Papers with Knowledge Base
Two-tiered Encoder-based Hallucination Detection for Retrieval-Augmented Generation in the Wild (2024.emnlp-industry)
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| Challenge: | Existing solutions for hallucination detection do not consider latency, train or evaluate on production data. |
| Approach: | They propose to use customer service conversation data to evaluate existing methods . they propose to train small encoder models on a new dataset to outperform existing methods. |
| Outcome: | The proposed model outperforms existing methods and highlights the value of combining small amounts of in-domain data with public datasets. |
DISCOSQA: A Knowledge Base Question Answering System for Space Debris based on Program Induction (2023.acl-industry)
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| Challenge: | a system that can answer complex natural language queries is developed for the European Space Agency . space debris are uncontrolled artificial objects left in orbit during normal operations or due to malfunctions . |
| Approach: | They propose a query-based system that can answer queries in natural language . it generates a program sketch from a natural language question and executes it against the database . |
| Outcome: | The proposed system can answer queries in natural language based on a natural language question generated by a query program . the system reduces overfitting and shortcut learning even with limited training data, the authors say . |
Disentangling Language and Knowledge in Task-Oriented Dialogs (N19-1)
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| Challenge: | Existing approaches to handle task-oriented dialogs break when asked to handle such changes. |
| Approach: | They propose an encoder-decoder architecture with a novel Bag-of-Sequences memory which facilitates the disentangled learning of the response’s language model and its knowledge incorporation. |
| Outcome: | The proposed architecture outperforms state-of-the-art models on bAbI OOV test sets and other human-human datasets and shows that it is robust to KB modifications. |
TDEER: An Efficient Translating Decoding Schema for Joint Extraction of Entities and Relations (2021.emnlp-main)
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| Challenge: | Existing methods to extract entities and relations from unstructured texts are difficult to handle due to the overlapping triple problem. |
| Approach: | They propose a translation decoding schema for joint extraction of entities and relations from unstructured texts to form factual triples. |
| Outcome: | The proposed model can handle the overlapping triple problem, and is 2 times faster than the state-of-the-art models. |