Detecting Multimodal Situations with Insufficient Context and Abstaining from Baseless Predictions

• URL: http://arxiv.org/abs/2405.11145v3
• Date: Sat, 25 May 2024 04:46:38 GMT
• Title: Detecting Multimodal Situations with Insufficient Context and Abstaining from Baseless Predictions
• Authors: Junzhang Liu, Zhecan Wang, Hammad Ayyubi, Haoxuan You, Chris Thomas, Rui Sun, Shih-Fu Chang, Kai-Wei Chang,
• Abstract summary: Vision-Language Understanding (VLU) benchmarks contain samples where answers rely on assumptions unsupported by the provided context. We collect contextual data for each sample whenever available and train a context selection module to facilitate evidence-based model predictions. We develop a general-purpose Context-AwaRe Abstention detector to identify samples lacking sufficient context and enhance model accuracy.
• Score: 75.45274978665684
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Despite the widespread adoption of Vision-Language Understanding (VLU) benchmarks such as VQA v2, OKVQA, A-OKVQA, GQA, VCR, SWAG, and VisualCOMET, our analysis reveals a pervasive issue affecting their integrity: these benchmarks contain samples where answers rely on assumptions unsupported by the provided context. Training models on such data foster biased learning and hallucinations as models tend to make similar unwarranted assumptions. To address this issue, we collect contextual data for each sample whenever available and train a context selection module to facilitate evidence-based model predictions. Strong improvements across multiple benchmarks demonstrate the effectiveness of our approach. Further, we develop a general-purpose Context-AwaRe Abstention (CARA) detector to identify samples lacking sufficient context and enhance model accuracy by abstaining from responding if the required context is absent. CARA exhibits generalization to new benchmarks it wasn't trained on, underscoring its utility for future VLU benchmarks in detecting or cleaning samples with inadequate context. Finally, we curate a Context Ambiguity and Sufficiency Evaluation (CASE) set to benchmark the performance of insufficient context detectors. Overall, our work represents a significant advancement in ensuring that vision-language models generate trustworthy and evidence-based outputs in complex real-world scenarios.

Related papers

• Context is Key: A Benchmark for Forecasting with Essential Textual Information [87.3175915185287]
  "Context is Key" (CiK) is a time series forecasting benchmark that pairs numerical data with diverse types of carefully crafted textual context. We evaluate a range of approaches, including statistical models, time series foundation models, and LLM-based forecasters. Our experiments highlight the importance of incorporating contextual information, demonstrate surprising performance when using LLM-based forecasting models, and also reveal some of their critical shortcomings.
  arXiv  Detail & Related papers  (2024-10-24T17:56:08Z)
• Uncertainty Aware Learning for Language Model Alignment [97.36361196793929]
  We propose uncertainty-aware learning (UAL) to improve the model alignment of different task scenarios. We implement UAL in a simple fashion -- adaptively setting the label smoothing value of training according to the uncertainty of individual samples. Experiments on widely used benchmarks demonstrate that our UAL significantly and consistently outperforms standard supervised fine-tuning.
  arXiv  Detail & Related papers  (2024-06-07T11:37:45Z)
• Multi-Modal Prompt Learning on Blind Image Quality Assessment [65.0676908930946]
  Image Quality Assessment (IQA) models benefit significantly from semantic information, which allows them to treat different types of objects distinctly. Traditional methods, hindered by a lack of sufficiently annotated data, have employed the CLIP image-text pretraining model as their backbone to gain semantic awareness. Recent approaches have attempted to address this mismatch using prompt technology, but these solutions have shortcomings. This paper introduces an innovative multi-modal prompt-based methodology for IQA.
  arXiv  Detail & Related papers  (2024-04-23T11:45:32Z)
• 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)
• In-context Prompt Learning for Test-time Vision Recognition with Frozen Vision-language Model [13.983810804606264]
  We propose In-Context Prompt Learning (InCPL) for test-time visual recognition tasks. InCPL associates a new test sample with very few labeled examples as context information. We introduce a context-aware unsupervised loss to optimize visual prompts tailored to test samples.
  arXiv  Detail & Related papers  (2024-03-10T08:15:51Z)
• BloomVQA: Assessing Hierarchical Multi-modal Comprehension [18.21961616174999]
  We collect multiple-choice samples based on picture stories that reflect different levels of comprehension. Our data maps to a novel hierarchical graph representation which enables automatic data augmentation and novel measures characterizing model consistency. In comparison to earlier models, GPT-4V demonstrates improved accuracy over all comprehension levels and shows a tendency of bypassing visual inputs especially for higher-level tasks.
  arXiv  Detail & Related papers  (2023-12-20T02:22:49Z)
• Preserving Knowledge Invariance: Rethinking Robustness Evaluation of Open Information Extraction [50.62245481416744]
  We present the first benchmark that simulates the evaluation of open information extraction models in the real world. We design and annotate a large-scale testbed in which each example is a knowledge-invariant clique. By further elaborating the robustness metric, a model is judged to be robust if its performance is consistently accurate on the overall cliques.
  arXiv  Detail & Related papers  (2023-05-23T12:05:09Z)
• Can NLP Models Correctly Reason Over Contexts that Break the Common Assumptions? [14.991565484636745]
  We investigate the ability of NLP models to correctly reason over contexts that break the common assumptions. We show that while doing fairly well on contexts that follow the common assumptions, the models struggle to correctly reason over contexts that break those assumptions. Specifically, the performance gap is as high as 20% absolute points.
  arXiv  Detail & Related papers  (2023-05-20T05:20:37Z)
• Knowledge-Based Counterfactual Queries for Visual Question Answering [0.0]
  We propose a systematic method for explaining the behavior and investigating the robustness of VQA models through counterfactual perturbations. For this reason, we exploit structured knowledge bases to perform deterministic, optimal and controllable word-level replacements targeting the linguistic modality. We then evaluate the model's response against such counterfactual inputs.
  arXiv  Detail & Related papers  (2023-03-05T08:00:30Z)
• Reassessing Evaluation Practices in Visual Question Answering: A Case Study on Out-of-Distribution Generalization [27.437077941786768]
  Vision-and-language (V&L) models pretrained on large-scale multimodal data have demonstrated strong performance on various tasks. We evaluate two pretrained V&L models under different settings by conducting cross-dataset evaluations. We find that these models tend to learn to solve the benchmark, rather than learning the high-level skills required by the VQA task.
  arXiv  Detail & Related papers  (2022-05-24T16:44:45Z)

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.