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Found 7 papers, 3 papers with code
Benchmarking Hallucination in Large Language Models based on Unanswerable Math Word Problem
This paper presents a new method for evaluating LLM hallucination in Question Answering (QA) based on the unanswerable math word problem (MWP).
Do Large Language Models Know What They Don't Know?
Large language models (LLMs) have a wealth of knowledge that allows them to excel in various Natural Language Processing (NLP) tasks.
Pre-training for Information Retrieval: Are Hyperlinks Fully Explored?
Hyperlinks, which are commonly used in Web pages, have been leveraged for designing pre-training objectives.
Coarse-to-Fine: Hierarchical Multi-task Learning for Natural Language Understanding
Our experiments on 13 benchmark datasets across five natural language understanding tasks demonstrate the superiority of our method.
Towards More Effective and Economic Sparsely-Activated Model
To increase the number of activated experts without an increase in computational cost, we propose SAM (Switch and Mixture) routing, an efficient hierarchical routing mechanism that activates multiple experts in a same device (GPU).
Towards Efficient NLP: A Standard Evaluation and A Strong Baseline
ELUE is dedicated to depict the Pareto Frontier for various language understanding tasks, such that it can tell whether and how much a method achieves Pareto improvement.
Replicating Active Appearance Model by Generator Network
A recent Cell paper [Chang and Tsao, 2017] reports an interesting discovery.
