| Challenge: | Contemporary large language models (LLMs) are pretrained on huge corpora of natural language text and fine-tuned using human feedback to improve their quality. |
| Approach: | They compare the performance of standard LLMs, LLM augmented with retrievals from a bespoke knowledge base and LLM with retrieval from . a web search on multiple cultural awareness benchmarks. |
| Outcome: | The retrieval augmented generation and search grounding techniques improve LLMs' ability to display familiarity with various national cultures on cultural awareness benchmarks. |
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How Well Do Large Language Models Truly Ground? (2024.naacl-long)
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| Challenge: | Existing research defines “grounding” as having the correct answer, which does not ensure the reliability of the entire response. |
| Approach: | They propose a stricter definition of grounding: fully utilizes the necessary knowledge from the provided context and stays within the limits of that knowledge. |
| Outcome: | The proposed model can be ground on external contexts and maintain its correct answer. |
Grounding Multilingual Multimodal LLMs With Cultural Knowledge (2025.emnlp-main)
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| Challenge: | a new data-centric approach could address cultural gaps in multimodal large language models . despite being trained on billions of image-text pairs, today's models are biased towards English and Western data. |
| Approach: | They propose a data-centric approach that directly grounds MLLMs in cultural knowledge. |
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CultureSynth: A Hierarchical Taxonomy-Guided and Retrieval-Augmented Framework for Cultural Question-Answer Synthesis (2025.findings-emnlp)
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| Challenge: | Cultural competence is defined as the ability to understand and adapt to multicultural contexts. |
| Approach: | They propose a framework that uses a hierarchical multilingual taxonomy and a Retrieval-Augmented Generation to synthesize culturally relevant question-answer pairs. |
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LLMs as Cultural Archives: Cultural Commonsense Knowledge Graph Extraction (2026.eacl-long)
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| Challenge: | Large language models encode rich cultural knowledge, but it remains mostly implicit and unstructured, limiting its interpretability and use. |
| Approach: | They propose an iterative framework for constructing a Cultural Commonsense Knowledge Graph using a prompt-based framework. |
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MMAC: A Multilingual, Multimodal Alignment Framework for Cultural Grounding Evaluation (2026.acl-long)
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Weihua Zheng, Zhengyuan Liu, Tanmoy Chakraborty, Weiwen Xu, Xiaoxue Gao, Bryan Chen Zhengyu Tan, Bowei Zou, Chang Liu, Yujia Hu, Xing Xie, Xiaoyuan Yi, Jing Yao, Chaojun Wang, Long Li, Rui Liu, Huiyao Liu, Koji Inoue, Ryuichi Sumida, Tatsuya Kawahara, Fan Xu, Lingyu Ye, Wei Tian, Dongjun Kim, Jimin Jung, Jaehyung Seo, Nadya Yuki Wangsajaya, Pham Minh Duc, Ojasva Saxena, Palash Nandi, Xiyan Tao, Wiwik Karlina, Tuan Luong, Keertana Arun Vasan, Roy Ka-Wei Lee, Nancy F. Chen
| Challenge: | Existing models lack cultural alignment across modalities and languages . a new framework to assess cultural awareness across linguistics and languages is needed . |
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MAKIEval: A Multilingual Automatic WiKidata-based Framework for Cultural Awareness Evaluation for LLMs (2025.findings-emnlp)
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| Challenge: | Large language models (LLMs) are used globally across many languages, but their English-centric pretraining raises concerns about cross-lingual disparities for cultural awareness . |
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Effective Large Language Model Adaptation for Improved Grounding and Citation Generation (2024.naacl-long)
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| Challenge: | Large language models generate "hallucinated" answers that are not factual . despite their widespread adoption, they can generate plausiblesounding but nonfactual information. |
| Approach: | They propose a framework that tunes large language models to self-ground claims and provide citations to retrieved documents. |
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Groundedness in Retrieval-augmented Long-form Generation: An Empirical Study (2024.findings-naacl)
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| Challenge: | a significant portion of correct answers remain compromised by hallucinations in large language models. |
| Approach: | They examine whether every generated sentence is grounded in retrieved documents or the model’s pre-training data. |
| Outcome: | The findings highlight the need for more robust mechanisms in large language models to mitigate the generation of ungrounded content. |
Embedding-Informed Adaptive Retrieval-Augmented Generation of Large Language Models (2025.coling-main)
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| Challenge: | Retrieval-augmented large language models excel in various NLP tasks but are not always helpful when the knowledge required is absent in the model. |
| Approach: | They propose to determine whether the model is knowledgeable on a query via inspecting the (contextualized) pre-trained token embeddings of LLMs. |
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DIVKNOWQA: Assessing the Reasoning Ability of LLMs via Open-Domain Question Answering over Knowledge Base and Text (2024.findings-naacl)
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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. |
| Approach: | They propose a model that uses symbolic language to generate symbolic queries . they use a dataset that is generated using predefined reasoning chains and human annotation . |
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