Question Answering in Climate Adaptation for Agriculture: Model Development and Evaluation with Expert Feedback (2025.findings-acl)
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| Challenge: | Existing domain-specific question answering systems have generative capabilities, but their ability to answer climate adaptation questions remains unclear. |
| Approach: | They propose an iterative framework that enables LLMs to dynamically aggregate information from heterogeneous sources, such as climate literature and structured tabular climate data from climate model projections and historical observations. |
| Outcome: | The proposed framework enables LLMs to dynamically aggregate information from heterogeneous sources, such as text from climate literature and structured tabular climate data from climate model projections and historical observations. |
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| Challenge: | Retrievalaugmented LLMs have been used to ground LLM in external knowledge . a gap exists in the current landscape regarding the effectiveness of grounding LLM on heterogeneous knowledge sources. |
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| Challenge: | Recent proposed long-form question answering systems have shown promising capabilities, but attributing and verifying their generated abstractive answers can be difficult. |
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YESciEval: Robust LLM-as-a-Judge for Scientific Question Answering (2025.acl-long)
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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 . |
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Ran Xu, Hui Liu, Sreyashi Nag, Zhenwei Dai, Yaochen Xie, Xianfeng Tang, Chen Luo, Yang Li, Joyce C. Ho, Carl Yang, Qi He
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| Challenge: | Large Language Models (LLMs) are showing emerging abilities, but they are not large enough to assess their capabilities. |
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A Survey of Generative Information Extraction (2025.coling-main)
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Adaptive-RAG: Learning to Adapt Retrieval-Augmented Large Language Models through Question Complexity (2024.naacl-long)
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| Challenge: | Recent Large Language Models (LLMs) generate factually incorrect answers based on their parametric memory. |
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