Papers with domain-specific tuning
Intelligent Predictive Maintenance RAG framework for Power Plants: Enhancing QA with StyleDFS and Domain Specific Instruction Tuning (2024.emnlp-industry)
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Seongtae Hong, Joong Shin, Jaehyung Seo, Taemin Lee, Jeongbae Park, Cho Young, Byeongho Choi, Heuiseok Lim
| Challenge: | Existing off-premise Question-Answering systems based on Large Language Models face data leakage and domain-specific tuning challenges. |
| Approach: | They propose an on-premise intelligent PMS framework based on a chunking method . they propose instruction tuning using relevant domain-specific data improves LLM performance . |
| Outcome: | The proposed framework improves performance even under limited data conditions. |
Truth, Trust, and Trouble: Medical AI on the Edge (2025.emnlp-industry)
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Mohammad Anas Azeez, Rafiq Ali, Ebad Shabbir, Zohaib Hasan Siddiqui, Gautam Siddharth Kashyap, Jiechao Gao, Usman Naseem
| Challenge: | Large Language Models (LLMs) are promising for transforming digital health applications . but ensuring they meet industry standards for factual accuracy, usefulness, and safety remains a challenge . |
| Approach: | They present a framework to assess large language models' accuracy, usefulness, and safety . they assess models' honesty, helpfulness, harmlessness and domain-specific tuning . |
| Outcome: | The proposed framework assesses models across honesty, helpfulness, and harmlessness . AlpaCare-13B achieves highest accuracy (91.7%) and harmlessity (0.92) . |
An Efficient Gloss-Free Sign Language Translation Using Spatial Configurations and Motion Dynamics with LLMs (2025.naacl-long)
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| Challenge: | Existing methods for sign language translation rely on glosses, which are written representations of signs. |
| Approach: | They propose a new LLM-based SLT framework that uses off-the-shelf visual encoders to extract spatial and motion features from sign videos. |
| Outcome: | The proposed framework captures spatial configurations and motion dynamics in sign language without domain-specific tuning. |
LM-Searcher: Cross-domain Neural Architecture Search with LLMs via Unified Numerical Encoding (2025.emnlp-main)
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Yuxuan Hu, Jihao Liu, Ke Wang, Jinliang Zheng, Weikang Shi, Manyuan Zhang, Qi Dou, Rui Liu, Aojun Zhou, Hongsheng Li
| Challenge: | Recent advances in Large Language Models have opened new avenues for solving complex optimization problems, including Neural Architecture Search (NAS). |
| Approach: | They propose a framework that leverages LLMs for cross-domain neural architecture optimization without extensive domain-specific tuning. |
| Outcome: | The proposed framework achieves competitive performance in both in-domain and out-of-domain tasks. |