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Found 28 papers, 13 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
+6
Understanding Particles From Video: Property Estimation of Granular Materials via Visuo-Haptic Learning
The generalization capability has also been evaluated and a real-world application on the beach is also demonstrated.
Compound-QA: A Benchmark for Evaluating LLMs on Compound Questions
In this paper, we introduce Compound Question Synthesis (CQ-Syn) to create the Compound-QA benchmark, focusing on compound questions with multiple sub-questions.
MCCE: Missingness-aware Causal Concept Explainer
We conduct validations using a real-world dataset, demonstrating that MCCE achieves promising performance compared to state-of-the-art explanation methods in causal concept effect estimation.
Position Paper On Diagnostic Uncertainty Estimation from Large Language Models: Next-Word Probability Is Not Pre-test Probability
Large language models (LLMs) are being explored for diagnostic decision support, yet their ability to estimate pre-test probabilities, vital for clinical decision-making, remains limited.
ChineseSafe: A Chinese Benchmark for Evaluating Safety in Large Language Models
While previous works have introduced several benchmarks to evaluate the safety risk of LLMs, the community still has a limited understanding of current LLMs' capability to recognize illegal and unsafe content in Chinese contexts.
FANNO: Augmenting High-Quality Instruction Data with Open-Sourced LLMs Only
Instruction fine-tuning stands as a crucial advancement in leveraging large language models (LLMs) for enhanced task performance.
SoP: Unlock the Power of Social Facilitation for Automatic Jailbreak Attack
The widespread applications of large language models (LLMs) have brought about concerns regarding their potential misuse.
SeTAR: Out-of-Distribution Detection with Selective Low-Rank Approximation
In this work, we propose SeTAR, a novel, training-free OOD detection method that leverages selective low-rank approximation of weight matrices in vision-language and vision-only models.
Out-of-Distribution Detection
Out of Distribution (OOD) Detection
MiLoRA: Harnessing Minor Singular Components for Parameter-Efficient LLM Finetuning
It is observed that the minor matrix corresponds to the noisy or long-tail information, while the principal matrix contains important knowledge.
A Two-Stage Prediction-Aware Contrastive Learning Framework for Multi-Intent NLU
Multi-intent natural language understanding (NLU) presents a formidable challenge due to the model confusion arising from multiple intents within a single utterance.
Ranked #2 on
Slot Filling
on MixSNIPS
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.
Distract Large Language Models for Automatic Jailbreak Attack
Extensive efforts have been made before the public release of Large language models (LLMs) to align their behaviors with human values.
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).
PACIT: Unlocking the Power of Examples for Better In-Context Instruction Tuning
Instruction tuning enhances the instruction following ability of large language models by finetuning with supervised instruction data.
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
+6
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.
