| Challenge: | Traditionally, mechanics manually browse lengthy documents to locate component information, a process that is time-consuming and error-prone. |
| Approach: | They propose to use large language models to enrich and unify a component database and use hybrid search to select the most relevant component for a document. |
| Outcome: | The proposed method outperforms baselines based on an expert-annotated dataset and significantly reduces the search space and improves retrieval efficiency. |
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| Challenge: | Large Language Models (LLMs) have demonstrated great potential in complex tasks such as multi-label classification, but the vast number of labels can exceed LLMs’ input limits. |
| Approach: | They propose a method that integrates large language models with dense retrieval techniques to overcome these challenges. |
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Best Practices for Distilling Large Language Models into BERT for Web Search Ranking (2025.coling-industry)
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| Challenge: | Recent studies have highlighted the potential of Large Language Models (LLMs) as zero-shot relevance rankers. |
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LLMs Can Also Do Well! Breaking Barriers in Semantic Role Labeling via Large Language Models (2025.findings-acl)
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| Challenge: | Semantic role labeling (SRL) is a crucial task of natural language processing (NLP). |
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Aligning Large Language Models with Recommendation Knowledge (2024.findings-naacl)
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Yuwei Cao, Nikhil Mehta, Xinyang Yi, Raghunandan Hulikal Keshavan, Lukasz Heldt, Lichan Hong, Ed Chi, Maheswaran Sathiamoorthy
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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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Large Language Models as Financial Data Annotators: A Study on Effectiveness and Efficiency (2024.lrec-main)
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Toyin D. Aguda, Suchetha Siddagangappa, Elena Kochkina, Simerjot Kaur, Dongsheng Wang, Charese Smiley
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Large Language Models for Generative Recommendation: A Survey and Visionary Discussions (2024.lrec-main)
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| Challenge: | Large language models (LLMs) have revolutionized the field of natural language processing but are not fully able to leverage the generative power of LLM. |
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| Challenge: | Document-level relation extraction (DocRE) provides a broad context for extracting relations for entities. |
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Large Language Models are Built-in Autoregressive Search Engines (2023.findings-acl)
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| Challenge: | Existing dual-encoder dense retrievers obtain representations for questions and documents independently, allowing only shallow interactions between them. |
| Approach: | They propose to use large language models to generate URLs for document retrieval by following human instructions. |
| Outcome: | The proposed method achieves better retrieval performance than existing retrieval approaches on open-domain question answering benchmarks. |
The Data Frontier for Large Language Models: Selection, Synthesis, and Tools (2026.acl-tutorials)
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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 . |
| Approach: | This tutorial provides a comprehensive and practical guide to the state-of-the-art in data research directions for LLMs. |
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