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Found 10 papers, 4 papers with code
BECEL: Benchmark for Consistency Evaluation of Language Models
Behavioural consistency is a critical condition for a language model (LM) to become trustworthy like humans.
KNOW How to Make Up Your Mind! Adversarially Detecting and Alleviating Inconsistencies in Natural Language Explanations
While recent works have been considerably improving the quality of the natural language explanations (NLEs) generated by a model to justify its predictions, there is very limited research in detecting and alleviating inconsistencies among generated NLEs.
Beyond Distributional Hypothesis: Let Language Models Learn Meaning-Text Correspondence
To alleviate the issue, we propose a novel intermediate training task, names meaning-matching, designed to directly learn a meaning-text correspondence, instead of relying on the distributional hypothesis.
KOBEST: Korean Balanced Evaluation of Significant Tasks
To this end, we propose a new benchmark named Korean balanced evaluation of significant tasks (KoBEST), which consists of five Korean-language downstream tasks.
Are Training Resources Insufficient? Predict First Then Explain!
The most predominant form of these models is the explain-then-predict (EtP) structure, which first generates explanations and uses them for making decisions.
NoiER: An Approach for Training more Reliable Fine-TunedDownstream Task Models
The recent development in pretrained language models trained in a self-supervised fashion, such as BERT, is driving rapid progress in the field of NLP.
Accurate, yet inconsistent? Consistency Analysis on Language Understanding Models
Consistency, which refers to the capability of generating the same predictions for semantically similar contexts, is a highly desirable property for a sound language understanding model.
Sentence transition matrix: An efficient approach that preserves sentence semantics
Sentence embedding is a significant research topic in the field of natural language processing (NLP).
Paraphrase Thought: Sentence Embedding Module Imitating Human Language Recognition
However, because the performances of sentence classification and sentiment analysis can be enhanced by using a simple sentence representation method, it is not sufficient to claim that these models fully reflect the meanings of sentences based on good performances for such tasks.
Recurrent Neural Network-Based Semantic Variational Autoencoder for Sequence-to-Sequence Learning
In this paper, we propose a new recurrent neural network (RNN)-based Seq2seq model, RNN semantic variational autoencoder (RNN--SVAE), to better capture the global latent information of a sequence of words.
