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Haobo Wang

ACL 2026×4AAAI 2026×3KDD 2026×3ICLR 2026×2推理检索与知识强化学习与后训练对齐与偏好多模态
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0 一作 · 12 篇 · ACL 2026 / AAAI 2026 / KDD 2026 / ICLR 2026

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2026 顶会论文

12
作者位次 #4/7ACL 2026Poster· Natural Language Processing

Everyone is unique: Towards Behaviorally Heterogeneous Negotiation Dialogue Systems for Debt Collection

Yuhang Yang · Kai Tang · Chao Ye · Haobo Wang · Qiqi Luo · Jin Guang Zheng · Zhixin Zhang

摘要

Debt collection is a critical negotiation task in the financial industry, with strong practical relevance and exceptional academic value as a behaviorally rich, high-stakes testbed for human-centered dialogue systems. While large language models (LLMs) have shown promise in dialogue and negotiati...

评测与基准智能体与工具对齐与偏好OpenReview 项目页
作者位次 #7/10ACL 2026Poster· Natural Language Processing

Towards Interpretable Tabular Reasoning: Enhancing LLM Reasoning on Tabular Data with Pre-Constructed Logic Graph

Lirong Gao · Zewei Yu · Zhongrui Yin · Qi Zhang · Yuke Zhu · Bo Zheng (Taobao &amp · Haobo Wang · Junbo Zhao · Gang Chen · Sheng Guo

摘要

Tabular data is widely used in fields such as finance and healthcare. Traditional tree-based models are prevalent for tabular prediction tasks due to their ability to handle heterogeneous features. However, their heavy reliance on feature engineering limits both their generalizability and their h...

推理社会与人本可解释性OpenReview 项目页
作者位次 #8/13ACL 2026Findings· Findings of ACL

F in MRAGB ench: A Realistic and Complex Benchmark for Multi-Modal RAG in Financial Document Analysis

Shouqing Yang · Qi Zhang · Yuhang Yang · Ruikang Xu · Yuwei Hou · Zhulin Jia · Lirong Gao · Haobo Wang · Jinglei Chen · Jiexiang Wang · Sheng Guo · Bo Zheng (Taobao &amp · Gang Chen

摘要

Retrieval-augmented generation (RAG) has become a widely adopted paradigm for realistic financial analysis over financial documents. However, existing benchmarks fail to capture realistic financial analysis settings that involve cross-document retrieval, multi-page evidence integration, and diver...

检索与知识推理智能体与工具OpenReview 项目页
作者位次 #8/9ACL 2026Findings· Findings of ACL

Fine-Grained Data Ordering Improves Fine-Tuning for Large Language Models

Xiaomeng Hu · Yixuan Tang · Haoze Li · Hao Chen · Qi Zhang · Zhanming Shen · Yiming Zhang · Haobo Wang · Junbo Zhao

摘要

With the rapid progress of large language models (LLMs), aligning a general-purpose model with downstream tasks through fine-tuning has become a central research focus. Selecting only high-quality examples for training has been shown to be one of the most effective ways to improve fine-tuning per...

对齐与偏好强化学习与后训练数据与预训练OpenReview 项目页

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