Automated Capability Evaluation of Foundation Models

• URL: http://arxiv.org/abs/2505.17228v1
• Date: Thu, 22 May 2025 19:09:57 GMT
• Title: Automated Capability Evaluation of Foundation Models
• Authors: Arash Afkanpour, Omkar Dige, Fatemeh Tavakoli,
• Abstract summary: Active learning for Capability Evaluation (ACE) is a novel framework for scalable, automated, and fine-grained evaluation of foundation models.<n>To maximize coverage and efficiency, ACE models a subject model's performance as a capability function over a latent semantic space.<n>This adaptive evaluation strategy enables cost-effective discovery of strengths, weaknesses, and failure modes that static benchmarks may miss.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Current evaluation frameworks for foundation models rely heavily on fixed, manually curated benchmarks, limiting their ability to capture the full breadth of model capabilities. This paper introduces Active learning for Capability Evaluation (ACE), a novel framework for scalable, automated, and fine-grained evaluation of foundation models. ACE leverages the knowledge embedded in powerful language models to decompose a domain into semantically meaningful capabilities and generate diverse evaluation tasks, significantly reducing human effort. To maximize coverage and efficiency, ACE models a subject model's performance as a capability function over a latent semantic space and uses active learning to prioritize the evaluation of the most informative capabilities. This adaptive evaluation strategy enables cost-effective discovery of strengths, weaknesses, and failure modes that static benchmarks may miss. Our results suggest that ACE provides a more complete and informative picture of model capabilities, which is essential for safe and well-informed deployment of foundation models.

