Language Models That Walk the Talk: A Framework for Formal Fairness Certificates

• URL: http://arxiv.org/abs/2505.12767v1
• Date: Mon, 19 May 2025 06:46:17 GMT
• Title: Language Models That Walk the Talk: A Framework for Formal Fairness Certificates
• Authors: Danqing Chen, Tobias Ladner, Ahmed Rayen Mhadhbi, Matthias Althoff,
• Abstract summary: This work presents a holistic verification framework to certify the robustness of transformer-based language models.<n>We focus on ensuring gender fairness and consistent outputs across different gender-related terms.<n>We extend this methodology to toxicity detection, offering formal guarantees that adversarially manipulated toxic inputs are consistently detected and appropriately censored.
• Score: 6.5301153208275675
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: As large language models become integral to high-stakes applications, ensuring their robustness and fairness is critical. Despite their success, large language models remain vulnerable to adversarial attacks, where small perturbations, such as synonym substitutions, can alter model predictions, posing risks in fairness-critical areas, such as gender bias mitigation, and safety-critical areas, such as toxicity detection. While formal verification has been explored for neural networks, its application to large language models remains limited. This work presents a holistic verification framework to certify the robustness of transformer-based language models, with a focus on ensuring gender fairness and consistent outputs across different gender-related terms. Furthermore, we extend this methodology to toxicity detection, offering formal guarantees that adversarially manipulated toxic inputs are consistently detected and appropriately censored, thereby ensuring the reliability of moderation systems. By formalizing robustness within the embedding space, this work strengthens the reliability of language models in ethical AI deployment and content moderation.

