| Challenge: | Query auto-completion (QAC) is a search engine feature that suggests completed queries as the user types . recent work suggests personalization of the recurrent layer to generate personalized completions. |
| Approach: | They propose to use a recurrent neural network language model to generate personalized completions for search engines. |
| Outcome: | The proposed model can generate personalized completions for users not seen during training. |
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Personalized neural language models for real-world query auto completion (N18-3)
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| Challenge: | Existing popularity-based methods for query auto completion (QAC) are ineffective in predicting unseen queries. |
| Approach: | They propose to use real-world data to build an end-to-end system that can predict unseen queries by integrating user information. |
| Outcome: | The proposed methods improve on two separate datasets while increasing diversity while scalability. |
Subword Language Model for Query Auto-Completion (D19-1)
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| Challenge: | Current neural query auto-completion systems rely on character-level language models but they slow down when queries are long. |
| Approach: | They propose to use subword language models for query auto-completion . they propose to implement a retrace algorithm and a method for approximate marginalization . |
| Outcome: | The proposed model achieves 2.5 times faster while maintaining similar quality to character-level baseline. |
Improving Text Auto-Completion with Next Phrase Prediction (2021.findings-emnlp)
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| Challenge: | Language models such as GPT-2 require considerable training effort to adapt to specific writing domains (e.g., medical). |
| Approach: | They propose an intermediate training strategy that encourages language models to complete partial queries with enriched phrases and eventually improve their text auto-completion performance. |
| Outcome: | The proposed approach outperforms baselines in auto-completion tasks for email and academic-writing domains with only around 1.2B tokens. |
Retrieval-based Language Models and Applications (2023.acl-tutorials)
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| Challenge: | In this tutorial, we will provide a comprehensive overview of retrieval-based language models. |
| Approach: | This tutorial will provide a comprehensive overview of recent advances in retrieval-based language models. |
| Outcome: | This tutorial will provide a comprehensive overview of recent advances in retrieval-based language models. |
A Framework for Adapting Pre-Trained Language Models to Knowledge Graph Completion (2022.emnlp-main)
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| Challenge: | Recent work has demonstrated that entity representations can be extracted from pre-trained language models to develop knowledge graph completion models that are more robust to the naturally occurring sparsity found in knowledge graphs. |
| Approach: | They propose unsupervised and supervised methods to extract more informative representations from pre-trained language models to develop knowledge graph completion models. |
| Outcome: | The proposed model outperforms recent neural models in terms of performance and unsupervised processing methods. |
Rethinking Word-Level Auto-Completion in Computer-Aided Translation (2023.emnlp-main)
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| Challenge: | Existing models for word-level auto-completion (WLAC) do not meet the criterion of good auto-completes. |
| Approach: | They propose a measurable criterion to address the question: what kind of words are good auto-completions? they propose an approach to enhance WLAC performance by promoting adherence to the cri-terion. |
| Outcome: | The proposed approach outperforms the top-performing system submitted to the WLAC shared tasks in WMT2022 while using significantly smaller model sizes. |
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. |
| Approach: | They examine the progress, methods, and future directions of large language models . they examine what generative recommendation is, why RS should advance to generative recommendations . |
| Outcome: | The proposed approach can be simplified to generate recommendations from the entire pool of items. |
Leveraging Similar Users for Personalized Language Modeling with Limited Data (2022.acl-long)
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| Challenge: | Recent work suggests that personalized models are more accurate for individual users than one-size-fits-all solutions. |
| Approach: | They propose a model trained on users that are similar to a new user to find similarity between new and existing users. |
| Outcome: | The proposed model can predict what a user will write when they join a platform and not enough text is available. |
An Efficient Retrieval-Based Method for Tabular Prediction with LLM (2025.coling-main)
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| Challenge: | Existing methods for tabular prediction rely on extensive pre-training or fine-tuning of LLMs . a retrieval-based approach eliminates the need for training any modules or performing data augmentation . |
| Approach: | They propose a retrieval-based approach that utilizes the powerful capabilities of large language models in representation, comprehension, and inference. |
| Outcome: | The proposed method exhibits strong predictive performance on tabular prediction task, affirming its practicality and effectiveness. |
AUTOSUMM: Automatic Model Creation for Text Summarization (2021.emnlp-main)
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Sharmila Reddy Nangi, Atharv Tyagi, Jay Mundra, Sagnik Mukherjee, Raj Snehal, Niyati Chhaya, Aparna Garimella
| Challenge: | Recent efforts to develop deep learning models for text generation tasks are challenging for non-experts. |
| Approach: | They propose methods to automatically create deep learning models for extractive and abstractive summarization tasks using large language models. |
| Outcome: | The proposed methods achieve near state-of-the-art performance on a range of datasets. |