Search Results for author:
Found 18 papers, 7 papers with code
Towards Making the Most of Cross-Lingual Transfer for Zero-Shot Neural Machine Translation
When applied to zero-shot cross-lingual abstractive summarization, it produces an average performance gain of 12. 3 ROUGE-L over mBART-ft. We conduct detailed analyses to understand the key ingredients of SixT+, including multilinguality of the auxiliary parallel data, positional disentangled encoder, and the cross-lingual transferability of its encoder.
Abstractive Text Summarization
Cross-Lingual Abstractive Summarization
+5
Unlocking Multi-View Insights in Knowledge-Dense Retrieval-Augmented Generation
While Retrieval-Augmented Generation (RAG) plays a crucial role in the application of Large Language Models (LLMs), existing retrieval methods in knowledge-dense domains like law and medicine still suffer from a lack of multi-perspective views, which are essential for improving interpretability and reliability.
Tastle: Distract Large Language Models for Automatic Jailbreak Attack
In this work, we propose Tastle, a novel black-box jailbreak framework for automated red teaming of LLMs.
Self-DC: When to retrieve and When to generate? Self Divide-and-Conquer for Compositional Unknown Questions
Retrieve-then-read and generate-then-read are two typical solutions to handle unknown and known questions in open-domain question-answering, while the former retrieves necessary external knowledge and the later prompt the large language models to generate internal known knowledge encoded in the parameters.
StyleBART: Decorate Pretrained Model with Style Adapters for Unsupervised Stylistic Headline Generation
Stylistic headline generation is the task to generate a headline that not only summarizes the content of an article, but also reflects a desired style that attracts users.
Evaluating Explanation Methods for Vision-and-Language Navigation
The ability to navigate robots with natural language instructions in an unknown environment is a crucial step for achieving embodied artificial intelligence (AI).
TADIS: Steering Models for Deep-Thinking about Demonstration Examples
To alleviate the illusion of competence of models, we first ask the model to verify the correctness of shown examples.
mCLIP: Multilingual CLIP via Cross-lingual Transfer
Furthermore, to enhance the token- and sentence-level multilingual representation of the MTE, we propose to train it with machine translation and contrastive learning jointly before the TriKD to provide a better initialization.
Multilingual Sentence Transformer as A Multilingual Word Aligner
In this paper, we investigate whether multilingual sentence Transformer LaBSE is a strong multilingual word aligner.
Predictions of photophysical properties of phosphorescent platinum(II) complexes based on ensemble machine learning approach
Here, we develop a general protocol for accurate predictions of emission wavelength, radiative decay rate constant, and PL quantum yield for phosphorescent Pt(II) emitters based on the combination of first-principles quantum mechanical method, machine learning (ML) and experimental calibration.
Meta-analysis of individualized treatment rules via sign-coherency
Medical treatments tailored to a patient's baseline characteristics hold the potential of improving patient outcomes while reducing negative side effects.
Policy Learning for Optimal Individualized Dose Intervals
In this paper, we propose a new method to estimate such an optimal dose interval, named probability dose interval (PDI).
Towards Making the Most of Multilingual Pretraining for Zero-Shot Neural Machine Translation
When applied to zero-shot cross-lingual abstractive summarization, it produces an average performance gain of 12. 3 ROUGE-L over mBART-ft. We conduct detailed analyses to understand the key ingredients of SixT+, including multilinguality of the auxiliary parallel data, positional disentangled encoder, and the cross-lingual transferability of its encoder.
Abstractive Text Summarization
Cross-Lingual Abstractive Summarization
+5
Robust Sample Weighting to Facilitate Individualized Treatment Rule Learning for a Target Population
The aim of this paper is to develop a weighting framework to mitigate the impact of such misspecification and thus facilitate the generalizability of optimal ITRs from a source population to a target population.
Zero-shot Cross-lingual Transfer of Neural Machine Translation with Multilingual Pretrained Encoders
In this paper, we focus on a zero-shot cross-lingual transfer task in NMT.
Accurate Word Alignment Induction from Neural Machine Translation
Shift-Att is an interpretation method that induces alignments from the attention weights of Transformer and does not require parameter update or architecture change.
Constructing Stabilized Dynamic Treatment Regimes
We propose a new method termed stabilized O-learning for deriving stabilized dynamic treatment regimes, which are sequential decision rules for individual patients that not only adapt over the course of the disease progression but also remain consistent over time in format.
Methodology
Biclustering Via Sparse Clustering
This framework can be used to identify biclusters that differ with respect to the means of the features, the variance of the features, or more general differences.
