AmazonQAC: A Large-Scale, Naturalistic Query Autocomplete Dataset (2024.emnlp-industry)
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| Challenge: | Existing systems that provide a graphical representation of QAC are limited in their ability to provide real-time data. |
| Approach: | They introduce a new QAC dataset sourced from Amazon Search logs . they assess Prefix Trees, semantic retrieval, and Large Language Models with and without finetuning . |
| Outcome: | The proposed system can predict search terms based on user-typed prefixes . the proposed system achieves only half of what is theoretically possible on the test data . |
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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. |
MESAQA: A Dataset for Multi-Span Contextual and Evidence-Grounded Question Answering (2025.coling-main)
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| Challenge: | Existing question answering systems focus on extracting answers from single spans, but real-world scenarios require synthesizing information from multiple spans. |
| Approach: | They propose a dataset that leverages the MASH-QA dataset and large language models (LLMs) to ensure that each Q/A pair requires considering all selected spans. |
| Outcome: | The proposed method enables the model to answer multiple Q/A pairs in a single span, while ensuring that all selected spans are considered. |
AutoQA: From Databases To QA Semantic Parsers With Only Synthetic Training Data (2020.emnlp-main)
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| Challenge: | Existing methods to generate semantic parsers that answer questions on databases require large amounts of annotated data. |
| Approach: | They propose a method to generate semantic parsers that answer questions on databases . they use automatic paraphrasing and template-based parsing to find alternative expressions . |
| Outcome: | The proposed method achieves 69.8% answer accuracy on natural questions, 16.4% higher than state-of-the-art models and 5.2% lower than the same model trained with human data. |
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. |
Adaptive-RAG: Learning to Adapt Retrieval-Augmented Large Language Models through Question Complexity (2024.naacl-long)
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| Challenge: | Recent Large Language Models (LLMs) generate factually incorrect answers based on their parametric memory. |
| Approach: | They propose a retrieval-augmented large language model that can dynamically select the most suitable strategy based on query complexity. |
| Outcome: | The proposed approach improves the performance of QA systems on open-domain QA datasets. |
QuickLLaMA: Query-aware Inference Acceleration for Large Language Models (2025.coling-main)
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| Challenge: | Large Language Models (LLMs) struggle with capturing long-distance dependencies within sequences to deeply understand semantics. |
| Approach: | They propose a system that captures relevant information within a fixed window size and provides precise answers to queries. |
| Outcome: | The proposed system can read Harry Potter within 30s and accurately answer the questions. |
QuAC: Question Answering in Context (D18-1)
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Eunsol Choi, He He, Mohit Iyyer, Mark Yatskar, Wen-tau Yih, Yejin Choi, Percy Liang, Luke Zettlemoyer
| Challenge: | a dataset for Question Answering in Context contains 14K information-seeking QA dialogs . questions are often more open-ended, unanswerable, or only meaningful within the dialog context . |
| Approach: | They propose a dataset for Question Answering in Context that contains 14K dialogs . they use a student to ask questions about a Wikipedia section and a teacher to answer them . |
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ComQA: A Community-sourced Dataset for Complex Factoid Question Answering with Paraphrase Clusters (N19-1)
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| Challenge: | ComQA dataset captures question phenomena and the diverse ways in which they are formulated. |
| Approach: | They propose a large dataset of real user questions that captures question phenomena and the diverse ways in which they are formulated. |
| Outcome: | The proposed dataset can be a driver of future research on factoid question answering (QA). |
Argument Mining with Fine-Tuned Large Language Models (2025.coling-main)
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| Challenge: | Argument Mining (AM) pipelines use fine-tuned large language models (LLMs) . initial approaches employ supervised machine learning algorithms, such as Maximum Entropy classifiers and Logistic Regressions. |
| Approach: | They propose to model the three main AM sub-tasks as text generation tasks and fine-tune eight popular quantized and non-quantized large language models (LLMs) on the benchmark PE, AbstRCT, and CDCP datasets. |
| Outcome: | The proposed pipeline achieves state-of-the-art across all AM sub-tasks and datasets, showing significant improvements over previous benchmarks. |
Developing and Utilizing a Large-Scale Cantonese Dataset for Multi-Tasking in Large Language Models (2025.findings-emnlp)
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Jiyue Jiang, Alfred Kar Yin Truong, Yanyu Chen, Qinghang Bao, Sheng Wang, Pengan Chen, Jiuming Wang, Lingpeng Kong, Yu Li, Chuan Wu
| Challenge: | Cantonese is considered a low-resource language due to the dominance of Mandarin . rich colloquial vocabulary of Cantone, English loanwords, and code-switching characteristics add to the complexity of corpus collection and processing. |
| Approach: | We collect Cantonese texts from open source corpora, Hong Kong-specific forums, Wikipedia . we refine the model through supervised fine-tuning on curated Cantonesian tasks . |
| Outcome: | The model achieves state-of-the-art (SOTA) performance on four Cantonese benchmarks. |