28 papers
Position: The Real Barrier to LLM Agent Usability is Agentic ROI
Weiwen Liu, Jiarui Qin, Xu Huang +10
Large Language Model (LLM) agents represent a promising shift in human-AI interaction, moving beyond passive prompt-response systems to autonomous agents capable of reasoning, plan…
ToolACE-R: Model-aware Iterative Training and Adaptive Refinement for Tool Learning
Xingshan Zeng, Weiwen Liu, Xu Huang +8
Tool learning, which allows Large Language Models (LLMs) to leverage external tools for solving complex user tasks, has emerged as a promising avenue for extending model capabiliti…
AdvKT: An Adversarial Multi-Step Training Framework for Knowledge Tracing
Lingyue Fu, Ting Long, Jianghao Lin +6
Knowledge Tracing (KT) monitors students' knowledge states and simulates their responses to question sequences. Existing KT models typically follow a single-step training paradigm,…
Benchmarking Retrieval-Augmented Multimodal Generation for Document Question Answering
Kuicai Dong, Yujing Chang, Shijie Huang +3
Document Visual Question Answering (DocVQA) faces dual challenges in processing lengthy multimodal documents (text, images, tables) and performing cross-modal reasoning. Current do…
MMDocIR: Benchmarking Multimodal Retrieval for Long Documents
Kuicai Dong, Yujing Chang, Xin Deik Goh +3
Multimodal document retrieval aims to identify and retrieve various forms of multimodal content, such as figures, tables, charts, and layout information from extensive documents. D…
NL-Debugging: Exploiting Natural Language as an Intermediate Representation for Code Debugging
Weiming Zhang, Qingyao Li, Xinyi Dai +7
Debugging is a critical aspect of LLM's coding ability. Early debugging efforts primarily focused on code-level analysis, which often falls short when addressing complex programmin…