Related papers

• ScoresActivation: A New Activation Function for Model Agnostic Global Explainability by Design [0.815557531820863]
  We introduce a novel differentiable approach to global explainability by design.<n>We integrate feature importance estimation directly into model training.<n>Our approach yields globally faithful, stable feature rankings aligned with SHAP values and ground-truth feature importance.
  arXiv  Detail & Related papers  (2025-11-17T18:10:34Z)
• Forgetting: A New Mechanism Towards Better Large Language Model Fine-tuning [53.398270878295754]
  Supervised fine-tuning (SFT) plays a critical role for pretrained large language models (LLMs)<n>We suggest categorizing tokens within each corpus into two parts -- positive and negative tokens -- based on whether they are useful to improve model performance.<n>We conduct experiments on well-established benchmarks, finding that this forgetting mechanism not only improves overall model performance and also facilitate more diverse model responses.
  arXiv  Detail & Related papers  (2025-08-06T11:22:23Z)
• RoHOI: Robustness Benchmark for Human-Object Interaction Detection [84.78366452133514]
  Human-Object Interaction (HOI) detection is crucial for robot-human assistance, enabling context-aware support.<n>We introduce the first benchmark for HOI detection, evaluating model resilience under diverse challenges.<n>Our benchmark, RoHOI, includes 20 corruption types based on the HICO-DET and V-COCO datasets and a new robustness-focused metric.
  arXiv  Detail & Related papers  (2025-07-12T01:58:04Z)
• AI in a vat: Fundamental limits of efficient world modelling for agent sandboxing and interpretability [84.52205243353761]
  Recent work proposes using world models to generate controlled virtual environments in which AI agents can be tested before deployment.<n>We investigate ways of simplifying world models that remain agnostic to the AI agent under evaluation.
  arXiv  Detail & Related papers  (2025-04-06T20:35:44Z)
• Can foundation models actively gather information in interactive environments to test hypotheses? [56.651636971591536]
  We introduce a framework in which a model must determine the factors influencing a hidden reward function.<n>We investigate whether approaches such as self- throughput and increased inference time improve information gathering efficiency.
  arXiv  Detail & Related papers  (2024-12-09T12:27:21Z)
• ACT-Bench: Towards Action Controllable World Models for Autonomous Driving [2.6749009435602122]
  World models have emerged as promising neural simulators for autonomous driving.<n>We develop an open-access evaluation framework, ACT-Bench, for quantifying action fidelity.<n>We demonstrate that the state-of-the-art model does not fully adhere to given instructions, while Terra achieves improved action fidelity.
  arXiv  Detail & Related papers  (2024-12-06T01:06:28Z)
• Idempotent Unsupervised Representation Learning for Skeleton-Based Action Recognition [13.593511876719367]
  We propose a novel skeleton-based idempotent generative model (IGM) for unsupervised representation learning. Our experiments on benchmark datasets, NTU RGB+D and PKUMMD, demonstrate the effectiveness of our proposed method.
  arXiv  Detail & Related papers  (2024-10-27T06:29:04Z)
• Transforming In-Vehicle Network Intrusion Detection: VAE-based Knowledge Distillation Meets Explainable AI [0.0]
  This paper introduces an advanced intrusion detection system (IDS) called KD-XVAE that uses a Variational Autoencoder (VAE)-based knowledge distillation approach. Our model significantly reduces complexity, operating with just 1669 parameters and achieving an inference time of 0.3 ms per batch.
  arXiv  Detail & Related papers  (2024-10-11T17:57:16Z)
• SOLD: Slot Object-Centric Latent Dynamics Models for Relational Manipulation Learning from Pixels [16.020835290802548]
  Slot-Attention for Object-centric Latent Dynamics is a novel model-based reinforcement learning algorithm.<n>It learns object-centric dynamics models in an unsupervised manner from pixel inputs.<n>We demonstrate that the structured latent space not only improves model interpretability but also provides a valuable input space for behavior models to reason over.
  arXiv  Detail & Related papers  (2024-10-11T14:03:31Z)
• A Retention-Centric Framework for Continual Learning with Guaranteed Model Developmental Safety [75.8161094916476]
  In real-world applications, learning-enabled systems often undergo iterative model development to address challenging or emerging tasks.<n>New or improving existing capabilities may inadvertently lose good capabilities of the old model, also known as catastrophic forgetting.<n>We propose a retention-centric framework with data-dependent constraints, and study how to continually develop a pretrained CLIP model for acquiring new or improving existing capabilities of image classification.
  arXiv  Detail & Related papers  (2024-10-04T22:34:58Z)
• Erasing Conceptual Knowledge from Language Models [24.63143961814566]
  Erasure of Language Memory (ELM) is an approach for concept-level unlearning built on the principle of matching the distribution defined by an introspective classifier.<n>ELM applies this framework to create targeted low-rank updates that reduce generation probabilities for concept-specific content.<n>We demonstrate ELM's efficacy on biosecurity, cybersecurity, and literary domain erasure tasks.
  arXiv  Detail & Related papers  (2024-10-03T17:59:30Z)
• Benchmarks as Microscopes: A Call for Model Metrology [76.64402390208576]
  Modern language models (LMs) pose a new challenge in capability assessment. To be confident in our metrics, we need a new discipline of model metrology.
  arXiv  Detail & Related papers  (2024-07-22T17:52:12Z)
• Data Quality Aware Approaches for Addressing Model Drift of Semantic Segmentation Models [1.6385815610837167]
  This study investigates two prominent quality aware strategies to combat model drift. The former leverages image quality assessment metrics to meticulously select high-quality training data, improving the model robustness. The latter makes use of learned vectors feature from existing models to guide the selection of future data, aligning it with the model's prior knowledge.
  arXiv  Detail & Related papers  (2024-02-11T18:01:52Z)
• Unifying Self-Supervised Clustering and Energy-Based Models [9.3176264568834]
  We establish a principled connection between self-supervised learning and generative models.<n>We show that our solution can be integrated into a neuro-symbolic framework to tackle a simple yet non-trivial instantiation of the symbol grounding problem.
  arXiv  Detail & Related papers  (2023-12-30T04:46:16Z)
• QualEval: Qualitative Evaluation for Model Improvement [82.73561470966658]
  We propose QualEval, which augments quantitative scalar metrics with automated qualitative evaluation as a vehicle for model improvement. QualEval uses a powerful LLM reasoner and our novel flexible linear programming solver to generate human-readable insights. We demonstrate that leveraging its insights, for example, improves the absolute performance of the Llama 2 model by up to 15% points relative.
  arXiv  Detail & Related papers  (2023-11-06T00:21:44Z)
• When Demonstrations Meet Generative World Models: A Maximum Likelihood Framework for Offline Inverse Reinforcement Learning [62.00672284480755]
  This paper aims to recover the structure of rewards and environment dynamics that underlie observed actions in a fixed, finite set of demonstrations from an expert agent. Accurate models of expertise in executing a task has applications in safety-sensitive applications such as clinical decision making and autonomous driving.
  arXiv  Detail & Related papers  (2023-02-15T04:14:20Z)
• MACE: An Efficient Model-Agnostic Framework for Counterfactual Explanation [132.77005365032468]
  We propose a novel framework of Model-Agnostic Counterfactual Explanation (MACE) In our MACE approach, we propose a novel RL-based method for finding good counterfactual examples and a gradient-less descent method for improving proximity. Experiments on public datasets validate the effectiveness with better validity, sparsity and proximity.
  arXiv  Detail & Related papers  (2022-05-31T04:57:06Z)
• Feeding What You Need by Understanding What You Learned [54.400455868448695]
  Machine Reading (MRC) reveals the ability to understand a given text passage and answer questions based on it. Existing research works in MRC rely heavily on large-size models and corpus to improve the performance evaluated by metrics such as Exact Match. We argue that a deep understanding of model capabilities and data properties can help us feed a model with appropriate training data.
  arXiv  Detail & Related papers  (2022-03-05T14:15:59Z)

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.