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Found 24 papers, 12 papers with code
EAGLE: Enhanced Visual Grounding Minimizes Hallucinations in Instructional Multimodal Models
Our approach, named EAGLE, is fully agnostic to the LLM or fusion module and works as a post-pretraining approach that improves the grounding and language alignment of the visual encoder.
Pixology: Probing the Linguistic and Visual Capabilities of Pixel-based Language Models
Pixel-based language models have emerged as a compelling alternative to subword-based language modelling, particularly because they can represent virtually any script.
Learning to Route for Dynamic Adapter Composition in Continual Learning with Language Models
In this paper, we present L2R, a method that isolates the training of new PEFT modules to ensure their task specialization.
Extracting and Encoding: Leveraging Large Language Models and Medical Knowledge to Enhance Radiological Text Representation
In the first stage, we propose a \textit{Fact Extractor} that leverages large language models (LLMs) to identify factual statements from well-curated domain-specific datasets.
CVQA: Culturally-diverse Multilingual Visual Question Answering Benchmark
Visual Question Answering (VQA) is an important task in multimodal AI, and it is often used to test the ability of vision-language models to understand and reason on knowledge present in both visual and textual data.
SemEval-2024 Task 1: Semantic Textual Relatedness for African and Asian Languages
We present the first shared task on Semantic Textual Relatedness (STR).
SemRel2024: A Collection of Semantic Textual Relatedness Datasets for 13 Languages
Exploring and quantifying semantic relatedness is central to representing language and holds significant implications across various NLP tasks.
Sequence-to-Sequence Spanish Pre-trained Language Models
In recent years, significant advancements in pre-trained language models have driven the creation of numerous non-English language variants, with a particular emphasis on encoder-only and decoder-only architectures.
A Memory Model for Question Answering from Streaming Data Supported by Rehearsal and Anticipation of Coreference Information
Existing question answering methods often assume that the input content (e. g., documents or videos) is always accessible to solve the task.
How Relevant is Selective Memory Population in Lifelong Language Learning?
Lifelong language learning seeks to have models continuously learn multiple tasks in a sequential order without suffering from catastrophic forgetting.
Memory Population in Continual Learning via Outlier Elimination
This paper introduces Memory Outlier Elimination (MOE), a method for identifying and eliminating outliers in the memory buffer by choosing samples from label-homogeneous subpopulations.
ALBETO and DistilBETO: Lightweight Spanish Language Models
In this paper we present ALBETO and DistilBETO, which are versions of ALBERT and DistilBERT pre-trained exclusively on Spanish corpora.
Entropy-based Stability-Plasticity for Lifelong Learning
The ability to continuously learn remains elusive for deep learning models.
Evaluation Benchmarks for Spanish Sentence Representations
Due to the success of pre-trained language models, versions of languages other than English have been released in recent years.
DACT-BERT: Differentiable Adaptive Computation Time for an Efficient BERT Inference
Large-scale pre-trained language models have shown remarkable results in diverse NLP applications.
Augmenting BERT-style Models with Predictive Coding to Improve Discourse-level Representations
Current language models are usually trained using a self-supervised scheme, where the main focus is learning representations at the word or sentence level.
Stress Test Evaluation of Biomedical Word Embeddings
The success of pretrained word embeddings has motivated their use in the biomedical domain, with contextualized embeddings yielding remarkable results in several biomedical NLP tasks.
TNT: Text-Conditioned Network with Transductive Inference for Few-Shot Video Classification
Recently, few-shot learning has received increasing interest.
DACT-BERT: Increasing the efficiency and interpretability of BERT by using adaptive computation time.
DACT-BERT adds an adaptive computation mechanism to the regular processing pipeline of BERT.
Interpretable Contextual Team-aware Item Recommendation: Application in Multiplayer Online Battle Arena Games
Our evaluation indicates that both the Transformer architecture and the contextual information are essential to get the best results for this item recommendation task.
Adversarial Evaluation of BERT for Biomedical Named Entity Recognition
The success of pre-trained word embeddings of the BERT model has motivated its use in tasks in the biomedical domain.
Translating Natural Language Instructions for Behavioral Robot Navigation with a Multi-Head Attention Mechanism
We propose a multi-head attention mechanism as a blending layer in a neural network model that translates natural language to a high level behavioral language for indoor robot navigation.
On Adversarial Examples for Biomedical NLP Tasks
We also show that we can significantly improve the robustness of the models by training them with adversarial examples.
Stress Test Evaluation of Transformer-based Models in Natural Language Understanding Tasks
There has been significant progress in recent years in the field of Natural Language Processing thanks to the introduction of the Transformer architecture.
Natural Language Inference
Natural Language Understanding
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