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Tao Sheng

ACL 2026×3检索与知识推理评测与基准效率与系统多模态
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0 一作 · 3 篇 · ACL 2026

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2026 顶会论文

3
作者位次 #8/11ACL 2026Poster· Natural Language Processing

LAD - RAG : Layout-aware Dynamic RAG for Visually-Rich Document Understanding

Zhivar Sourati · Zheng Wang · Marianne Menglin Liu · Yazhe Hu · Mengqing Guo · Sujeeth Bharadwaj · Kyu J. Han · Tao Sheng · Sujith Ravi · Morteza Dehghani · Dan Roth

摘要

Question answering over visually rich documents (VRDs) requires reasoning not only over isolated content but also over documents’ structural organization and cross-page dependencies. However, conventional retrieval-augmented generation (RAG) methods encode content in isolated chunks during ingest...

检索与知识效率与系统推理OpenReview 项目页
作者位次 #6/8ACL 2026Poster· ACL Industry Track

PAR ^2 - RAG : Planned Active Retrieval and Reasoning for Multi-Hop Question Answering

Xingyu Li · Rongguang Wang · Yuying Wang · Mengqing Guo · Chenyang Li · Tao Sheng · Sujith Ravi · Dan Roth

摘要

Multi-hop question answering (MHQA) is a practical bottleneck in industry applications such as enterprise assistants, customer-support copilots, and compliance analysis, where systems must combine evidence across multiple documents before answering. Large language models (LLMs) remain brittle in...

检索与知识推理评测与基准OpenReview 项目页
作者位次 #10/12ACL 2026Findings· Findings of ACL

Do Image–Text Metrics Respect Semantic Invariances?

Amit Agarwal · Hitesh Laxmichand Patel · Meizhu Liu · Jyotika Singh · Karan Dua · Hansa Meghwani · Matthew Rowe · M. Avendi · Yassi Abbasi · Tao Sheng · Sujith Ravi · Dan Roth

摘要

Reference-free image–to–text evaluators are now standard for scoring image–caption alignment, yet it is unclear whether they respect semantic invariances. We present an invariance probe on five popular evaluators (CLIPScore, PAC-S, UMIC, FLEUR, and a deterministic LLM judge) under semantics-prese...

多模态评测与基准社会与人本OpenReview 项目页

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