paper-with-me

홈 › Papers

A Picture Is Worth a Graph: A Blueprint Debate Paradigm for Multimodal Reasoning

2024-03-22 · Changmeng Zheng, Dayong Liang, WengYu Zhang, Xiao-Yong Wei, Tat-Seng Chua, Qing Li

This paper presents a pilot study aimed at introducing multi-agent debate into multimodal reasoning. The study addresses two key challenges: the trivialization of opinions resulting from excessive summarization and the diversion of focus caused by distractor concepts introduced from images. These challenges stem from the inductive (bottom-up) nature of existing debating schemes. To address the issue, we propose a deductive (top-down) debating approach called Blueprint Debate on Graphs (BDoG). In BDoG, debates are confined to a blueprint graph to prevent opinion trivialization through world-level summarization. Moreover, by storing evidence in branches within the graph, BDoG mitigates distractions caused by frequent but irrelevant concepts. Extensive experiments validate that BDoG is able to achieve state-of-the-art results in ScienceQA and MMBench with significant improvements over previous methods. The source code can be accessed at https://github.com/thecharm/BDoG.

📄 PDF Abstract BibTeX arXiv:2403.14972

Code (1)

thecharm/bdog 공식 구현

Tasks

Multimodal Reasoning

Methods 이 논문이 사용한 방법론

Focus 설명 없음

Similar Papers 제목 키워드 기반

What did we learn from forty years of research on semantic interference? A Bayesian metaanalysis

2020-04-26

When participants in an experiment have to name pictures while ignoring distractor words superimposed on the picture or presented auditorily (i.e., picture-word interference paradigm), they take more time when the word t…

CortexDebate: Debating Sparsely and Equally for Multi-Agent Debate

2025-07-05 · Yiliu Sun, Zicheng Zhao, Sheng Wan, Chen Gong arxiv

Nowadays, single Large Language Model (LLM) struggles with critical issues such as hallucination and inadequate reasoning abilities. To mitigate these issues, Multi-Agent Debate (MAD) has emerged as an effective strategy…

It Takes Nine to Smell a Rat: Neural Multi-Task Learning for Check-Worthiness Prediction

2019-08-19 · RANLP 2019 9 · Slavena Vasileva, Pepa Atanasova, Lluís Màrquez, Alberto Barrón-Cedeño 외

We propose a multi-task deep-learning approach for estimating the check-worthiness of claims in political debates. Given a political debate, such as the 2016 US Presidential and Vice-Presidential ones, the task is to pre…

Fact CheckingMulti-Task Learning

Detecting Check-Worthy Claims in Political Debates, Speeches, and Interviews Using Audio Data

2023-05-24 · Petar Ivanov, Ivan Koychev, Momchil Hardalov, Preslav Nakov

Developing tools to automatically detect check-worthy claims in political debates and speeches can greatly help moderators of debates, journalists, and fact-checkers. While previous work on this problem has focused exclu…

Fact CheckingMisinformation

Towards AI Search Paradigm

2025-06-20 · Yuchen Li, Hengyi Cai, Rui Kong, Xinran Chen 외

In this paper, we introduce the AI Search Paradigm, a comprehensive blueprint for next-generation search systems capable of emulating human information processing and decision-making. The paradigm employs a modular archi…

Decision MakingRetrieval-augmented GenerationTask Planning