11 citations · 27 across the 13 of their papers we have counts for
9 papers · 1 filter
Multi-refined Feature Enhanced Sentiment Analysis Using Contextual Instruction
Peter Atandoh, Jie Zou, Weikang Guo +2
Sentiment analysis using deep learning and pre-trained language models (PLMs) has gained significant traction due to their ability to capture rich contextual representations. Howev…
LAD-RAG: Layout-aware Dynamic RAG for Visually-Rich Document Understanding
Zhivar Sourati, Zheng Wang, Marianne Menglin Liu +8
Question answering over visually rich documents (VRDs) requires reasoning not only over isolated content but also over documents' structural organization and cross-page dependencie…
OTTER: Open-Tagging via Text-Image Representation for Multi-modal Understanding
Jieer Ouyang, Xiaoneng Xiang, Zheng Wang +1
We introduce OTTER, a unified open-set multi-label tagging framework that harmonizes the stability of a curated, predefined category set with the adaptability of user-driven open t…
Detecting Corpus-Level Knowledge Inconsistencies in Wikipedia with Large Language Models
Sina J. Semnani, Jirayu Burapacheep, Arpandeep Khatua +3
Wikipedia is the largest open knowledge corpus, widely used worldwide and serving as a key resource for training large language models (LLMs) and retrieval-augmented generation (RA…
BlueLM-2.5-3B Technical Report
Baojiao Xiong, Boheng Chen, Chengzhi Wang +58
We present BlueLM-2.5-3B, a compact and unified dense Multimodal Large Language Model (MLLM) designed for efficient edge-device deployment, offering strong general-purpose and reas…
AxBench: Steering LLMs? Even Simple Baselines Outperform Sparse Autoencoders
Zhengxuan Wu, Aryaman Arora, Atticus Geiger +5
Fine-grained steering of language model outputs is essential for safety and reliability. Prompting and finetuning are widely used to achieve these goals, but interpretability resea…