DEAL: Disentangle and Localize Concept-level Explanations for VLMs

• URL: http://arxiv.org/abs/2407.14412v1
• Date: Fri, 19 Jul 2024 15:39:19 GMT
• Title: DEAL: Disentangle and Localize Concept-level Explanations for VLMs
• Authors: Tang Li, Mengmeng Ma, Xi Peng,
• Abstract summary: Large pre-trained Vision-Language Models might not be able to identify fine-grained concepts. We propose to DisEnt and Localize (Angle) concept-level explanations for concepts without human annotations. Our empirical results demonstrate that the proposed method significantly improves the concept-level explanations of the model in terms of disentanglability and localizability.
• Score: 10.397502254316645
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Large pre-trained Vision-Language Models (VLMs) have become ubiquitous foundational components of other models and downstream tasks. Although powerful, our empirical results reveal that such models might not be able to identify fine-grained concepts. Specifically, the explanations of VLMs with respect to fine-grained concepts are entangled and mislocalized. To address this issue, we propose to DisEntAngle and Localize (DEAL) the concept-level explanations for VLMs without human annotations. The key idea is encouraging the concept-level explanations to be distinct while maintaining consistency with category-level explanations. We conduct extensive experiments and ablation studies on a wide range of benchmark datasets and vision-language models. Our empirical results demonstrate that the proposed method significantly improves the concept-level explanations of the model in terms of disentanglability and localizability. Surprisingly, the improved explainability alleviates the model's reliance on spurious correlations, which further benefits the prediction accuracy.

Related papers

• Improve Vision Language Model Chain-of-thought Reasoning [86.83335752119741]
  Chain-of-thought (CoT) reasoning in vision language models (VLMs) is crucial for improving interpretability and trustworthiness. We show that training VLM on short answers does not generalize well to reasoning tasks that require more detailed responses.
  arXiv  Detail & Related papers  (2024-10-21T17:00:06Z)
• Evaluating Readability and Faithfulness of Concept-based Explanations [35.48852504832633]
  Concept-based explanations arise as a promising avenue for explaining high-level patterns learned by Large Language Models. Current methods approach concepts from different perspectives, lacking a unified formalization. This makes evaluating the core measures of concepts, namely faithfulness or readability, challenging.
  arXiv  Detail & Related papers  (2024-04-29T09:20:25Z)
• VALOR-EVAL: Holistic Coverage and Faithfulness Evaluation of Large Vision-Language Models [57.43276586087863]
  Large Vision-Language Models (LVLMs) suffer from hallucination issues, wherein the models generate plausible-sounding but factually incorrect outputs. Existing benchmarks are often limited in scope, focusing mainly on object hallucinations. We introduce a multi-dimensional benchmark covering objects, attributes, and relations, with challenging images selected based on associative biases.
  arXiv  Detail & Related papers  (2024-04-22T04:49:22Z)
• On the Tip of the Tongue: Analyzing Conceptual Representation in Large Language Models with Reverse-Dictionary Probe [36.65834065044746]
  We use in-context learning to guide the models to generate the term for an object concept implied in a linguistic description. Experiments suggest that conceptual inference ability as probed by the reverse-dictionary task predicts model's general reasoning performance.
  arXiv  Detail & Related papers  (2024-02-22T09:45:26Z)
• Explanation-aware Soft Ensemble Empowers Large Language Model In-context Learning [50.00090601424348]
  Large language models (LLMs) have shown remarkable capabilities in various natural language understanding tasks. We propose EASE, an Explanation-Aware Soft Ensemble framework to empower in-context learning with LLMs.
  arXiv  Detail & Related papers  (2023-11-13T06:13:38Z)
• Interpreting Pretrained Language Models via Concept Bottlenecks [55.47515772358389]
  Pretrained language models (PLMs) have made significant strides in various natural language processing tasks. The lack of interpretability due to their black-box'' nature poses challenges for responsible implementation. We propose a novel approach to interpreting PLMs by employing high-level, meaningful concepts that are easily understandable for humans.
  arXiv  Detail & Related papers  (2023-11-08T20:41:18Z)
• Explainability for Large Language Models: A Survey [59.67574757137078]
  Large language models (LLMs) have demonstrated impressive capabilities in natural language processing. This paper introduces a taxonomy of explainability techniques and provides a structured overview of methods for explaining Transformer-based language models.
  arXiv  Detail & Related papers  (2023-09-02T22:14:26Z)
• Evaluating and Explaining Large Language Models for Code Using Syntactic Structures [74.93762031957883]
  This paper introduces ASTxplainer, an explainability method specific to Large Language Models for code. At its core, ASTxplainer provides an automated method for aligning token predictions with AST nodes. We perform an empirical evaluation on 12 popular LLMs for code using a curated dataset of the most popular GitHub projects.
  arXiv  Detail & Related papers  (2023-08-07T18:50:57Z)
• Explaining Language Models' Predictions with High-Impact Concepts [11.47612457613113]
  We propose a complete framework for extending concept-based interpretability methods to NLP. We optimize for features whose existence causes the output predictions to change substantially. Our method achieves superior results on predictive impact, usability, and faithfulness compared to the baselines.
  arXiv  Detail & Related papers  (2023-05-03T14:48:27Z)
• Benchmarking Faithfulness: Towards Accurate Natural Language Explanations in Vision-Language Tasks [0.0]
  Natural language explanations (NLEs) promise to enable the communication of a model's decision-making in an easily intelligible way. While current models successfully generate convincing explanations, it is an open question how well the NLEs actually represent the reasoning process of the models. We propose three faithfulness metrics: Attribution-Similarity, NLE-Sufficiency, and NLE-Comprehensiveness.
  arXiv  Detail & Related papers  (2023-04-03T08:24:10Z)

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.