.com/paper/2112.00861/) (2021)
- [An Explanation of In-context Learning as Implicit Bayesian Inference](https://mlsanity.com/paper/2111.02080/) (2021)
- [Semantics derived automatically from language corpora contain human-like biases](https://mlsanity.com/paper/1608.07187/) (2016)
- [What learning algorithm is in-context learning? Investigations with linear models](https://mlsanity.com/paper/2211.15661/) (2022)
- [TabNet: Attentive Interpretable Tabular Learning](https://mlsanity.com/paper/1908.07442/) (2019)
- [Deep Neural Networks and Tabular Data: A Survey](https://mlsanity.com/paper/2110.01889/) (2021)
- [Men Also Like Shopping: Reducing Gender Bias Amplification using Corpus-level Constraints](https://mlsanity.com/paper/1707.09457/) (2017)
- [TaBERT: Pretraining for Joint Understanding of Textual and Tabular Data](https://mlsanity.com/paper/2005.08314/) (2020)
- [Larger language models do in-context learning differently](https://mlsanity.com/paper/2303.03846/) (2023)
- [Persistent Anti-Muslim Bias in Large Language Models](https://mlsanity.com/paper/2101.05783/) (2021)
- [AI Fairness 360: An Extensible Toolkit for Detecting, Understanding, and Mitigating Unwanted Algorithmic Bias](https://mlsanity.com/paper/1810.01943/) (2018)
- [Predictability and Surprise in Large Generative Models](https://mlsanity.com/paper/2202.07785/) (2022)
- [Evaluating the Underlying Gender Bias in Contextualized Word Embeddings](https://mlsanity.com/paper/1904.08783/) (2019)
- [Symbol tuning improves in-context learning in language models](https://mlsanity.com/paper/2305.08298/) (2023)
- [Z-ICL: Zero-Shot In-Context Learning with Pseudo-Demonstrations](https://mlsanity.com/paper/2212.09865/) (2022)
- [Multi-VALUE: A Framework for Cross-Dialectal English NLP](https://mlsanity.com/paper/2212.08011/) (2022)
- [TABLET: Learning From Instructions For Tabular Data](https://mlsanity.com/paper/2304.13188/) (2023)
- [DADA: Dialect Adaptation via Dynamic Aggregation of Linguistic Rules](https://mlsanity.com/paper/2305.13406/) (2023)
- [Semantic-Oriented Unlabeled Priming for Large-Scale Language Models](https://mlsanity.com/paper/2202.06133/) (2022)

## Built on by (most cited first, 15 of 15)

- [On the Hardness of Faithful Chain-of-Thought Reasoning in Large Language Models](https://mlsanity.com/paper/2406.10625/) (2024)
- [The Typing Cure: Experiences with Large Language Model Chatbots for Mental Health Support](https://mlsanity.com/paper/2401.14362/) (2024)
- [Few-Shot Fairness: Unveiling LLM's Potential for Fairness-Aware Classification](https://mlsanity.com/paper/2402.18502/) (2024)
- [Improving LLM Group Fairness on Tabular Data via In-Context Learning](https://mlsanity.com/paper/2412.04642/) (2024)
- [Emergent Bias and Fairness in Multi-Agent Decision Systems](https://mlsanity.com/paper/2512.16433/) (2025)
- [Equal Access, Unequal Interaction: A Counterfactual Audit of LLM Fairness](https://mlsanity.com/paper/2602.02932/) (2026)
- [Fair In-Context Learning via Latent Concept Variables](https://mlsanity.com/paper/2411.02671/) (2024)
- [Emergence of Biased Consensus in Multi-Agent LLM Debates](https://mlsanity.com/paper/2608.02827/) (2026)
- [Predicting Language Models' Success at Zero-Shot Probabilistic Prediction](https://mlsanity.com/paper/2509.15356/) (2025)
- [SureMap: Simultaneous Mean Estimation for Single-Task and Multi-Task Disaggregated Evaluation](https://mlsanity.com/paper/2411.09730/) (2024)
- [In-Context Bias Propagation in LLM-Based Tabular Data Generation](https://mlsanity.com/paper/2506.09630/) (2025)
- [When Names Change Verdicts: Intervention Consistency Reveals Systematic Bias in LLM Decision-Making](https://mlsanity.com/paper/2603.18530/) (2026)
- [Group Fairness Meets the Black Box: Enabling Fair Algorithms on Closed LLMs via Post-Processing](https://mlsanity.com/paper/2508.11258/) (2025)
- [SMITE: Enhancing Fairness in LLMs through Optimal In-Context Example Selection via Dynamic Validation](https://mlsanity.com/paper/2508.17735/) (2025)
- [Algorithmic Harms Associated with Generative Model-Augmented Recommendation Systems](https://mlsanity.com/paper/2609.33073/) (2026)

## Abstract (arXiv)

Recent literature has suggested the potential of using large language models (LLMs) to make classifications for tabular tasks. However, LLMs have been shown to exhibit harmful social biases that reflect the stereotypes and inequalities present in society. To this end, as well as the widespread use of tabular data in many high-stake applications, it is important to explore the following questions: what sources of information do LLMs draw upon when making classifications for tabular tasks; whether and to what extent are LLM classifications for tabular data influenced by social biases and stereotypes; and what are the consequential implications for fairness? Through a series of experiments, we delve into these questions and show that LLMs tend to inherit social biases from their training data which significantly impact their fairness in tabular classification tasks. Furthermore, our investigations show that in the context of bias mitigation, though in-context learning and finetuning have a moderate effect, the fairness metric gap between different subgroups is still larger than that in traditional machine learning models, such as Random Forest and shallow Neural Networks. This observation emphasizes that the social biases are inherent within the LLMs themselves and inherited from their pretraining corpus, not only from the downstream task datasets. Besides, we demonstrate that label-flipping of in-context examples can significantly reduce biases, further highlighting the presence of inherent bias within LLMs.