ENGinius: A Bilingual LLM Optimized for Plant Construction Engineering (2025.acl-industry)
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| Challenge: | Recent advances in large language models have drawn attention for their potential to automate and optimize processes across diverse sectors. |
| Approach: | They propose a specialized LLM for plant construction engineering that delivers optimized responses to plant engineers by leveraging enriched domain knowledge. |
| Outcome: | The proposed model delivers optimized responses to plant engineers by leveraging enriched domain knowledge. |
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| Challenge: | Existing LLMs mainly support English alongside a handful of high resource languages . this leaves a major gap for most low-resource languages despite increasing pace of research . |
| Approach: | This tutorial examines approaches to expand the language coverage of LLMs . they look at tokenizer training, pre-training, instruction tuning, alignment, evaluation, etc. |
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Self-Distillation for Model Stacking Unlocks Cross-Lingual NLU in 200+ Languages (2024.findings-emnlp)
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| Challenge: | Large Language Models (LLMs) excel on English NLU tasks, yet struggle to extend their NLU capabilities to underrepresented languages. |
| Approach: | They integrate machine translation models (MT) directly into LLM backbones via sample-efficient self-distillation. |
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Empowering Large Language Models for Textual Data Augmentation (2024.findings-acl)
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| Challenge: | True. True. False |
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Injecting Domain-Specific Knowledge into Large Language Models: A Comprehensive Survey (2025.findings-emnlp)
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| Challenge: | specialized LLMs are often limited in domain-specific applications that require specialized knowledge. |
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Pipeline Analysis for Developing Instruct LLMs in Low-Resource Languages: A Case Study on Basque (2025.naacl-long)
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| Challenge: | Large language models are typically optimized for resource-rich languages like English . however, the proprietary nature of these models makes them impractical for many researchers and developers. |
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| Challenge: | acquiring and curating high-quality training data remains a significant bottleneck . acquiring such high-quality data is a key challenge for researchers and practitioners . |
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Symbol-LLM: Towards Foundational Symbol-centric Interface For Large Language Models (2024.acl-long)
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| Challenge: | Large Language Models (LLMs) have limitations when it comes to comprehending and expressing world knowledge that extends beyond the boundaries of natural language. |
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EngiBench: A Benchmark for Evaluating Large Language Models on Engineering Problem Solving (2026.findings-acl)
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Xiyuan Zhou, Xinlei Wang, Yirui He, Ruixi Zou, Yang Wu, Yuheng Cheng, Yulu Xie, Wenxuan Liu, Huan Zhao, Yan Xu, Jinjin Gu, Junhua Zhao
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| Challenge: | Large language models excel in general tasks but struggle with domain-specific ones, requiring fine-tuning with specific data. |
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TPTU-v2: Boosting Task Planning and Tool Usage of Large Language Model-based Agents in Real-world Industry Systems (2024.emnlp-industry)
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Yilun Kong, Jingqing Ruan, YiHong Chen, Bin Zhang, Tianpeng Bao, Shi Shiwei, Du Qing, Xiaoru Hu, Hangyu Mao, Ziyue Li, Xingyu Zeng, Rui Zhao, Xueqian Wang
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