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Found 12 papers, 6 papers with code
HyperCLOVA X Technical Report
We introduce HyperCLOVA X, a family of large language models (LLMs) tailored to the Korean language and culture, along with competitive capabilities in English, math, and coding.
Tasks That Language Models Don't Learn
We argue that there are certain properties of language that our current large language models (LLMs) don't learn.
Instruction Tuning with Human Curriculum
In this work, we (1) introduce Curriculum Instruction Tuning, (2) explore the potential advantages of employing diverse curriculum strategies, and (3) delineate a synthetic instruction-response generation framework that complements our theoretical approach.
A Side-by-side Comparison of Transformers for English Implicit Discourse Relation Classification
Though discourse parsing can help multiple NLP fields, there has been no wide language model search done on implicit discourse relation classification.
Discourse Parsing
Implicit Discourse Relation Classification
+3
Linguistic Properties of Truthful Response
We investigate the phenomenon of an LLM's untruthful response using a large set of 220 handcrafted linguistic features.
LFTK: Handcrafted Features in Computational Linguistics
We open-source our system for public access to a rich set of pre-implemented handcrafted features.
Prompt-based Learning for Text Readability Assessment
We propose the novel adaptation of a pre-trained seq2seq model for readability assessment.
Traditional Readability Formulas Compared for English
Traditional English readability formulas, or equations, were largely developed in the 20th century.
Auto-Select Reading Passages in English Assessment Tests?
We show a method to auto-select reading passages in English assessment tests and share some key insights that can be helpful in related fields.
Pushing on Text Readability Assessment: A Transformer Meets Handcrafted Linguistic Features
We report two essential improvements in readability assessment: 1. three novel features in advanced semantics and 2. the timely evidence that traditional ML models (e. g. Random Forest, using handcrafted features) can combine with transformers (e. g. RoBERTa) to augment model performance.
LXPER Index 2.0: Improving Text Readability Assessment Model for L2 English Students in Korea
We train our model with CoKEC-text and significantly improve the accuracy of readability assessment for texts in the Korean ELT curriculum.
LXPER Index: a curriculum-specific text readability assessment model for EFL students in Korea
Automatic readability assessment is one of the most important applications of Natural Language Processing (NLP) in education.
