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Found 9 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.
The Hallucinations Leaderboard -- An Open Effort to Measure Hallucinations in Large Language Models
Large Language Models (LLMs) have transformed the Natural Language Processing (NLP) landscape with their remarkable ability to understand and generate human-like text.
Edinburgh Clinical NLP at SemEval-2024 Task 2: Fine-tune your model unless you have access to GPT-4
The NLI4CT task assesses Natural Language Inference systems in predicting whether hypotheses entail or contradict evidence from Clinical Trial Reports.
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.
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.
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.
Aligning Open IE Relations and KB Relations using a Siamese Network Based on Word Embedding
Open Information Extraction (Open IE) aims at generating entity-relation-entity triples from a large amount of text, aiming at capturing key semantics of the text.
