Search Results for author:
Found 31 papers, 11 papers with code
A Contrastive Framework for Learning Sentence Representations from Pairwise and Triple-wise Perspective in Angular Space
Learning high-quality sentence representations is a fundamental problem of natural language processing which could benefit a wide range of downstream tasks.
mChartQA: A universal benchmark for multimodal Chart Question Answer based on Vision-Language Alignment and Reasoning
In the fields of computer vision and natural language processing, multimodal chart question-answering, especially involving color, structure, and textless charts, poses significant challenges.
Monotonic Paraphrasing Improves Generalization of Language Model Prompting
In this paper, we propose monotonic paraphrasing (MonoPara), an end-to-end decoding strategy that paraphrases given prompts or instructions into their lower perplexity counterparts based on an ensemble of a paraphrase LM for prompt (or instruction) rewriting, and a target LM (i. e. the prompt or instruction executor) that constrains the generation for lower perplexity.
YAYI-UIE: A Chat-Enhanced Instruction Tuning Framework for Universal Information Extraction
The difficulty of the information extraction task lies in dealing with the task-specific label schemas and heterogeneous data structures.
YAYI 2: Multilingual Open-Source Large Language Models
As the latest advancements in natural language processing, large language models (LLMs) have achieved human-level language understanding and generation abilities in many real-world tasks, and even have been regarded as a potential path to the artificial general intelligence.
Rational Sensibility: LLM Enhanced Empathetic Response Generation Guided by Self-presentation Theory
Having the ability to empathize is crucial for accurately representing human behavior during conversations.
MMA-Diffusion: MultiModal Attack on Diffusion Models
In recent years, Text-to-Image (T2I) models have seen remarkable advancements, gaining widespread adoption.
Cognitive Overload: Jailbreaking Large Language Models with Overloaded Logical Thinking
While large language models (LLMs) have demonstrated increasing power, they have also given rise to a wide range of harmful behaviors.
Dense Retrieval as Indirect Supervision for Large-space Decision Making
Many discriminative natural language understanding (NLU) tasks have large label spaces.
Evaluating Large Language Models on Controlled Generation Tasks
While recent studies have looked into the abilities of large language models in various benchmark tasks, including question generation, reading comprehension, multilingual and etc, there have been few studies looking into the controllability of large language models on generation tasks.
AutoDAN: Generating Stealthy Jailbreak Prompts on Aligned Large Language Models
In light of these challenges, we intend to answer this question: Can we develop an approach that can automatically generate stealthy jailbreak prompts?
Improving Zero-Shot Generalization for CLIP with Synthesized Prompts
Thereafter, we fine-tune CLIP with off-the-shelf methods by combining labeled and synthesized features.
CEO: Corpus-based Open-Domain Event Ontology Induction
Existing event-centric NLP models often only apply to the pre-defined ontology, which significantly restricts their generalization capabilities.
Look-back Decoding for Open-Ended Text Generation
Given a prefix (context), open-ended generation aims to decode texts that are coherent, which do not abruptly drift from previous topics, and informative, which do not suffer from undesired repetitions.
CAMEL: Curvature-Augmented Manifold Embedding and Learning
CAMEL utilizes a topology metric defined on the Riemannian manifold, and a unique Riemannian metric for both distance and curvature to enhance its expressibility.
Does Your Model Classify Entities Reasonably? Diagnosing and Mitigating Spurious Correlations in Entity Typing
Due to shortcuts from surface patterns to annotated entity labels and biased training, existing entity typing models are subject to the problem of spurious correlations.
Smart Tracking Tray System for A Smart and Sustainable Wet Lab Community
Positive feedback has been collected from pilot tests in several labs.
Functional Connectivity of the Brain Across Rodents and Humans
This review begins by examining similarities and differences in anatomical features, acquisition parameters, and preprocessing techniques, as factors that contribute to functional connectivity.
A Multi-scale Time-series Dataset with Benchmark for Machine Learning in Decarbonized Energy Grids
The electric grid is a key enabling infrastructure for the ambitious transition towards carbon neutrality as we grapple with climate change.
AnANet: Modeling Association and Alignment for Cross-modal Correlation Classification
Thus researchers study the definition of cross-modal correlation category and construct various classification systems and predictive models.
A Direct Slip Ratio Estimation Method based on an Intelligent Tire and Machine Learning
The proposed estimation model is able to estimate the slip ratio continuously and stably using only the acceleration from the intelligent tire system, and the estimated slip ratio range can reach 30%.
MIMIC-IF: Interpretability and Fairness Evaluation of Deep Learning Models on MIMIC-IV Dataset
The recent release of large-scale healthcare datasets has greatly propelled the research of data-driven deep learning models for healthcare applications.
An Examination of Preference-based Reinforcement Learning for Treatment Recommendation
Treatment recommendation is a complex multi-faceted problem with many conflicting objectives, e. g., optimizing the survival rate (or expected lifetime), mitigating negative impacts, reducing financial expenses and time costs, avoiding over-treatment, etc.
Differentially Private Adversarial Robustness Through Randomized Perturbations
Deep Neural Networks, despite their great success in diverse domains, are provably sensitive to small perturbations on correctly classified examples and lead to erroneous predictions.
Lateral Force Prediction using Gaussian Process Regression for Intelligent Tire Systems
It is delineated that the proposed intelligent tire system can provide reliable information about the tire-road interactions even in the case of high slip angles.
Reasoning with Multimodal Sarcastic Tweets via Modeling Cross-Modality Contrast and Semantic Association
In multimodal context, sarcasm is no longer a pure linguistic phenomenon, and due to the nature of social media short text, the opposite is more often manifested via cross-modality expressions.
Modeling Conversation Structure and Temporal Dynamics for Jointly Predicting Rumor Stance and Veracity
The bottom component of our framework classifies the stances of tweets in a conversation discussing a rumor via modeling the structural property based on a novel graph convolutional network.
VASPKIT: A User-friendly Interface Facilitating High-throughput Computing and Analysis Using VASP Code
The executable versions of VASPKIT and the related examples, together with the tutorials, are available in its official website vaspkit. com.
Materials Science
Diagnosing Reinforcement Learning for Traffic Signal Control
In this paper, we propose to re-examine the RL approaches through the lens of classic transportation theory.
CoLight: Learning Network-level Cooperation for Traffic Signal Control
To enable cooperation of traffic signals, in this paper, we propose a model, CoLight, which uses graph attentional networks to facilitate communication.
Joint Adaptive Neighbours and Metric Learning for Multi-view Subspace Clustering
In this method, we use the subspace representations of different views to adaptively learn a consensus similarity matrix, uncovering the subspace structure and avoiding noisy nature of original data.
