Predicting Human Similarity Judgments Using Large Language Models

• URL: http://arxiv.org/abs/2202.04728v1
• Date: Wed, 9 Feb 2022 21:09:25 GMT
• Title: Predicting Human Similarity Judgments Using Large Language Models
• Authors: Raja Marjieh, Ilia Sucholutsky, Theodore R. Sumers, Nori Jacoby, Thomas L. Griffiths
• Abstract summary: We propose an efficient procedure for predicting similarity judgments based on text descriptions. The number of descriptions required grows only linearly with the number of stimuli, drastically reducing the amount of data required. We test this procedure on six datasets of naturalistic images and show that our models outperform previous approaches based on visual information.
• Score: 13.33450619901885
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Similarity judgments provide a well-established method for accessing mental representations, with applications in psychology, neuroscience and machine learning. However, collecting similarity judgments can be prohibitively expensive for naturalistic datasets as the number of comparisons grows quadratically in the number of stimuli. One way to tackle this problem is to construct approximation procedures that rely on more accessible proxies for predicting similarity. Here we leverage recent advances in language models and online recruitment, proposing an efficient domain-general procedure for predicting human similarity judgments based on text descriptions. Intuitively, similar stimuli are likely to evoke similar descriptions, allowing us to use description similarity to predict pairwise similarity judgments. Crucially, the number of descriptions required grows only linearly with the number of stimuli, drastically reducing the amount of data required. We test this procedure on six datasets of naturalistic images and show that our models outperform previous approaches based on visual information.

Related papers

• Using Contrastive Learning with Generative Similarity to Learn Spaces that Capture Human Inductive Biases [9.63129238638334]
  Humans rely on strong inductive biases to learn from few examples and abstract useful information from sensory data. We introduce a notion of generative similarity whereby two datapoints are considered similar if they are likely to have been sampled from the same distribution. We show that generative similarity can be used to define a contrastive learning objective even when its exact form is intractable.
  arXiv  Detail & Related papers  (2024-05-29T18:01:58Z)
• This Reads Like That: Deep Learning for Interpretable Natural Language Processing [9.002523763052848]
  Prototype learning is a popular machine learning method designed for inherently interpretable decisions. We introduce a learned weighted similarity measure that enhances the similarity by focusing on informative dimensions of pre-trained sentence embeddings. We propose a post-hoc explainability mechanism that extracts prediction-relevant words from both the prototype and input sentences.
  arXiv  Detail & Related papers  (2023-10-25T21:18:35Z)
• MAUVE Scores for Generative Models: Theory and Practice [95.86006777961182]
  We present MAUVE, a family of comparison measures between pairs of distributions such as those encountered in the generative modeling of text or images. We find that MAUVE can quantify the gaps between the distributions of human-written text and those of modern neural language models. We demonstrate in the vision domain that MAUVE can identify known properties of generated images on par with or better than existing metrics.
  arXiv  Detail & Related papers  (2022-12-30T07:37:40Z)
• Counting Like Human: Anthropoid Crowd Counting on Modeling the Similarity of Objects [92.80955339180119]
  mainstream crowd counting methods regress density map and integrate it to obtain counting results. Inspired by this, we propose a rational and anthropoid crowd counting framework.
  arXiv  Detail & Related papers  (2022-12-02T07:00:53Z)
• Similarity between Units of Natural Language: The Transition from Coarse to Fine Estimation [0.0]
  Capturing the similarities between human language units is crucial for explaining how humans associate different objects. My research goal in this thesis is to develop regression models that account for similarities between language units in a more refined way.
  arXiv  Detail & Related papers  (2022-10-25T18:54:32Z)
• MINIMALIST: Mutual INformatIon Maximization for Amortized Likelihood Inference from Sampled Trajectories [61.3299263929289]
  Simulation-based inference enables learning the parameters of a model even when its likelihood cannot be computed in practice. One class of methods uses data simulated with different parameters to infer an amortized estimator for the likelihood-to-evidence ratio. We show that this approach can be formulated in terms of mutual information between model parameters and simulated data.
  arXiv  Detail & Related papers  (2021-06-03T12:59:16Z)
• Toward Scalable and Unified Example-based Explanation and Outlier Detection [128.23117182137418]
  We argue for a broader adoption of prototype-based student networks capable of providing an example-based explanation for their prediction. We show that our prototype-based networks beyond similarity kernels deliver meaningful explanations and promising outlier detection results without compromising classification accuracy.
  arXiv  Detail & Related papers  (2020-11-11T05:58:17Z)
• Action similarity judgment based on kinematic primitives [48.99831733355487]
  We investigate to which extent a computational model based on kinematics can determine action similarity. The chosen model has its roots in developmental robotics and performs action classification based on learned kinematic primitives. The results show that both the model and human performance are highly accurate in an action similarity task based on kinematic-level features.
  arXiv  Detail & Related papers  (2020-08-30T13:58:47Z)
• Few-shot Visual Reasoning with Meta-analogical Contrastive Learning [141.2562447971]
  We propose to solve a few-shot (or low-shot) visual reasoning problem, by resorting to analogical reasoning. We extract structural relationships between elements in both domains, and enforce them to be as similar as possible with analogical learning. We validate our method on RAVEN dataset, on which it outperforms state-of-the-art method, with larger gains when the training data is scarce.
  arXiv  Detail & Related papers  (2020-07-23T14:00:34Z)
• Building and Interpreting Deep Similarity Models [0.0]
  We propose to make similarities interpretable by augmenting them with an explanation in terms of input features. We develop BiLRP, a scalable and theoretically founded method to systematically decompose similarity scores on pairs of input features.
  arXiv  Detail & Related papers  (2020-03-11T17:46:55Z)
• Adaptive Discrete Smoothing for High-Dimensional and Nonlinear Panel Data [4.550919471480445]
  We develop a data-driven smoothing technique for high-dimensional and non-linear panel data models. The weights are determined by a data-driven way and depend on the similarity between the corresponding functions. We conduct a simulation study which shows that the prediction can be greatly improved by using our estimator.
  arXiv  Detail & Related papers  (2019-12-30T09:50:58Z)

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.