Safety vs. Performance: How Multi-Objective Learning Reduces Barriers to Market Entry

• URL: http://arxiv.org/abs/2409.03734v1
• Date: Thu, 5 Sep 2024 17:45:01 GMT
• Title: Safety vs. Performance: How Multi-Objective Learning Reduces Barriers to Market Entry
• Authors: Meena Jagadeesan, Michael I. Jordan, Jacob Steinhardt,
• Abstract summary: We study whether there are insurmountable barriers to entry in emerging markets for large language models. We show that the required number of data points can be significantly smaller than the incumbent company's dataset size. Our results demonstrate how multi-objective considerations can fundamentally reduce barriers to entry.
• Score: 86.79268605140251
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Emerging marketplaces for large language models and other large-scale machine learning (ML) models appear to exhibit market concentration, which has raised concerns about whether there are insurmountable barriers to entry in such markets. In this work, we study this issue from both an economic and an algorithmic point of view, focusing on a phenomenon that reduces barriers to entry. Specifically, an incumbent company risks reputational damage unless its model is sufficiently aligned with safety objectives, whereas a new company can more easily avoid reputational damage. To study this issue formally, we define a multi-objective high-dimensional regression framework that captures reputational damage, and we characterize the number of data points that a new company needs to enter the market. Our results demonstrate how multi-objective considerations can fundamentally reduce barriers to entry -- the required number of data points can be significantly smaller than the incumbent company's dataset size. En route to proving these results, we develop scaling laws for high-dimensional linear regression in multi-objective environments, showing that the scaling rate becomes slower when the dataset size is large, which could be of independent interest.

Related papers

• Impact of Missing Values in Machine Learning: A Comprehensive Analysis [0.0]
  This paper aims to examine the nuanced impact of missing values on machine learning (ML) models. Our analysis focuses on the challenges posed by missing values, including biased inferences, reduced predictive power, and increased computational burdens. The study employs case studies and real-world examples to illustrate the practical implications of addressing missing values.
  arXiv  Detail & Related papers  (2024-10-10T18:31:44Z)
• Cross-modality Information Check for Detecting Jailbreaking in Multimodal Large Language Models [17.663550432103534]
  Multimodal Large Language Models (MLLMs) extend the capacity of LLMs to understand multimodal information comprehensively. These models are susceptible to jailbreak attacks, where malicious users can break the safety alignment of the target model and generate misleading and harmful answers. We propose Cross-modality Information DEtectoR (CIDER), a plug-and-play jailbreaking detector designed to identify maliciously perturbed image inputs.
  arXiv  Detail & Related papers  (2024-07-31T15:02:46Z)
• RigorLLM: Resilient Guardrails for Large Language Models against Undesired Content [62.685566387625975]
  Current mitigation strategies, while effective, are not resilient under adversarial attacks. This paper introduces Resilient Guardrails for Large Language Models (RigorLLM), a novel framework designed to efficiently moderate harmful and unsafe inputs.
  arXiv  Detail & Related papers  (2024-03-19T07:25:02Z)
• Robustness, Efficiency, or Privacy: Pick Two in Machine Learning [7.278033100480175]
  This paper examines the costs associated with achieving privacy and robustness in distributed machine learning architectures. Traditional noise injection hurts accuracy by concealing poisoned inputs, while cryptographic methods clash with poisoning defenses due to their non-linear nature. We outline future research directions aimed at reconciling this compromise with efficiency by considering weaker threat models.
  arXiv  Detail & Related papers  (2023-12-22T14:10:07Z)
• Bayesian Regression Markets [0.16385815610837165]
  We develop a regression market to provide a monetary incentive for data sharing. We show that similar proposals in literature expose the market agents to sizeable financial risks.
  arXiv  Detail & Related papers  (2023-10-23T14:45:51Z)
• Tackling Diverse Minorities in Imbalanced Classification [80.78227787608714]
  Imbalanced datasets are commonly observed in various real-world applications, presenting significant challenges in training classifiers. We propose generating synthetic samples iteratively by mixing data samples from both minority and majority classes. We demonstrate the effectiveness of our proposed framework through extensive experiments conducted on seven publicly available benchmark datasets.
  arXiv  Detail & Related papers  (2023-08-28T18:48:34Z)
• You Don't Need Robust Machine Learning to Manage Adversarial Attack Risks [31.111554739533663]
  The ability to subvert a machine learning model into making errant predictions is startling. Current mitigations come with a high cost and simultaneously reduce the model's accuracy. This is done with an eye toward how one would then mitigate these attacks in practice, the risks for production deployment, and how those risks could be managed.
  arXiv  Detail & Related papers  (2023-06-16T16:32:27Z)
• Enhancing Multiple Reliability Measures via Nuisance-extended Information Bottleneck [77.37409441129995]
  In practical scenarios where training data is limited, many predictive signals in the data can be rather from some biases in data acquisition. We consider an adversarial threat model under a mutual information constraint to cover a wider class of perturbations in training. We propose an autoencoder-based training to implement the objective, as well as practical encoder designs to facilitate the proposed hybrid discriminative-generative training.
  arXiv  Detail & Related papers  (2023-03-24T16:03:21Z)
• Fairness Increases Adversarial Vulnerability [50.90773979394264]
  This paper shows the existence of a dichotomy between fairness and robustness, and analyzes when achieving fairness decreases the model robustness to adversarial samples. Experiments on non-linear models and different architectures validate the theoretical findings in multiple vision domains. The paper proposes a simple, yet effective, solution to construct models achieving good tradeoffs between fairness and robustness.
  arXiv  Detail & Related papers  (2022-11-21T19:55:35Z)
• Exploring the Trade-off between Plausibility, Change Intensity and Adversarial Power in Counterfactual Explanations using Multi-objective Optimization [73.89239820192894]
  We argue that automated counterfactual generation should regard several aspects of the produced adversarial instances. We present a novel framework for the generation of counterfactual examples.
  arXiv  Detail & Related papers  (2022-05-20T15:02:53Z)

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.