Turing Representational Similarity Analysis (RSA): A Flexible Method for Measuring Alignment Between Human and Artificial Intelligence

• URL: http://arxiv.org/abs/2412.00577v1
• Date: Sat, 30 Nov 2024 20:24:52 GMT
• Title: Turing Representational Similarity Analysis (RSA): A Flexible Method for Measuring Alignment Between Human and Artificial Intelligence
• Authors: Mattson Ogg, Ritwik Bose, Jamie Scharf, Christopher Ratto, Michael Wolmetz,
• Abstract summary: We developed Turing Representational Similarity Analysis (RSA), a method that uses pairwise similarity ratings to quantify alignment between AIs and humans. We tested this approach on semantic alignment across text and image modalities, measuring how different Large Language and Vision Language Model (LLM and VLM) similarity judgments aligned with human responses at both group and individual levels.
• Score: 0.62914438169038
• License:
• Abstract: As we consider entrusting Large Language Models (LLMs) with key societal and decision-making roles, measuring their alignment with human cognition becomes critical. This requires methods that can assess how these systems represent information and facilitate comparisons to human understanding across diverse tasks. To meet this need, we developed Turing Representational Similarity Analysis (RSA), a method that uses pairwise similarity ratings to quantify alignment between AIs and humans. We tested this approach on semantic alignment across text and image modalities, measuring how different Large Language and Vision Language Model (LLM and VLM) similarity judgments aligned with human responses at both group and individual levels. GPT-4o showed the strongest alignment with human performance among the models we tested, particularly when leveraging its text processing capabilities rather than image processing, regardless of the input modality. However, no model we studied adequately captured the inter-individual variability observed among human participants. This method helped uncover certain hyperparameters and prompts that could steer model behavior to have more or less human-like qualities at an inter-individual or group level. Turing RSA enables the efficient and flexible quantification of human-AI alignment and complements existing accuracy-based benchmark tasks. We demonstrate its utility across multiple modalities (words, sentences, images) for understanding how LLMs encode knowledge and for examining representational alignment with human cognition.

Related papers

• HLB: Benchmarking LLMs' Humanlikeness in Language Use [2.438748974410787]
  We present a comprehensive humanlikeness benchmark (HLB) evaluating 20 large language models (LLMs) We collected responses from over 2,000 human participants and compared them to outputs from the LLMs in these experiments. Our results reveal fine-grained differences in how well LLMs replicate human responses across various linguistic levels.
  arXiv  Detail & Related papers  (2024-09-24T09:02:28Z)
• PersLLM: A Personified Training Approach for Large Language Models [66.16513246245401]
  We propose PersLLM, integrating psychology-grounded principles of personality: social practice, consistency, and dynamic development. We incorporate personality traits directly into the model parameters, enhancing the model's resistance to induction, promoting consistency, and supporting the dynamic evolution of personality.
  arXiv  Detail & Related papers  (2024-07-17T08:13:22Z)
• Dimensions underlying the representational alignment of deep neural networks with humans [3.1668470116181817]
  We propose a generic framework to compare human and AI representations, based on identifying latent representational dimensions. Applying this framework to humans and a deep neural network (DNN) model of natural images revealed a low-dimensional DNN embedding of both visual and semantic dimensions. While in-silico experiments showed seemingly consistent interpretability of DNN dimensions, a direct comparison between human and DNN representations revealed substantial differences in how they process images.
  arXiv  Detail & Related papers  (2024-06-27T11:14:14Z)
• Learning Human-Aligned Representations with Contrastive Learning and Generative Similarity [9.63129238638334]
  Humans rely on effective representations to learn from few examples and abstract useful information from sensory data. We use a Bayesian notion of generative similarity whereby two data points are considered similar if they are likely to have been sampled from the same distribution. We demonstrate the utility of our approach by showing that it can be used to capture human-like representations of shape regularity, abstract Euclidean geometric concepts, and semantic hierarchies for natural images.
  arXiv  Detail & Related papers  (2024-05-29T18:01:58Z)
• You Only Learn One Query: Learning Unified Human Query for Single-Stage Multi-Person Multi-Task Human-Centric Perception [37.667147915777534]
  Human-centric perception is a long-standing problem for computer vision. This paper introduces a unified and versatile framework (HQNet) for single-stage multi-person multi-task human-centric perception (HCP) Human Query captures intricate instance-level features for individual persons and disentangles complex multi-person scenarios.
  arXiv  Detail & Related papers  (2023-12-09T10:36:43Z)
• Detecting Any Human-Object Interaction Relationship: Universal HOI Detector with Spatial Prompt Learning on Foundation Models [55.20626448358655]
  This study explores the universal interaction recognition in an open-world setting through the use of Vision-Language (VL) foundation models and large language models (LLMs) Our design includes an HO Prompt-guided Decoder (HOPD), facilitates the association of high-level relation representations in the foundation model with various HO pairs within the image. For open-category interaction recognition, our method supports either of two input types: interaction phrase or interpretive sentence.
  arXiv  Detail & Related papers  (2023-11-07T08:27:32Z)
• Multimodality and Attention Increase Alignment in Natural Language Prediction Between Humans and Computational Models [0.8139163264824348]
  Humans are known to use salient multimodal features, such as visual cues, to facilitate the processing of upcoming words. multimodal computational models can integrate visual and linguistic data using a visual attention mechanism to assign next-word probabilities. We show that predictability estimates from humans aligned more closely with scores generated from multimodal models vs. their unimodal counterparts.
  arXiv  Detail & Related papers  (2023-08-11T09:30:07Z)
• DIME: Fine-grained Interpretations of Multimodal Models via Disentangled Local Explanations [119.1953397679783]
  We focus on advancing the state-of-the-art in interpreting multimodal models. Our proposed approach, DIME, enables accurate and fine-grained analysis of multimodal models.
  arXiv  Detail & Related papers  (2022-03-03T20:52:47Z)
• Differentiable Multi-Granularity Human Representation Learning for Instance-Aware Human Semantic Parsing [131.97475877877608]
  A new bottom-up regime is proposed to learn category-level human semantic segmentation and multi-person pose estimation in a joint and end-to-end manner. It is a compact, efficient and powerful framework that exploits structural information over different human granularities. Experiments on three instance-aware human datasets show that our model outperforms other bottom-up alternatives with much more efficient inference.
  arXiv  Detail & Related papers  (2021-03-08T06:55:00Z)
• DRG: Dual Relation Graph for Human-Object Interaction Detection [65.50707710054141]
  We tackle the challenging problem of human-object interaction (HOI) detection. Existing methods either recognize the interaction of each human-object pair in isolation or perform joint inference based on complex appearance-based features. In this paper, we leverage an abstract spatial-semantic representation to describe each human-object pair and aggregate the contextual information of the scene via a dual relation graph.
  arXiv  Detail & Related papers  (2020-08-26T17:59:40Z)
• Joint Inference of States, Robot Knowledge, and Human (False-)Beliefs [90.20235972293801]
  Aiming to understand how human (false-temporal)-belief-a core socio-cognitive ability unify-would affect human interactions with robots, this paper proposes to adopt a graphical model to the representation of object states, robot knowledge, and human (false-)beliefs. An inference algorithm is derived to fuse individual pg from all robots across multi-views into a joint pg, which affords more effective reasoning inference capability to overcome the errors originated from a single view.
  arXiv  Detail & Related papers  (2020-04-25T23:02:04Z)

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.