Team Triple-Check at Factify 2: Parameter-Efficient Large Foundation Models with Feature Representations for Multi-Modal Fact Verification

• URL: http://arxiv.org/abs/2302.07740v1
• Date: Sun, 12 Feb 2023 18:08:54 GMT
• Title: Team Triple-Check at Factify 2: Parameter-Efficient Large Foundation Models with Feature Representations for Multi-Modal Fact Verification
• Authors: Wei-Wei Du, Hong-Wei Wu, Wei-Yao Wang, Wen-Chih Peng
• Abstract summary: Multi-modal fact verification has become an important but challenging issue on social media. In this paper, we propose the Pre-CoFactv2 framework for modeling fine-grained text and input embeddings with lightening parameters. We show that Pre-CoFactv2 outperforms Pre-CoFact by a large margin and achieved new state-of-the-art results at the Factify challenge at AAAI 2023.
• Score: 5.552606716659022
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Multi-modal fact verification has become an important but challenging issue on social media due to the mismatch between the text and images in the misinformation of news content, which has been addressed by considering cross-modalities to identify the veracity of the news in recent years. In this paper, we propose the Pre-CoFactv2 framework with new parameter-efficient foundation models for modeling fine-grained text and input embeddings with lightening parameters, multi-modal multi-type fusion for not only capturing relations for the same and different modalities but also for different types (i.e., claim and document), and feature representations for explicitly providing metadata for each sample. In addition, we introduce a unified ensemble method to boost model performance by adjusting the importance of each trained model with not only the weights but also the powers. Extensive experiments show that Pre-CoFactv2 outperforms Pre-CoFact by a large margin and achieved new state-of-the-art results at the Factify challenge at AAAI 2023. We further illustrate model variations to verify the relative contributions of different components. Our team won the first prize (F1-score: 81.82%) and we made our code publicly available at https://github.com/wwweiwei/Pre-CoFactv2-AAAI-2023.

Related papers

• EMR-Merging: Tuning-Free High-Performance Model Merging [55.03509900949149]
  We show that Elect, Mask & Rescale-Merging (EMR-Merging) shows outstanding performance compared to existing merging methods. EMR-Merging is tuning-free, thus requiring no data availability or any additional training while showing impressive performance.
  arXiv  Detail & Related papers  (2024-05-23T05:25:45Z)
• Dissecting Multimodality in VideoQA Transformer Models by Impairing Modality Fusion [54.33764537135906]
  VideoQA Transformer models demonstrate competitive performance on standard benchmarks. Do these models capture the rich multimodal structures and dynamics from video and text jointly? Are they achieving high scores by exploiting biases and spurious features?
  arXiv  Detail & Related papers  (2023-06-15T06:45:46Z)
• 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)
• An Empirical Study of Multimodal Model Merging [148.48412442848795]
  Model merging is a technique that fuses multiple models trained on different tasks to generate a multi-task solution. We conduct our study for a novel goal where we can merge vision, language, and cross-modal transformers of a modality-specific architecture. We propose two metrics that assess the distance between weights to be merged and can serve as an indicator of the merging outcomes.
  arXiv  Detail & Related papers  (2023-04-28T15:43:21Z)
• Model ensemble instead of prompt fusion: a sample-specific knowledge transfer method for few-shot prompt tuning [85.55727213502402]
  We focus on improving the few-shot performance of prompt tuning by transferring knowledge from soft prompts of source tasks. We propose Sample-specific Ensemble of Source Models (SESoM) SESoM learns to adjust the contribution of each source model for each target sample separately when ensembling source model outputs.
  arXiv  Detail & Related papers  (2022-10-23T01:33:16Z)
• Long Document Summarization with Top-down and Bottom-up Inference [113.29319668246407]
  We propose a principled inference framework to improve summarization models on two aspects. Our framework assumes a hierarchical latent structure of a document where the top-level captures the long range dependency. We demonstrate the effectiveness of the proposed framework on a diverse set of summarization datasets.
  arXiv  Detail & Related papers  (2022-03-15T01:24:51Z)
• Team Yao at Factify 2022: Utilizing Pre-trained Models and Co-attention Networks for Multi-Modal Fact Verification [7.3724108865167945]
  We propose a framework, Pre-CoFact, composed of two pre-trained models for extracting features from text and images. We adopt the ensemble method by using different pre-trained models in Pre-CoFact to achieve better performance. Our model achieved competitive performance without using auxiliary tasks or extra information.
  arXiv  Detail & Related papers  (2022-01-26T16:04:37Z)
• Logically at the Factify 2022: Multimodal Fact Verification [2.8914815569249823]
  This paper describes our participant system for the multi-modal fact verification (Factify) challenge at AAAI 2022. Two baseline approaches are proposed and explored including an ensemble model and a multi-modal attention network. Our best model is ranked first in leaderboard which obtains a weighted average F-measure of 0.77 on both validation and test set.
  arXiv  Detail & Related papers  (2021-12-16T23:34:07Z)

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.