Related papers

• From Passive Metric to Active Signal: The Evolving Role of Uncertainty Quantification in Large Language Models [77.04403907729738]
  This survey charts the evolution of uncertainty from a passive diagnostic metric to an active control signal guiding real-time model behavior.<n>We demonstrate how uncertainty is leveraged as an active control signal across three frontiers.<n>This survey argues that mastering the new trend of uncertainty is essential for building the next generation of scalable, reliable, and trustworthy AI.
  arXiv  Detail & Related papers  (2026-01-22T06:21:31Z)
• Confident, Calibrated, or Complicit: Probing the Trade-offs between Safety Alignment and Ideological Bias in Language Models in Detecting Hate Speech [0.916708284510944]
  We investigate the efficacy of Large Language Models (LLMs) in detecting implicit and explicit hate speech.<n>We find that censored models significantly outperform their uncensored counterparts in both accuracy and robustness.
  arXiv  Detail & Related papers  (2025-08-31T03:00:55Z)
• Evaluating Language Model Reasoning about Confidential Information [95.64687778185703]
  We study whether language models exhibit contextual robustness, or the capability to adhere to context-dependent safety specifications.<n>We develop a benchmark (PasswordEval) that measures whether language models can correctly determine when a user request is authorized.<n>We find that current open- and closed-source models struggle with this seemingly simple task, and that, perhaps surprisingly, reasoning capabilities do not generally improve performance.
  arXiv  Detail & Related papers  (2025-08-27T15:39:46Z)
• Benchmarking the Spatial Robustness of DNNs via Natural and Adversarial Localized Corruptions [49.546479320670464]
  This paper introduces specialized metrics for benchmarking the spatial robustness of segmentation models.<n>We propose region-aware multi-attack adversarial analysis, a method that enables a deeper understanding of model robustness.<n>The results reveal that models respond to these two types of threats differently.
  arXiv  Detail & Related papers  (2025-04-02T11:37:39Z)
• Enhancing Trust in Large Language Models with Uncertainty-Aware Fine-Tuning [10.457661605916435]
  Large language models (LLMs) have revolutionized the field of natural language processing with their impressive reasoning and question-answering capabilities.<n>LLMs are sometimes prone to generating credible-sounding but incorrect information, a phenomenon known as hallucinations.<n>We introduce a novel uncertainty-aware causal language modeling loss function, grounded in the principles of decision theory.
  arXiv  Detail & Related papers  (2024-12-03T23:14:47Z)
• On the Fairness, Diversity and Reliability of Text-to-Image Generative Models [68.62012304574012]
  multimodal generative models have sparked critical discussions on their reliability, fairness and potential for misuse.<n>We propose an evaluation framework to assess model reliability by analyzing responses to global and local perturbations in the embedding space.<n>Our method lays the groundwork for detecting unreliable, bias-injected models and tracing the provenance of embedded biases.
  arXiv  Detail & Related papers  (2024-11-21T09:46:55Z)
• Epistemic Integrity in Large Language Models [11.173637560124828]
  Large language models are increasingly relied upon sources of information, but their propensity for false or misleading statements poses high risks for users and society. In this paper, we confront the critical problem of miscalibration where a model's linguistic assertiveness fails to reflect its true internal certainty. We introduce a new human misalignment evaluation and a novel method for measuring the linguistic assertiveness of Large Language Models.
  arXiv  Detail & Related papers  (2024-11-10T17:10:13Z)
• Trustworthy Alignment of Retrieval-Augmented Large Language Models via Reinforcement Learning [84.94709351266557]
  We focus on the trustworthiness of language models with respect to retrieval augmentation. We deem that retrieval-augmented language models have the inherent capabilities of supplying response according to both contextual and parametric knowledge. Inspired by aligning language models with human preference, we take the first step towards aligning retrieval-augmented language models to a status where it responds relying merely on the external evidence.
  arXiv  Detail & Related papers  (2024-10-22T09:25:21Z)
• Collapsed Language Models Promote Fairness [88.48232731113306]
  We find that debiased language models exhibit collapsed alignment between token representations and word embeddings.<n>We design a principled fine-tuning method that can effectively improve fairness in a wide range of debiasing methods.
  arXiv  Detail & Related papers  (2024-10-06T13:09:48Z)
• FaithEval: Can Your Language Model Stay Faithful to Context, Even If "The Moon is Made of Marshmallows" [74.7488607599921]
  FaithEval is a benchmark to evaluate the faithfulness of large language models (LLMs) in contextual scenarios.<n>FaithEval comprises 4.9K high-quality problems in total, validated through a rigorous four-stage context construction and validation framework.<n>Our study reveals that even state-of-the-art models often struggle to remain faithful to the given context, and that larger models do not necessarily exhibit improved faithfulness.
  arXiv  Detail & Related papers  (2024-09-30T06:27:53Z)
• DPP-Based Adversarial Prompt Searching for Lanugage Models [56.73828162194457]
  Auto-regressive Selective Replacement Ascent (ASRA) is a discrete optimization algorithm that selects prompts based on both quality and similarity with determinantal point process (DPP) Experimental results on six different pre-trained language models demonstrate the efficacy of ASRA for eliciting toxic content.
  arXiv  Detail & Related papers  (2024-03-01T05:28:06Z)
• Contrastive Perplexity for Controlled Generation: An Application in Detoxifying Large Language Models [21.341749351654453]
  The generation of toxic content by large language models (LLMs) remains a critical challenge for the safe deployment of language technology.<n>We propose a novel framework for implicit knowledge editing and controlled text generation by fine-tuning LLMs with a prototype-based contrastive perplexity objective.
  arXiv  Detail & Related papers  (2024-01-16T16:49:39Z)
• Unveiling Safety Vulnerabilities of Large Language Models [4.562678399685183]
  This paper introduces a unique dataset containing adversarial examples in the form of questions, which we call AttaQ. We assess the efficacy of our dataset by analyzing the vulnerabilities of various models when subjected to it. We introduce a novel automatic approach for identifying and naming vulnerable semantic regions.
  arXiv  Detail & Related papers  (2023-11-07T16:50:33Z)
• Reward Modeling for Mitigating Toxicity in Transformer-based Language Models [0.0]
  Transformer-based language models are able to generate fluent text and be efficiently adapted across various natural language generation tasks. Language models that are pretrained on large unlabeled web text corpora have been shown to suffer from degenerating toxic content and social bias behaviors. We propose Reinforce-Detoxify; A reinforcement learning-based method for mitigating toxicity in language models.
  arXiv  Detail & Related papers  (2022-02-19T19:26:22Z)

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.