Papers by Man Luo
In-BoXBART: Get Instructions into Biomedical Multi-Task Learning (2022.findings-naacl)
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| Challenge: | Experimental results show that the proposed model outperforms single-task baseline by 3% and multi-task (without instruction) baseline by 18% on an average. |
| Approach: | They propose a unified model that can learn all 32 instruction tasks of the BoX without any task-specific modules. |
| Outcome: | The proposed model outperforms single-task baseline by 3% and multi-task (without instruction) baseline by 18% on an average. |
Neural Retriever and Go Beyond: A Thesis Proposal (2022.naacl-srw)
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| Challenge: | Existing neural retrievers are developed for pure-text queries, which prevents them from handling multi-modality queries. |
| Approach: | They propose methods to address issues of existing neural retrievers from three angles . they propose new model architectures, IR-oriented pretraining tasks and generating large scale training data . |
| Outcome: | The proposed methods address the abovementioned issues of neural retrievers from three angles and generate large scale training data. |
End-to-end Knowledge Retrieval with Multi-modal Queries (2023.acl-long)
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| Challenge: | a new task is proposed to learn knowledge retrieval with multimodal queries . a vision-language model can retrieve knowledge using images and text inputs . |
| Approach: | They propose a task for vision-language models to retrieve knowledge with multi-modal queries . they propose reViz, a model that integrates content from both text and image queries based on a multimodal query task . |
| Outcome: | The proposed task performs better under zero-shot settings than previous work on cross-modal retrieval. |
Weakly-Supervised Visual-Retriever-Reader for Knowledge-based Question Answering (2021.emnlp-main)
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| Challenge: | Existing knowledge-based visual question answering systems rely on Concept-Net and Wikipedia to obtain external knowledge. |
| Approach: | They propose a visual retriever-reader pipeline that uses a natural language knowledge base and a Visual retriever to retrieve relevant knowledge. |
| Outcome: | The proposed method significantly improves the visual retriever-reader pipeline on the OK-VQA benchmark. |
LogicBench: Towards Systematic Evaluation of Logical Reasoning Ability of Large Language Models (2024.acl-long)
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Mihir Parmar, Nisarg Patel, Neeraj Varshney, Mutsumi Nakamura, Man Luo, Santosh Mashetty, Arindam Mitra, Chitta Baral
| Challenge: | Existing work investigating the logical reasoning ability of large language models has focused only on a couple of inference rules of propositional and first-order logics. |
| Approach: | They propose to use a natural language question-answering dataset to evaluate the logical reasoning ability of large language models. |
| Outcome: | The proposed model performs poorly on a range of natural language questions using chain-of-thought prompting. |
Probing Semantic Routing in Large Mixture-of-Expert Models (2025.findings-emnlp)
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| Challenge: | large mixture-of-expert models have become increasingly common in the open domain . prior work has explored functional differentiation through routing behavior . |
| Approach: | They investigate whether expert routing in large mixture-of-expert models is influenced by the semantics of the inputs. |
| Outcome: | The results show that expert routing is influenced by the semantics of the inputs. |
Generalized but not Robust? Comparing the Effects of Data Modification Methods on Out-of-Domain Generalization and Adversarial Robustness (2022.findings-acl)
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| Challenge: | Data modification has been proposed as an effective solution for generalizing to out-of-domain (OOD) inputs. |
| Approach: | They propose to use data modification to generalize to out-of-domain inputs . they also analyze their adversarial robustness using a synthetic dataset . |
| Outcome: | The proposed data modification strategies improve OOD accuracy and AR, but data filtering hurts OOD on other tasks. |
‘Just because you are right, doesn’t mean I am wrong’: Overcoming a bottleneck in development and evaluation of Open-Ended VQA tasks (2021.eacl-main)
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Man Luo, Shailaja Keyur Sampat, Riley Tallman, Yankai Zeng, Manuha Vancha, Akarshan Sajja, Chitta Baral
| Challenge: | Existing visual question answering datasets assume only one ground truth answer for each question. |
| Approach: | They propose alternative answer sets (AAS) of ground-truth answers to address this limitation . they modify top VQA solvers to support multiple plausible answers for a question . |
| Outcome: | The proposed approach improves on the GQA dataset and shows that it is more efficient than previous approaches. |
A Study on the Efficiency and Generalization of Light Hybrid Retrievers (2023.acl-short)
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Man Luo, Shashank Jain, Anchit Gupta, Arash Einolghozati, Barlas Oguz, Debojeet Chatterjee, Xilun Chen, Chitta Baral, Peyman Heidari
| Challenge: | Recent research focuses on building neural retrievers which learn dense embeddings of query and document into a semantic space. |
| Approach: | They propose to use an indexing-efficient dense retriever to reduce hybrid retrievers' memory by using the state-based indexing algorithm. |
| Outcome: | The proposed hybrid retriever saves 13 memory while maintaining 98.0% performance on out-of-domain datasets and adversarial attacks datasets. |
A Simple Approach to Jointly Rank Passages and Select Relevant Sentences in the OBQA Context (2022.naacl-srw)
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| Challenge: | Existing frameworks for OBQA use separate models to select relevant passages and sentences. |
| Approach: | They propose a framework to jointly rank passages and select sentences to improve correlation between them. |
| Outcome: | The proposed framework outperforms baseline systems in terms of matching of relevant sentences on the hotpotQA dataset by 28%. |