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Found 14 papers, 7 papers with code
Adaptor: Objective-Centric Adaptation Framework for Language Models
Progress in natural language processing research is catalyzed by the possibilities given by the widespread software frameworks.
Calc-X and Calcformers: Empowering Arithmetical Chain-of-Thought through Interaction with Symbolic Systems
We address this deficiency by creating Calc-X, a collection of datasets that demonstrates the appropriate use of a calculator in reasoning chains.
Text classification with word embedding regularization and soft similarity measure
In our work, we investigate the individual and joint effect of the two word embedding regularization techniques on the document processing speed and the task performance of the SCM and the WMD on text classification.
Ranked #2 on
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When FastText Pays Attention: Efficient Estimation of Word Representations using Constrained Positional Weighting
In 2018, Mikolov et al. introduced the positional language model, which has characteristics of attention-based neural machine translation models and which achieved state-of-the-art performance on the intrinsic word analogy task.
Resources and Few-shot Learners for In-context Learning in Slavic Languages
Despite the rapid recent progress in creating accurate and compact in-context learners, most recent work focuses on in-context learning (ICL) for tasks in English.
Regressive Ensemble for Machine Translation Quality Evaluation
This work introduces a simple regressive ensemble for evaluating machine translation quality based on a set of novel and established metrics.
One Size Does Not Fit All: Finding the Optimal Subword Sizes for FastText Models across Languages
In our work, we find the optimal subword sizes on the English, German, Czech, Italian, Spanish, French, Hindi, Turkish, and Russian word analogy tasks.
WebMIaS on Docker: Deploying Math-Aware Search in a Single Line of Code
Math informational retrieval (MIR) search engines are absent in the wide-spread production use, even though documents in the STEM fields contain many mathematical formulae, which are sometimes more important than text for understanding.
Methods for Estimating and Improving Robustness of Language Models
Despite their outstanding performance, large language models (LLMs) suffer notorious flaws related to their preference for simple, surface-level textual relations over full semantic complexity of the problem.
Soft Alignment Objectives for Robust Adaptation of Language Generation
Domain adaptation allows generative language models to address specific flaws caused by the domain shift of their application.
Can In-context Learners Learn a Reasoning Concept from Demonstrations?
We find that most of the recent in-context learners can not consistently benefit from the demonstrated concepts, irrespective of the model size.
Think Twice: Measuring the Efficiency of Eliminating Prediction Shortcuts of Question Answering Models
We find that while existing debiasing methods can mitigate reliance on a chosen spurious feature, the OOD performance gains of these methods can not be explained by mitigated reliance on biased features, suggesting that biases are shared among different QA datasets.
Concept-aware Training Improves In-context Learning Ability of Language Models
Many recent language models (LMs) of Transformers family exhibit so-called in-context learning (ICL) ability, manifested in the LMs' ability to modulate their function by a task described in a natural language input.
People and Places of Historical Europe: Bootstrapping Annotation Pipeline and a New Corpus of Named Entities in Late Medieval Texts
Although pre-trained named entity recognition (NER) models are highly accurate on modern corpora, they underperform on historical texts due to differences in language OCR errors.
