Harnessing non-adversarial robustness in large language models

• URL: http://arxiv.org/abs/2605.29816v1
• Date: Thu, 28 May 2026 12:00:00 GMT
• Title: Harnessing non-adversarial robustness in large language models
• Abstract summary: We show that robustness can be achieved via a simple fine-tuning process: debiasing for robustness.<n>We identify conditions when debiasing helps and when it does not, and demonstrate, through both theory and extensive experiments, that debiasing may indeed be a quick and efficient tool to enhance robustness.
• Score: 53.703320836018605
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The work presents an approach for addressing the challenge of robustness in Large Language Models (LLMs) to alterations and potential errors caused by semantically similar but textually different prompts. Recent works have shown that these kinds of prompt variations can significantly impact the performance of LLMs on tasks. The central question is: can LLMs' robustness to semantically-neutral prompt alterations be acquired without expensive retraining of the entire model? We address this question both theoretically and through experiments. Our theoretical analysis reveals a crucial factor impacting model robustness - a systematic expected shift or perturbation-induced bias in neural network module outputs. Motivated by this analysis, we show that robustness can be achieved via a simple fine-tuning process: debiasing for robustness. We identify conditions when debiasing helps and when it does not, and demonstrate, through both theory and extensive experiments, that debiasing for robustness may indeed be a quick and efficient tool to enhance robustness and provide certification against random prompt perturbations.

Related papers

• Clustered Self-Assessment: A Simple yet Effective Method for Uncertainty Quantification in Large Language Models [44.59799077650502]
  We propose a simple yet effective self-assessment method for uncertainty quantification in large language models.<n>Our approach groups sampled generations into semantically distinct clusters, converts them into answer options in a structured multiple-choice question, and uses the probability assigned by the model to each option as a confidence estimate.
  arXiv  Detail & Related papers  (2026-06-02T16:25:54Z)
• What Makes VLMs Robust? Towards Reconciling Robustness and Accuracy in Vision-Language Models [64.99746027349767]
  Adversarial Robustness Adaptation (R-Adapt) is a framework that freezes all pre-trained weights and introduces minimal, insight-driven adaptations only in the initial layers.<n>R-Adapt generalizes efficiently to large vision-language models (e.g., LLaVA and Qwen-VL) to enhance their robustness.
  arXiv  Detail & Related papers  (2026-03-13T09:02:11Z)
• Analyzing Reasoning Consistency in Large Multimodal Models under Cross-Modal Conflicts [74.47786985522762]
  We identify a critical failure mode termed textual inertia, where models tend to blindly adhere to the erroneous text while neglecting conflicting visual evidence.<n>We propose the LogicGraph Perturbation Protocol that structurally injects perturbations into the reasoning chains of diverse LMMs.<n>Results reveal that models successfully self-correct in less than 10% of cases and predominantly succumb to blind textual error propagation.
  arXiv  Detail & Related papers  (2026-01-07T16:39:34Z)
• TokUR: Token-Level Uncertainty Estimation for Large Language Model Reasoning [27.449948943467163]
  We propose a Token-level Uncertainty estimation framework for Reasoning (TokUR)<n>TokUR enables Large Language Models to self-assess and self-improve their responses in mathematical reasoning.<n> Experiments on mathematical reasoning datasets of varying difficulty demonstrate that TokUR exhibits a strong correlation with answer correctness and model robustness.
  arXiv  Detail & Related papers  (2025-05-16T22:47:32Z)
• Rigorous Probabilistic Guarantees for Robust Counterfactual Explanations [80.86128012438834]
  We show for the first time that computing the robustness of counterfactuals with respect to plausible model shifts is NP-complete. We propose a novel probabilistic approach which is able to provide tight estimates of robustness with strong guarantees.
  arXiv  Detail & Related papers  (2024-07-10T09:13:11Z)
• Extreme Miscalibration and the Illusion of Adversarial Robustness [66.29268991629085]
  Adversarial Training is often used to increase model robustness. We show that this observed gain in robustness is an illusion of robustness (IOR) We urge the NLP community to incorporate test-time temperature scaling into their robustness evaluations.
  arXiv  Detail & Related papers  (2024-02-27T13:49:12Z)
• Distinguishing the Knowable from the Unknowable with Language Models [15.471748481627143]
  In the absence of ground-truth probabilities, we explore a setting where, in order to disentangle a given uncertainty, a significantly larger model stands in as a proxy for the ground truth. We show that small linear probes trained on the embeddings of frozen, pretrained models accurately predict when larger models will be more confident at the token level. We propose a fully unsupervised method that achieves non-trivial accuracy on the same task.
  arXiv  Detail & Related papers  (2024-02-05T22:22:49Z)
• RoMA: a Method for Neural Network Robustness Measurement and Assessment [0.0]
  We present a new statistical method, called Robustness Measurement and Assessment (RoMA) RoMA determines the probability that a random input perturbation might cause misclassification. One interesting insight obtained through this work is that, in a classification network, different output labels can exhibit very different robustness levels.
  arXiv  Detail & Related papers  (2021-10-21T12:01:54Z)
• Non-Singular Adversarial Robustness of Neural Networks [58.731070632586594]
  Adrial robustness has become an emerging challenge for neural network owing to its over-sensitivity to small input perturbations. We formalize the notion of non-singular adversarial robustness for neural networks through the lens of joint perturbations to data inputs as well as model weights.
  arXiv  Detail & Related papers  (2021-02-23T20:59:30Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.