Diversify, Rationalize, and Combine: Ensembling Multiple QA Strategies for Zero-shot Knowledge-based VQA (2024.findings-emnlp)
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| Challenge: | Knowledge-based Visual Qustion-answering (K-VQA) often requires background knowledge beyond the image content. |
| Approach: | They propose a method that uses a bundle of complementary question-answering tactics to aggregate their answers using textual rationales. |
| Outcome: | Experiments show that DietCoke outperforms state-of-the-art LLM-based baselines by 2.8% and 4.7% on K-VQA. |
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Knowledge Generation for Zero-shot Knowledge-based VQA (2024.findings-eacl)
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| Challenge: | Recent knowledge-based visual question answering methods do not explicitly show the knowledge needed to answer the questions and therefore lack interpretability. |
| Approach: | They propose a method which generates knowledge from an LLM and incorporates it into a zero-shot manner. |
| Outcome: | The proposed method performs better than previous zero-shot K-VQA methods on two benchmarks and is generally relevant and helpful. |
MAGIC-VQA: Multimodal And Grounded Inference with Commonsense Knowledge for Visual Question Answering (2025.findings-acl)
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| Challenge: | Existing Large Vision-Language Models (LVLMs) lack integrated commonsense knowledge . lack of integrated common knowledge limits their robustness and accuracy in VQA . |
| Approach: | They propose a framework to enhance multimodal inference by integrating commonsense reasoning. |
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MoEMoE: Question Guided Dense and Scalable Sparse Mixture-of-Expert for Multi-source Multi-modal Answering (2025.naacl-industry)
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| Challenge: | Question Answering (QA) and Visual Question Answers (VQA) are well-studied problems in the language and vision domain. |
| Approach: | They propose a question-answer generation framework that learns attention across multiple sources and decodes this information for robust and unbiased answer generation. |
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Zero-Shot Rationalization by Multi-Task Transfer Learning from Question Answering (2020.findings-emnlp)
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| Challenge: | Existing methods to extract rationales from input text are difficult and impractical. |
| Approach: | They propose a method that leverages multi-task learning and transfer learning to generate rationales through question answering in a zero-shot fashion. |
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Generating Questions for Knowledge Bases via Incorporating Diversified Contexts and Answer-Aware Loss (D19-1)
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| Challenge: | Conventional methods for question generation neglect two crucial research issues: 1) the given predicate needs to be expressed; 2) the answer to the generated question needs to have a definitive answer. |
| Approach: | They propose a neural encoder-decoder model with multi-level copy mechanisms to generate questions . they also introduce answer-aware loss to make generated questions correspond to more definitive answers. |
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Diversifying Question Generation over Knowledge Base via External Natural Questions (2024.lrec-main)
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| Challenge: | Existing methods on knowledge base question generation focus on refining the quality of a single generated question. |
| Approach: | They propose a new diversity evaluation metric which measures the diversity among top-k generated questions for each instance while ensuring their relevance to the ground truth. |
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Commonsense for Generative Multi-Hop Question Answering Tasks (D18-1)
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| Challenge: | Reading comprehension QA tasks have seen a recent surge in popularity, yet most work has focused on fact-finding extractive QA. |
| Approach: | They propose a multi-hop generative task that uses a pointer-generator decoder to synthesize disjoint pieces of information within the context to generate an answer. |
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ExpertGenQA: Open-ended QA generation in Specialized Domains (2025.findings-emnlp)
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| Challenge: | Existing methods for generating high-quality question–answer (QA) pairs yield generic or shallow questions that fail to reflect the depth and structure of expert-written examples. |
| Approach: | They propose a question-answer generation protocol that combines few-shot prompting with dual categorization by topic and question style to produce more diverse and cognitively meaningful QA pairs. |
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FC-KBQA: A Fine-to-Coarse Composition Framework for Knowledge Base Question Answering (2023.acl-long)
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| Challenge: | Existing methods for question answering over knowledge bases (KBQA) suffer from generalization issues due to coarse-grained modeling of the logical expression. |
| Approach: | They propose a fine-to- coarse-grained framework for KBQA to ensure generalization and executability of the logical expression. |
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A Two-Stage Approach towards Generalization in Knowledge Base Question Answering (2022.findings-emnlp)
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Srinivas Ravishankar, Dung Thai, Ibrahim Abdelaziz, Nandana Mihindukulasooriya, Tahira Naseem, Pavan Kapanipathi, Gaetano Rossiello, Achille Fokoue
| Challenge: | Existing approaches for Knowledge Base Question Answering focus on a specific knowledge base or evaluating it on underlying knowledge base requires non-trivial changes. |
| Approach: | They propose a framework that separates semantic parsing from knowledge base interaction . they propose KBQA framework that allows generalization across knowledge bases . |
| Outcome: | The proposed framework achieves comparable or state-of-the-art performance on datasets with a different knowledge base. |