activity
20162024
most citedExploring the Reasoning Abilities of Multimodal Large Language Models (MLLMs): A Comprehensive Survey on Emerging Trends in Multimodal Reasoning

20 citations · 25 across the 5 of their papers we have counts for

collaborators

5 papers

cs.CL202420 cited

Exploring the Reasoning Abilities of Multimodal Large Language Models (MLLMs): A Comprehensive Survey on Emerging Trends in Multimodal Reasoning

Yiqi Wang, Wentao Chen, Xiaotian Han +7

Strong Artificial Intelligence (Strong AI) or Artificial General Intelligence (AGI) with abstract reasoning ability is the goal of next-generation AI. Recent advancements in Large…

cs.CL2024

InfiAgent-DABench: Evaluating Agents on Data Analysis Tasks

Xueyu Hu, Ziyu Zhao, Shuang Wei +14

In this paper, we introduce InfiAgent-DABench, the first benchmark specifically designed to evaluate LLM-based agents on data analysis tasks. These tasks require agents to end-to-e…

cs.CL2023

Let's reward step by step: Step-Level reward model as the Navigators for Reasoning

Qianli Ma, Haotian Zhou, Tingkai Liu +4

Recent years have seen considerable advancements in multi-step reasoning with Large Language Models (LLMs). The previous studies have elucidated the merits of integrating feedback…

cs.IR20165 cited

Solving Cold-Start Problem in Large-scale Recommendation Engines: A Deep Learning Approach

Jianbo Yuan, Walid Shalaby, Mohammed Korayem +3

Collaborative Filtering (CF) is widely used in large-scale recommendation engines because of its efficiency, accuracy and scalability. However, in practice, the fact that recommend…

cs.SI2016

The Effect of Pets on Happiness: A Data-Driven Approach via Large-Scale Social Media

Yuchen Wu, Jianbo Yuan, Quanzeng You +1

Psychologists have demonstrated that pets have a positive impact on owners' happiness. For example, lonely people are often advised to have a dog or cat to quell their social isola…