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Found 13 papers, 3 papers with code
Have You Seen That Number? Investigating Extrapolation in Question Answering Models
Our work rigorously tests state-of-the-art models on DROP, a numerical MRC dataset, to see if they can handle passages that contain out-of-range numbers.
Exploiting Numerical-Contextual Knowledge to Improve Numerical Reasoning in Question Answering
Numerical reasoning over text is a challenging subtask in question answering (QA) that requires both the understanding of texts and numbers.
Can You Distinguish Truthful from Fake Reviews? User Analysis and Assistance Tool for Fake Review Detection
Customer reviews are useful in providing an indirect, secondhand experience of a product.
Self-Specialization: Uncovering Latent Expertise within Large Language Models
Recent works have demonstrated the effectiveness of self-alignment in which a large language model is, by itself, aligned to follow general instructions through the automatic generation of instructional data using a handful of human-written seeds.
Schema-Driven Information Extraction from Heterogeneous Tables
We use this collection of annotated tables to evaluate the ability of open-source and API-based language models to extract information from tables covering diverse domains and data formats.
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Distill or Annotate? Cost-Efficient Fine-Tuning of Compact Models
Fine-tuning large models is highly effective, however, inference can be expensive and produces carbon emissions.
Why So Gullible? Enhancing the Robustness of Retrieval-Augmented Models against Counterfactual Noise
Most existing retrieval-augmented language models (LMs) assume a naive dichotomy within a retrieved document set: query-relevance and irrelevance.
Maximizing Efficiency of Language Model Pre-training for Learning Representation
Moreover, we evaluate an initial approach to the problem that has not succeeded in maintaining the accuracy of the model while showing a promising compute efficiency by thoroughly investigating the necessity of the generator module of ELECTRA.
Ultra-High Dimensional Sparse Representations with Binarization for Efficient Text Retrieval
The semantic matching capabilities of neural information retrieval can ameliorate synonymy and polysemy problems of symbolic approaches.
Leveraging Order-Free Tag Relations for Context-Aware Recommendation
Tag recommendation relies on either a ranking function for top-$k$ tags or an autoregressive generation method.
Handling Anomalies of Synthetic Questions in Unsupervised Question Answering
Advances in Question Answering (QA) research require additional datasets for new domains, languages, and types of questions, as well as for performance increases.
Regularization of Distinct Strategies for Unsupervised Question Generation
Unsupervised question answering (UQA) has been proposed to avoid the high cost of creating high-quality datasets for QA.
Let Me Know What to Ask: Interrogative-Word-Aware Question Generation
Owing to an increased recall of deciding the interrogative words to be used for the generated questions, the proposed model achieves new state-of-the-art results on the task of QG in SQuAD, improving from 46. 58 to 47. 69 in BLEU-1, 17. 55 to 18. 53 in BLEU-4, 21. 24 to 22. 33 in METEOR, and from 44. 53 to 46. 94 in ROUGE-L.
