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Found 13 papers, 6 papers with code
Decoding Probing: Revealing Internal Linguistic Structures in Neural Language Models using Minimal Pairs
4) For Transformer-based models, both embeddings and attentions capture grammatical features but show distinct patterns.
Decomposed Prompting: Unveiling Multilingual Linguistic Structure Knowledge in English-Centric Large Language Models
Despite the predominance of English in their training data, English-centric Large Language Models (LLMs) like GPT-3 and LLaMA display a remarkable ability to perform multilingual tasks, raising questions about the depth and nature of their cross-lingual capabilities.
GNNavi: Navigating the Information Flow in Large Language Models by Graph Neural Network
Large Language Models (LLMs) exhibit strong In-Context Learning (ICL) capabilities when prompts with demonstrations are applied to them.
Why Lift so Heavy? Slimming Large Language Models by Cutting Off the Layers
Large Language Models (LLMs) possess outstanding capabilities in addressing various natural language processing (NLP) tasks.
ToPro: Token-Level Prompt Decomposition for Cross-Lingual Sequence Labeling Tasks
However, most previous studies primarily focused on sentence-level classification tasks, and only a few considered token-level labeling tasks such as Named Entity Recognition (NER) and Part-of-Speech (POS) tagging.
From Classification to Generation: Insights into Crosslingual Retrieval Augmented ICL
The remarkable ability of Large Language Models (LLMs) to understand and follow instructions has sometimes been limited by their in-context learning (ICL) performance in low-resource languages.
Crosslingual Retrieval Augmented In-context Learning for Bangla
The promise of Large Language Models (LLMs) in Natural Language Processing has often been overshadowed by their limited performance in low-resource languages such as Bangla.
Unleashing the Multilingual Encoder Potential: Boosting Zero-Shot Performance via Probability Calibration
Pretrained multilingual encoder models can directly perform zero-shot multilingual tasks or linguistic probing by reformulating the input examples into cloze-style prompts.
Cross-Lingual Constituency Parsing for Middle High German: A Delexicalized Approach
However, training an automatic syntactic analysis system for ancient languages solely relying on annotated parse data is a formidable task due to the inherent challenges in building treebanks for such languages.
Baby's CoThought: Leveraging Large Language Models for Enhanced Reasoning in Compact Models
Large Language Models (LLMs) demonstrate remarkable performance on a variety of natural language understanding (NLU) tasks, primarily due to their in-context learning ability.
Is Prompt-Based Finetuning Always Better than Vanilla Finetuning? Insights from Cross-Lingual Language Understanding
We conduct comprehensive experiments on diverse cross-lingual language understanding tasks (sentiment classification, paraphrase identification, and natural language inference) and empirically analyze the variation trends of prompt-based finetuning performance in cross-lingual transfer across different few-shot and full-data settings.
Natural Language Inference
Natural Language Understanding
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Cross-Lingual Retrieval Augmented Prompt for Low-Resource Languages
Multilingual Pretrained Language Models (MPLMs) have shown their strong multilinguality in recent empirical cross-lingual transfer studies.
What cleaves? Is proteasomal cleavage prediction reaching a ceiling?
While direct identification of proteasomal cleavage \emph{in vitro} is cumbersome and low throughput, it is possible to implicitly infer cleavage events from the termini of MHC-presented epitopes, which can be detected in large amounts thanks to recent advances in high-throughput MHC ligandomics.
