Papers by Tao Sheng
RCI: A Score for Evaluating Global and Local Reasoning in Multimodal Benchmarks (2025.emnlp-industry)
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Amit Agarwal, Hitesh Laxmichand Patel, Srikant Panda, Hansa Meghwani, Jyotika Singh, Karan Dua, Paul Li, Tao Sheng, Sujith Ravi, Dan Roth
| Challenge: | Existing evaluation methods do not explicitly measure this distinction, hindering effective dataset curation and real-world focused model development. |
| Approach: | They introduce a region-based score to quantify a dataset's reliance on global versus local visual information. |
| Outcome: | The proposed model-based score systematically compares model performance on image patches versus full images to determine if tasks require holistic image understanding or can be solved with partial or localized visual cues. |
Aligning LLMs for Multilingual Consistency in Enterprise Applications (2025.emnlp-industry)
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| Challenge: | Large language models (LLMs) remain unreliable for global enterprise applications due to performance gaps between high-resource and mid/low-resourced languages . |
| Approach: | They propose a batch-wise alignment strategy that aligns model outputs across languages . this method improves non-English accuracy by up to 23.9% without compromising English performance . |
| Outcome: | The proposed approach improves non-English accuracy by up to 23.9% without compromising English performance, model reasoning, or retrieval quality. |
PCRI: Measuring Context Robustness in Multimodal Models for Enterprise Applications (2025.emnlp-industry)
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Hitesh Laxmichand Patel, Amit Agarwal, Srikant Panda, Hansa Meghwani, Karan Dua, Paul Li, Tao Sheng, Sujith Ravi, Dan Roth
| Challenge: | Existing evaluation metrics for Multimodal Large Language Models (MLLMs) are inadequate to assess their robustness to irrelevant or distracting visual context. |
| Approach: | They propose a patch-context-robustness index to measure MLLMs' robustness to visual context variations. |
| Outcome: | The proposed score measures the robustness of MLLMs to visual contexts across 15 vision-language benchmarks. |
LAD-RAG: Layout-aware Dynamic RAG for Visually-Rich Document Understanding (2026.acl-long)
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Zhivar Sourati, Zheng Wang, Marianne Menglin Liu, Yazhe Hu, Mengqing Guo, Sujeeth Bharadwaj, Kyu J. Han, Tao Sheng, Sujith Ravi, Morteza Dehghani, Dan Roth
| Challenge: | Conventional retrieval-augmented generation (RAG) methods encode content in isolated chunks during ingestion, losing structural and cross-page dependencies, and retrieve a fixed number of pages at inference. |
| Approach: | They propose a Layout-Aware Dynamic RAG framework that encodes content in isolated chunks during ingestion and retrieves a fixed number of pages at inference. |
| Outcome: | Experiments on MMLongBench-Doc, LongDocURL, DUDE, and MP-DoxVQA show that LAD-RAG improves retrieval, achieving over 90% perfect recall on average without any top-k tuning, and outperforming baseline retrievers by up to 20% in recall at comparable noise levels. |
PAR2-RAG: Planned Active Retrieval and Reasoning for Multi-Hop Question Answering (2026.acl-industry)
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| Challenge: | Multi-hop question answering is a practical bottleneck in industry applications . large language models (LLMs) fail frequently when evidence coverage is incomplete or reasoning trajectories drift . |
| Approach: | They propose a training-free two-stage framework that separates coverage from commitment . it performs breadth-first anchoring to build a high-recall evidence frontier . compared with IRCoT, it achieves 23.5% higher answer accuracy . |
| Outcome: | The proposed framework outperforms strong baselines in MHQA benchmarks and achieves 23.5% higher answer accuracy and 10.5% NDCG gains in retrieval quality. |
Do Image–Text Metrics Respect Semantic Invariances? (2026.findings-acl)
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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
| Challenge: | Reference-free image–to–text evaluators are now standard for scoring image–caption alignment, yet it is unclear whether they respect semantic invariances. |
| Approach: | They propose an invariance probe on five popular evaluators under semantics-preserving perturbations along three axes: spatial edits, object changes, and socio-linguistic framing. |
| Outcome: | The proposed invariance probe shows that spatial edits and simple phrasing changes shift scores by ()6% on average and cause ranking flips in up to (),37% of cases. |
No Label? No Problem: Unsupervised Continual Learning for Adaptive Medical ASR (2026.eacl-industry)
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| Challenge: | Medical audio often contains specialized terminology, such as medication names, which existing ASR systems struggle to transcribe accurately. |
| Approach: | They propose an unsupervised continual learning ASR framework that adapts to new data while preserving prior knowledge. |
| Outcome: | Experiments on real-world medical audio show that the proposed framework improves over state-of-the-art models. |