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Found 16 papers, 14 papers with code
Prompt-Driven Neural Machine Translation
Neural machine translation (NMT) has obtained significant performance improvement over the recent years.
LexMatcher: Dictionary-centric Data Collection for LLM-based Machine Translation
The fine-tuning of open-source large language models (LLMs) for machine translation has recently received considerable attention, marking a shift towards data-centric research from traditional neural machine translation.
Spotting AI's Touch: Identifying LLM-Paraphrased Spans in Text
To this end, we propose a novel detection framework, paraphrased text span detection (PTD), aiming to identify paraphrased text spans within a text.
What Have We Achieved on Non-autoregressive Translation?
Recent advances have made non-autoregressive (NAT) translation comparable to autoregressive methods (AT).
Generating Diverse Criteria On-the-Fly to Improve Point-wise LLM Rankers
The most recent pointwise Large Language Model (LLM) rankers have achieved remarkable ranking results.
Potential and Challenges of Model Editing for Social Debiasing
Large language models (LLMs) trained on vast corpora suffer from inevitable stereotype biases.
Understanding In-Context Learning from Repetitions
This paper explores the elusive mechanism underpinning in-context learning in Large Language Models (LLMs).
Siren's Song in the AI Ocean: A Survey on Hallucination in Large Language Models
While large language models (LLMs) have demonstrated remarkable capabilities across a range of downstream tasks, a significant concern revolves around their propensity to exhibit hallucinations: LLMs occasionally generate content that diverges from the user input, contradicts previously generated context, or misaligns with established world knowledge.
An Empirical Study of Catastrophic Forgetting in Large Language Models During Continual Fine-tuning
Catastrophic forgetting (CF) is a phenomenon that occurs in machine learning when a model forgets previously learned information while acquiring new knowledge.
Revisiting Cross-Lingual Summarization: A Corpus-based Study and A New Benchmark with Improved Annotation
Additionally, based on the same intuition, we propose a 2-Step method, which takes both conversation and summary as input to simulate human annotation process.
Explicit Syntactic Guidance for Neural Text Generation
Most existing text generation models follow the sequence-to-sequence paradigm.
MAGE: Machine-generated Text Detection in the Wild
In practical scenarios, however, the detector faces texts from various domains or LLMs without knowing their sources.
GLUE-X: Evaluating Natural Language Understanding Models from an Out-of-distribution Generalization Perspective
Pre-trained language models (PLMs) are known to improve the generalization performance of natural language understanding models by leveraging large amounts of data during the pre-training phase.
Natural Language Understanding
Out-of-Distribution Generalization
Multi-Granularity Optimization for Non-Autoregressive Translation
Despite low latency, non-autoregressive machine translation (NAT) suffers severe performance deterioration due to the naive independence assumption.
Categorizing Semantic Representations for Neural Machine Translation
Modern neural machine translation (NMT) models have achieved competitive performance in standard benchmarks.
On Compositional Generalization of Neural Machine Translation
Modern neural machine translation (NMT) models have achieved competitive performance in standard benchmarks such as WMT.
