1 citations · 2 across the 16 of their papers we have counts for
7 papers · 1 filter
Augmenting Intra-Modal Understanding in MLLMs for Robust Multimodal Keyphrase Generation
Jiajun Cao, Qinggang Zhang, Yunbo Tang +3
Multimodal keyphrase generation (MKP) aims to extract a concise set of keyphrases that capture the essential meaning of paired image-text inputs, enabling structured understanding,…
LAG: Logic-Augmented Generation from a Cartesian Perspective
Yilin Xiao, Chuang Zhou, Yujing Zhang +5
Large language models (LLMs) have demonstrated remarkable capabilities across a wide range of tasks, yet exhibit critical limitations in knowledge-intensive tasks, often generating…
Why Regression? Binary Encoding Classification Brings Confidence to Stock Market Index Price Prediction
Junzhe Jiang, Chang Yang, Xinrun Wang +1
Stock market indices serve as fundamental market measurement that quantify systematic market dynamics. However, accurate index price prediction remains challenging, primarily becau…
FinMaster: A Holistic Benchmark for Mastering Full-Pipeline Financial Workflows with LLMs
Junzhe Jiang, Chang Yang, Aixin Cui +6
Financial tasks are pivotal to global economic stability; however, their execution faces challenges including labor intensive processes, low error tolerance, data fragmentation, an…
Resolving Latency and Inventory Risk in Market Making with Reinforcement Learning
Junzhe Jiang, Chang Yang, Xinrun Wang +3
The latency of the exchanges in Market Making (MM) is inevitable due to hardware limitations, system processing times, delays in receiving data from exchanges, the time required fo…
Nondeterministic Polynomial-time Problem Challenge: An Ever-Scaling Reasoning Benchmark for LLMs
Chang Yang, Ruiyu Wang, Junzhe Jiang +9
Reasoning is the fundamental capability of large language models (LLMs). Due to the rapid progress of LLMs, there are two main issues of current benchmarks: i) these benchmarks can…
Each Graph is a New Language: Graph Learning with LLMs
Huachi Zhou, Jiahe Du, Chuang Zhou +4
Recent efforts leverage Large Language Models (LLMs) for modeling text-attributed graph structures in node classification tasks. These approaches describe graph structures for LLMs…