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Found 6 papers, 5 papers with code
EEE-QA: Exploring Effective and Efficient Question-Answer Representations
Current approaches to question answering rely on pre-trained language models (PLMs) like RoBERTa.
Think While You Write: Hypothesis Verification Promotes Faithful Knowledge-to-Text Generation
Knowledge-to-text generators often struggle to faithfully generate descriptions for the input facts: they may produce hallucinations that contradict the input, or describe facts not present in the input.
Are Large Language Models Temporally Grounded?
Instead, we provide LLMs with textual narratives and probe them with respect to their common-sense knowledge of the structure and duration of events, their ability to order events along a timeline, and self-consistency within their temporal model (e. g., temporal relations such as after and before are mutually exclusive for any pair of events).
Detecting and Mitigating Hallucinations in Multilingual Summarisation
With the existing faithful metrics focusing on English, even measuring the extent of this phenomenon in cross-lingual settings is hard.
Abstractive Summarization Guided by Latent Hierarchical Document Structure
Sequential abstractive neural summarizers often do not use the underlying structure in the input article or dependencies between the input sentences.
DuReader_retrieval: A Large-scale Chinese Benchmark for Passage Retrieval from Web Search Engine
In this paper, we present DuReader_retrieval, a large-scale Chinese dataset for passage retrieval.
