| Challenge: | Existing models for text-based recommendation lack data sparsity and flexibility to capture fluctuations in user preferences over time. |
| Approach: | They present the first domain-adapted and fully-trained large language model for text-based recommendation. |
| Outcome: | The proposed model outperforms baseline models on rating prediction and sequential recommendation tasks. |
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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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Advances in Pre-Training Distributed Word Representations (L18-1)
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| Challenge: | Pre-trained word representations are a building block of many Natural Language Processing and Machine Learning applications. |
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GPT3Mix: Leveraging Large-scale Language Models for Text Augmentation (2021.findings-emnlp)
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| Challenge: | Recent studies report that prompt-based direct classification eliminates the need for fine-tuning but lacks data and inference scalability. |
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FinGPT: Large Generative Models for a Small Language (2023.emnlp-main)
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Risto Luukkonen, Ville Komulainen, Jouni Luoma, Anni Eskelinen, Jenna Kanerva, Hanna-Mari Kupari, Filip Ginter, Veronika Laippala, Niklas Muennighoff, Aleksandra Piktus, Thomas Wang, Nouamane Tazi, Teven Scao, Thomas Wolf, Osma Suominen, Samuli Sairanen, Mikko Merioksa, Jyrki Heinonen, Aija Vahtola, Samuel Antao, Sampo Pyysalo
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Shall We Pretrain Autoregressive Language Models with Retrieval? A Comprehensive Study (2023.emnlp-main)
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Boxin Wang, Wei Ping, Peng Xu, Lawrence McAfee, Zihan Liu, Mohammad Shoeybi, Yi Dong, Oleksii Kuchaiev, Bo Li, Chaowei Xiao, Anima Anandkumar, Bryan Catanzaro
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StyleDGPT: Stylized Response Generation with Pre-trained Language Models (2020.findings-emnlp)
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| Challenge: | Existing methods for generating responses following a desired style are lacking of parallel data for training. |
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Combining Deep Generative Models and Multi-lingual Pretraining for Semi-supervised Document Classification (2021.eacl-main)
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| Challenge: | Semi-supervised learning and multilingual pretraining have been shown to be effective for task-specific labelled data shortages. |
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Exploring Versatile Generative Language Model Via Parameter-Efficient Transfer Learning (2020.findings-emnlp)
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| Challenge: | Large-scale language models can be fine-tuned to learn highly transferable embedding, but they are expensive and require multiple model parameters. |
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Textagon: Boosting Language Models with Theory-guided Parallel Representations (2025.acl-demo)
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| Challenge: | Pretrained language models do not account for the wide variety of available expert-generated language resources and lexicons that explicitly encode linguistic/domain knowledge. |
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General Purpose Text Embeddings from Pre-trained Language Models for Scalable Inference (2020.findings-emnlp)
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| Challenge: | Large pre-trained language models are currently used for many NLP tasks . however, inference for these models requires significant computational resources . |
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