ICLR 2026 Papers

Large language models (LLMs) have demonstrated impressive capabilities across a wide range of natural language processing tasks. However, their outputs often exhibit social biases, raising fairness concerns. Existing debiasing methods, such as fine-tuning on additional datasets or prompt engineering, face scalability issues or compromise user experience in multi-turn interactions. To address these challenges, we propose a framework for detecting stereotype-inducing words and attributing neuron-level bias in LLMs, without the need for fine-tuning or prompt modification. Our framework first identifies stereotype-inducing adjectives and nouns via comparative analysis across demographic groups. We then attribute biased behavior to specific neurons using two attribution strategies based on integrated gradients. Finally, we mitigate bias by directly intervening on their activations at the projection layer. Experiments on three widely used LLMs demonstrate that our method effectively reduces bias while preserving overall model performance.

Data-Free Robustness Distillation (DFRD) aims to transfer the robustness from the teacher to the student without accessing the training data. While existing methods focus on overall robustness, they overlook the robust fairness issues, leading to severe disparity of robustness across different categories. In this paper, we find two key problems: (1) student model distilled with equal class proportion data behaves significantly different across distinct categories; and (2) the robustness of student model is not stable across different attacks target. To bridge these gaps, we present the first Fairness Enhanced data-free Robustness Distillation (FERD) framework to adjust the proportion and distribution of adversarial examples. For the proportion, FERD adopts a robustness guided class reweighting strategy to synthesize more samples for the less robust categories, thereby improving robustness of them. For the distribution, FERD generates complementary data samples for advanced robustness distillation. It generates Fairness-Aware Examples (FAEs) by enforcing a uniformity constraint on feature-level predictions, which suppress the dominance of class-specific non-robust features, providing a more balanced representation across all categories. Then, FERD constructs Uniform-Target Adversarial Examples (UTAEs) from FAEs by applying a uniform target class constraint to avoid biased attack directions, which distribute the attack targets across all categories and prevents overfitting to specific vulnerable categories. Extensive experiments on three public datasets show that FERD achieves state-of-the-art worst-class robustness under all adversarial attack (e.g., the worst-class robustness under FGSM and AutoAttack are improved by 15.1% and 6.4% using MobileNetV2 on CIFAR-10), demonstrating superior performance in both robustness and fairness aspects. Our code is available at: [https://anonymous.4open.science/r/FERD-2A48/](https://anonymous.4open.science/r/FERD-2A48/).

Selecting representatives based on voters' preferences is a fundamental problem in social choice theory. While cardinal utility functions offer a detailed representation of preferences, voters often cannot precisely quantify their affinity towards a given candidate. As a result, modern voting systems rely on ordinal rankings to simplistically represent preference profiles. In quantifying the suboptimality of solutions due to the loss of information when using ordinal preferences, the metric distortion framework models voters and candidates as points in a metric space, with distortion bounding the efficiency loss. Prior works within this framework use the distance between a voter and a candidate in the underlying metric as the cost of selecting the candidate for the given voter, with a goal of minimizing the sum (utilitarian) or maximum (egalitarian) of costs across voters. For deterministic election mechanisms selecting a single winning candidate, the best possible distortion is known to be 3 for any metric, as established by Gkatzelis, Halpern, and Shah (FOCS'20). In contrast, for randomized mechanisms, distortions cannot be lower than $2.112$, as shown by Charikar and Ramakrishnan (SODA'22), and there exists a mechanism with a distortion guarantee of $2.753$, according to Charikar, Ramakrishnan, Wang, and Wu (SODA'24 Best Paper Award). Our work asks: can one obtain a better approximation compared to an optimal candidate by selecting a committee of $k$ candidates ($k \ge 1$), where the cost of a voter is defined to be its distance to the closest candidate in the committee? We affirmatively answer this question by introducing the concept of bi-criteria approximation within the metric distortion framework. In the line metric, it is possible to achieve optimal cost with only $O(1)$ candidates. In contrast, we also prove that in both the two-dimensional and tree metrics -- which naturally generalize the line metric -- achieving optimal cost is impossible unless all candidates are selected. These results apply to both utilitarian and egalitarian objectives. Our results establish a stark separation between the line metric and the 2D or tree metric in the context of the metric distortion problem.