Papers with EQA
Grounded, or a Good Guesser? A Per-Question Balanced Dataset to Separate Blind from Grounded Models for Embodied Question Answering (2025.acl-short)
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Miles Shelton, Nate Wingerd, Kritim K Rijal, Ayush Garg, Adelina Gutic, Brett Barnes, Catherine Finegan-Dollak
| Challenge: | Embodied question answering (EQA) is based on using perception and action in an environment to answer natural language questions. |
| Approach: | They propose a "per-question balanced" EQA dataset that uses two different environments to ground a model's answers in its environment. |
| Outcome: | The proposed model performs better than chance on the PQB-EQA benchmark, showing that it does not require the model to use perception, let alone to act in its environment to find the answer. |
EQA-RM: A Generative Embodied Reward Model with Test-time Scaling (2025.emnlp-main)
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| Challenge: | Existing generic Reward Models are ill-equipped for dynamic and interactive domains. |
| Approach: | They propose a novel generative multimodal reward model specifically architected for EQA that provides interpretable, structured reward feedback. |
| Outcome: | The proposed model outperforms proprietary benchmarks, including Gemini-2.5-Flash, GPT-4o, Claude-3.5-Haiku, and open-sourced state-of-the-art models such as RoVRM and VisualPRM. |
IndustryAssetEQA: A Neurosymbolic Operational Intelligence System for Embodied Question Answering in Industrial Asset Maintenance (2026.acl-industry)
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| Challenge: | Industrial maintenance assistants produce generic explanations that are weakly grounded in telemetry and omit verifiable provenance. |
| Approach: | They propose a neurosymbolic operational intelligence system that combines episode-centric telemetry representations with a Failure Mode and Effects Analysis Knowledge Graph to enable Embodied Question Answering over industrial assets. |
| Outcome: | The proposed system improves structural validity by up to +0.51, counterfactual accuracy by up . to +0.47, and explanation entailment by +0.64, while reducing severe expert-rated overclaims from 28% to 2%. |
From Multiple-Choice to Extractive QA: A Case Study for English and Arabic (2025.coling-main)
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Teresa Lynn, Malik H. Altakrori, Samar M. Magdy, Rocktim Jyoti Das, Chenyang Lyu, Mohamed Nasr, Younes Samih, Kirill Chirkunov, Alham Fikri Aji, Preslav Nakov, Shantanu Godbole, Salim Roukos, Radu Florian, Nizar Habash
| Challenge: | Recent years have brought about very fast developments in Natural Language Processing (NLP), but many other languages are overlooked due to limited resources. |
| Approach: | They propose to repurpose a multilingual BELEBELE dataset for a task of extractive QA in the style of machine reading comprehension. |
| Outcome: | The proposed approach could be used to extract QA in the style of machine reading comprehension. |
KECP: Knowledge Enhanced Contrastive Prompting for Few-shot Extractive Question Answering (2022.emnlp-main)
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| Challenge: | Extractive Question Answering (EQA) is one of the most essential tasks in Machine Reading Comprehension (MRC). |
| Approach: | They propose a framework that transforms extractive question answering into a non-autoregressive Masked Language Modeling (MLM) generation problem. |
| Outcome: | The proposed framework outperforms state-of-the-art approaches in few-shot learning scenarios by a large margin. |
AutoEQA: Auto-Encoding Questions for Extractive Question Answering (2021.findings-emnlp)
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| Challenge: | Extractive question answering models are reliant on annotations of answer-spans in the corresponding passages. |
| Approach: | They propose a method that auto-encodes a question and generates corresponding questions from it. |
| Outcome: | The proposed method performs well in a zero-shot setting and can provide an additional loss to boost performance for extractive question answering (EQA). |
Answer Quality Aware Aggregation for Extractive QA Crowdsourcing (2022.findings-emnlp)
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| Challenge: | Existing methods for creating extractive question answering datasets are crowdsourcing, but results are often inconsistent. |
| Approach: | They propose a method for aggregating answers from different crowd workers that takes into account the relations between the answer, question, and context passage. |
| Outcome: | The proposed method outperforms baselines by 16% on precision and effectively conduct answer aggregation for extractive question answering task. |
Exploring Language Model Generalization in Low-Resource Extractive QA (2025.coling-main)
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| Challenge: | Existing LLMs struggle with dataset demands of closed domains such as medicine and law . current LLM performance in closed domain is lacking, even on traditional tasks such as Natural Language Inference . |
| Approach: | They investigate Extractive Question Answering (EQA) with Large Language Models (LLMs) under domain drift . they find that LLMs struggle with dataset demands of closed domains . |
| Outcome: | The proposed model performs poorly in extractive question answering tasks under domain drift . the proposed model can generalize to domains that require specific knowledge without training . |
CityEQA: A Hierarchical LLM Agent on Embodied Question Answering Benchmark in City Space (2025.emnlp-main)
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Yong Zhao, Kai Xu, Zhengqiu Zhu, Yue Hu, Zhiheng Zheng, Yingfeng Chen, Yatai Ji, Chen Gao, Yong Li, Jincai Huang
| Challenge: | Embodied Question Answering (EQA) tasks are primarily focused on indoor environments, leaving the complexities of urban settings unexplored. |
| Approach: | They propose a task where an embodied agent answers open-vocabulary questions in dynamic city spaces. |
| Outcome: | The proposed agent achieves 60.7% of human-level answering accuracy compared to baselines . the proposed agent outperforms existing agents in open-ended city spaces . |
CoV: Chain-of-View Prompting for Spatial Reasoning (2026.findings-acl)
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Haoyu Zhao, Akide Liu, Zeyu Zhang, Weijie Wang, Feng Chen, Ruihan Zhu, Gholamreza Haffari, Bohan Zhuang
| Challenge: | Embodied question answering requires collecting context that is distributed across multiple viewpoints . most recent vision–language models (VLMs) are constrained to a fixed and finite set of input views . |
| Approach: | They propose a training-free, test-time reasoning framework that transforms a VLM into an active viewpoint reasoner through a coarse-to-fine exploration process. |
| Outcome: | The proposed framework improves LLM-Match performance by 11.98% on four mainstream VLMs. |