4 papers
LaME: Learning to Think in Latent Space for Multimodal Embedding via Information Bottleneck
Peixi Wu, Biao Yang, Feipeng Ma +7
The paper introduces LaME, a multimodal embedding model that performs reasoning in a compact latent space using learnable tokens and an information‑bottleneck objective, eliminatin…
Structure-BiEval: A Self-Supervised, Dual-Track Framework for Decoupling Structure and Content in LLM Evaluation for Web Information Systems
Boxiang Zhao, Qince Li, Zhonghao Wang +4
As Large Language Models (LLMs) evolve into the core of Web-based autonomous agents and complex Web Information Systems, their ability to faithfully translate natural language into…
Bridging the Arithmetic Gap: The Cognitive Complexity Benchmark and Financial-PoT for Robust Financial Reasoning
Boxiang Zhao, Qince Li, Zhonghao Wang +3
While Large Language Models excel at semantic tasks, they face a critical bottleneck in financial quantitative reasoning, frequently suffering from "Arithmetic Hallucinations" and…
Clustering Algorithms and RAG Enhancing Semi-Supervised Text Classification with Large LLMs
Shan Zhong, Jiahao Zeng, Yongxin Yu +1
This paper proposes a Clustering, Labeling, then Augmenting framework that significantly enhances performance in Semi-Supervised Text Classification (SSTC) tasks, effectively addre…