| Challenge: | Existing approaches to question answering over heterogeneous data are limited due to large scale of information and organic coupling of heterogenous data. |
| Approach: | They propose a program-based prompting framework for hybrid question answering tasks . it integrates various functions to perform hybrid information-seeking over data . |
| Outcome: | The proposed framework surpasses baseline systems and achieves the best performance under the fewshot settings. |
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| Challenge: | End-to-end neural networks excel at answering natural language questions but fail on complex ones . a proposed framework for question parsing and execution on textual QA is designed to combine the strengths of neural and symbolic methods. |
| Approach: | They propose a framework for question parsing and execution on textual QA . they parse questions into an intermediate representation and use deterministic rules to translate them . |
| Outcome: | The proposed framework outperforms existing methods in supervised, few-shot, and zero-shot settings while preserving its underlying reasoning process. |
Answering Ambiguous Questions via Iterative Prompting (2023.acl-long)
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| Challenge: | Empirical studies show that AmbigPrompt achieves state-of-the-art or competitive results while using less memory and having a lower inference latency than competing approaches. |
| Approach: | They propose an answering model with a prompting model to address imperfections in open-domain question answering . Empirical studies show AmbigPrompt achieves state-of-the-art or competitive results . |
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BlendSQL: A Scalable Dialect for Unifying Hybrid Question Answering in Relational Algebra (2024.findings-acl)
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| Challenge: | Existing hybrid question answering systems use a "prompt-and-pray" paradigm . context size limitations limit ability of many transformer-based LLMs to fit into a given prompt . |
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S3HQA: A Three-Stage Approach for Multi-hop Text-Table Hybrid Question Answering (2023.acl-short)
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| Challenge: | Existing question answering systems use a retriever-reader framework to answer multi-hop questions . existing models lack retrieval, selector, and reasoner capabilities . |
| Approach: | They propose a three-stage text tableQA framework which comprises of retriever, selector, and reasoner. |
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HybridQA: A Dataset of Multi-Hop Question Answering over Tabular and Textual Data (2020.findings-emnlp)
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| Challenge: | Existing question answering datasets focus on dealing with homogeneous information, but using homogenous information alone might lead to coverage problems. |
| Approach: | They propose a large-scale question-answering dataset that requires reasoning on heterogeneous information. |
| Outcome: | The proposed model can achieve an EM score of 40% while the existing model is far behind human performance. |
Prompting-based Synthetic Data Generation for Few-Shot Question Answering (2024.lrec-main)
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| Challenge: | Language models have boosted the performance of Question Answering, but data annotation is costly. |
| Approach: | They propose to use large language models to improve Question Answering performance . they argue that domain-agnostic knowledge from LMs is sufficient to create a well-curated dataset. |
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Doc-React: Multi-page Heterogeneous Document Question-answering (2025.acl-short)
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Junda Wu, Yu Xia, Tong Yu, Xiang Chen, Sai Sree Harsha, Akash V Maharaj, Ruiyi Zhang, Victor Bursztyn, Sungchul Kim, Ryan A. Rossi, Julian McAuley, Yunyao Li, Ritwik Sinha
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TSGP: Two-Stage Generative Prompting for Unsupervised Commonsense Question Answering (2022.findings-emnlp)
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| Challenge: | Existing studies focus on acquiring relevant knowledge by retrieving external knowledge bases and fine-tuning pre-trained models. |
| Approach: | They propose a two-stage prompt-based unsupervised commonsense question answering framework that leverages implicit knowledge stored in PrLMs to generate knowledge for questions with unlimited types and possible candidate answers independent of specified choices. |
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Retrieving Multimodal Prompts for Generative Visual Question Answering (2023.findings-acl)
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| Challenge: | Visual question answering (VQA) is a multimodal machine learning problem that challenges a model to answer a question posed about an image. |
| Approach: | They propose a generative model enhanced by multimodal prompt retrieval that integrates retrieved prompts and multimodal features to generate answers in free text. |
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IterCOMP: Reasoning-aware Adaptive Prompt Compression for Multi-hop Question Answering (2026.acl-long)
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| Challenge: | Existing prompt compression methods are designed for single-turn queries and fail to capture interdependent reasoning steps. |
| Approach: | They propose a unified, training-free prompt compression framework that integrates multi-hop reasoning within an iterative compression loop. |
| Outcome: | Experiments on MusiQue, 2WikiMultiHopQA, and HotpotQA show that iterCOMP achieves significant improvements in Exact Match and F1 scores while reducing the token budget. |