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Found 22 papers, 8 papers with code
NoahNMT at WMT 2021: Dual Transfer for Very Low Resource Supervised Machine Translation
This paper describes the NoahNMT system submitted to the WMT 2021 shared task of Very Low Resource Supervised Machine Translation.
Bridging the Language Gaps in Large Language Models with Inference-Time Cross-Lingual Intervention
Large Language Models (LLMs) have shown remarkable capabilities in natural language processing but exhibit significant performance gaps among different languages.
The Best of Both Worlds: Bridging Quality and Diversity in Data Selection with Bipartite Graph
In this paper, we introduce GraphFilter, a novel method that represents the dataset as a bipartite graph, linking sentences to their constituent n-grams.
Rewarding What Matters: Step-by-Step Reinforcement Learning for Task-Oriented Dialogue
Reinforcement learning (RL) is a powerful approach to enhance task-oriented dialogue (TOD) systems.
Mixture-of-Skills: Learning to Optimize Data Usage for Fine-Tuning Large Language Models
In this work, we propose a general, model-agnostic, reinforcement learning framework, Mixture-of-Skills (MoS), that learns to optimize data usage automatically during the fine-tuning process.
Sharing Matters: Analysing Neurons Across Languages and Tasks in LLMs
In this study, we aim to fill the research gap by examining how neuron activation is shared across tasks and languages.
(Perhaps) Beyond Human Translation: Harnessing Multi-Agent Collaboration for Translating Ultra-Long Literary Texts
Recent advancements in machine translation (MT) have significantly enhanced translation quality across various domains.
EmbSum: Leveraging the Summarization Capabilities of Large Language Models for Content-Based Recommendations
Content-based recommendation systems play a crucial role in delivering personalized content to users in the digital world.
Beyond Probabilities: Unveiling the Misalignment in Evaluating Large Language Models
Large Language Models (LLMs) have demonstrated remarkable capabilities across various applications, fundamentally reshaping the landscape of natural language processing (NLP) research.
Importance-Aware Data Augmentation for Document-Level Neural Machine Translation
Document-level neural machine translation (DocNMT) aims to generate translations that are both coherent and cohesive, in contrast to its sentence-level counterpart.
Adapting Large Language Models for Document-Level Machine Translation
We provide an in-depth analysis of these LLMs tailored for DocMT, examining translation errors, discourse phenomena, strategies for training and inference, the data efficiency of parallel documents, recent test set evaluations, and zero-shot crosslingual transfer.
Demystifying Instruction Mixing for Fine-tuning Large Language Models
Instruction tuning significantly enhances the performance of large language models (LLMs) across various tasks.
GPT4Video: A Unified Multimodal Large Language Model for lnstruction-Followed Understanding and Safety-Aware Generation
While the recent advances in Multimodal Large Language Models (MLLMs) constitute a significant leap forward in the field, these models are predominantly confined to the realm of input-side multimodal comprehension, lacking the capacity for multimodal content generation.
Style Over Substance: Evaluation Biases for Large Language Models
This study investigates the behavior of crowd-sourced and expert annotators, as well as LLMs, when comparing outputs from different models.
Macaw-LLM: Multi-Modal Language Modeling with Image, Audio, Video, and Text Integration
Although instruction-tuned large language models (LLMs) have exhibited remarkable capabilities across various NLP tasks, their effectiveness on other data modalities beyond text has not been fully studied.
Bactrian-X: Multilingual Replicable Instruction-Following Models with Low-Rank Adaptation
However, research on multilingual instruction tuning has been limited due to the scarcity of high-quality instruction-response datasets across different languages.
A Paradigm Shift: The Future of Machine Translation Lies with Large Language Models
We conclude by emphasizing the critical role of LLMs in guiding the future evolution of MT and offer a roadmap for future exploration in the sector.
LaMini-LM: A Diverse Herd of Distilled Models from Large-Scale Instructions
The results demonstrate that our proposed LaMini-LM models are comparable to competitive baselines, while being much smaller in size.
Ranked #15 on
Word Sense Disambiguation
on Words in Context
Document Flattening: Beyond Concatenating Context for Document-Level Neural Machine Translation
Existing work in document-level neural machine translation commonly concatenates several consecutive sentences as a pseudo-document, and then learns inter-sentential dependencies.
Universal Conditional Masked Language Pre-training for Neural Machine Translation
To the best of our knowledge, this is the first work to pre-train a unified model for fine-tuning on both NMT tasks.
Uncertainty-Aware Balancing for Multilingual and Multi-Domain Neural Machine Translation Training
In this work, we propose an approach, MultiUAT, that dynamically adjusts the training data usage based on the model's uncertainty on a small set of trusted clean data for multi-corpus machine translation.
Evaluating the Utility of Hand-crafted Features in Sequence Labelling
Conventional wisdom is that hand-crafted features are redundant for deep learning models, as they already learn adequate representations of text automatically from corpora.
Ranked #43 on
Named Entity Recognition (NER)
on CoNLL 2003 (English)
