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Found 5 papers, 3 papers with code
Enabling Ensemble Learning for Heterogeneous Large Language Models with Deep Parallel Collaboration
To address this challenge, DEEPEN maps the probability distribution of each model from the probability space to a universe relative space based on the relative representation theory, and performs aggregation.
Aligning Translation-Specific Understanding to General Understanding in Large Language Models
To align the translation-specific understanding to the general one, we propose a novel translation process xIoD (Cross-Lingual Interpretation of Difficult words), explicitly incorporating the general understanding on the content incurring inconsistent understanding to guide the translation.
Towards Higher Pareto Frontier in Multilingual Machine Translation
Multilingual neural machine translation has witnessed remarkable progress in recent years.
Unifying the Convergences in Multilingual Neural Machine Translation
In this paper, we propose a novel training strategy named LSSD (Language-Specific Self-Distillation), which can alleviate the convergence inconsistency and help MNMT models achieve the best performance on each language pair simultaneously.
The Factual Inconsistency Problem in Abstractive Text Summarization: A Survey
Recently, various neural encoder-decoder models pioneered by Seq2Seq framework have been proposed to achieve the goal of generating more abstractive summaries by learning to map input text to output text.
