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Found 29 papers, 12 papers with code
COPNER: Contrastive Learning with Prompt Guiding for Few-shot Named Entity Recognition
Second, the referents may be far from representing corresponding entity classes due to the label scarcity in the few-shot setting.
ESGSenticNet: A Neurosymbolic Knowledge Base for Corporate Sustainability Analysis
Evaluating corporate sustainability performance is essential to drive sustainable business practices, amid the need for a more sustainable economy.
AntiLeak-Bench: Preventing Data Contamination by Automatically Constructing Benchmarks with Updated Real-World Knowledge
Data contamination hinders fair LLM evaluation by introducing test data into newer models' training sets.
VidComposition: Can MLLMs Analyze Compositions in Compiled Videos?
The advancement of Multimodal Large Language Models (MLLMs) has enabled significant progress in multimodal understanding, expanding their capacity to analyze video content.
Discriminative Pedestrian Features and Gated Channel Attention for Clothes-Changing Person Re-Identification
In public safety and social life, the task of Clothes-Changing Person Re-Identification (CC-ReID) has become increasingly significant.
Can a large language model be a gaslighter?
In contrast, we advanced three safety alignment strategies to strengthen (by 12. 05%) the safety guardrail of LLMs.
Exploring Loss Landscapes through the Lens of Spin Glass Theory
Specifically, we used (1) random walk in the parameter space of DNNs to unravel the structures in their loss landscape; (2) a permutation-interpolation protocol to study the connection between copies of identical regions in the loss landscape due to the permutation symmetry in the hidden layers; (3) hierarchical clustering to reveal the hierarchy among trained solutions of DNNs, reminiscent of the so-called Replica Symmetry Breaking (RSB) phenomenon (i. e. the Parisi solution) in spin glass; (4) finally, we examine the relationship between the ruggedness of DNN's loss landscape and its generalizability, showing an improvement of flattened minima.
Explainable Natural Language Processing for Corporate Sustainability Analysis
Sustainability commonly refers to entities, such as individuals, companies, and institutions, having a non-detrimental (or even positive) impact on the environment, society, and the economy.
Re-Search for The Truth: Multi-round Retrieval-augmented Large Language Models are Strong Fake News Detectors
The proliferation of fake news has had far-reaching implications on politics, the economy, and society at large.
BClean: A Bayesian Data Cleaning System
By evaluating on both real-world and synthetic datasets, we demonstrate that BClean is capable of achieving an F-measure of up to 0. 9 in data cleaning, outperforming existing Bayesian methods by 2% and other data cleaning methods by 15%.
A Survey on Semantic Processing Techniques
We connect the surveyed tasks with downstream applications because this may inspire future scholars to fuse these low-level semantic processing tasks with high-level natural language processing tasks.
A Survey of Large Language Models for Healthcare: from Data, Technology, and Applications to Accountability and Ethics
Secondly, we conduct a comparison between the previous PLMs and the latest LLMs, as well as comparing various LLMs with each other.
Self-Convinced Prompting: Few-Shot Question Answering with Repeated Introspection
While large language models (LLMs) such as ChatGPT and PaLM have demonstrated remarkable performance in various language understanding and generation tasks, their capabilities in complex reasoning and intricate knowledge utilization still fall short of human-level proficiency.
A Comprehensive Review on Financial Explainable AI
The success of artificial intelligence (AI), and deep learning models in particular, has led to their widespread adoption across various industries due to their ability to process huge amounts of data and learn complex patterns.
A Wide Evaluation of ChatGPT on Affective Computing Tasks
In this work, we widely study the capabilities of the ChatGPT models, namely GPT-4 and GPT-3. 5, on 13 affective computing problems, namely aspect extraction, aspect polarity classification, opinion extraction, sentiment analysis, sentiment intensity ranking, emotions intensity ranking, suicide tendency detection, toxicity detection, well-being assessment, engagement measurement, personality assessment, sarcasm detection, and subjectivity detection.
GPTEval: A Survey on Assessments of ChatGPT and GPT-4
The emergence of ChatGPT has generated much speculation in the press about its potential to disrupt social and economic systems.
Finding the Pillars of Strength for Multi-Head Attention
Specifically, the heads of MHA were originally designed to attend to information from different representation subspaces, whereas prior studies found that some attention heads likely learn similar features and can be pruned without harming performance.
Logical Reasoning over Natural Language as Knowledge Representation: A Survey
This paper provides a comprehensive overview on a new paradigm of logical reasoning, which uses natural language as knowledge representation and pretrained language models as reasoners, including philosophical definition and categorization of logical reasoning, advantages of the new paradigm, benchmarks and methods, challenges of the new paradigm, possible future directions, and relation to related NLP fields.
Hierarchical Attention Network for Explainable Depression Detection on Twitter Aided by Metaphor Concept Mappings
Automatic depression detection on Twitter can help individuals privately and conveniently understand their mental health status in the early stages before seeing mental health professionals.
Interpreting Verbal Metaphors by Paraphrasing
Metaphorical expressions are difficult linguistic phenomena, challenging diverse Natural Language Processing tasks.
Combining Pre-trained Word Embeddings and Linguistic Features for Sequential Metaphor Identification
The pre-trained word embeddings GloVe, ELMo and BERT have individually shown good performance on sequential metaphor identification.
Accelerating Federated Learning over Reliability-Agnostic Clients in Mobile Edge Computing Systems
In this paper, a multi-layer federated learning protocol called HybridFL is designed for the MEC architecture.
Consistent and Flexible Selectivity Estimation for High-Dimensional Data
Selectivity estimation aims at estimating the number of database objects that satisfy a selection criterion.
On Random Walk Based Graph Sampling
In this paper, we first present a comprehensive analysis of the drawbacks of three widely-used random walk based graph sampling algorithms, called re-weighted random walk (RW) algorithm, Metropolis-Hastings random walk (MH) algorithm and maximum-degree random walk (MD) algorithm.
A Stable Variational Autoencoder for Text Modelling
Variational Autoencoder (VAE) is a powerful method for learning representations of high-dimensional data.
SAFA: a Semi-Asynchronous Protocol for Fast Federated Learning with Low Overhead
Federated learning (FL) has attracted increasing attention as a promising approach to driving a vast number of end devices with artificial intelligence.
End-to-End Sequential Metaphor Identification Inspired by Linguistic Theories
End-to-end training with Deep Neural Networks (DNN) is a currently popular method for metaphor identification.
Word Embedding and WordNet Based Metaphor Identification and Interpretation
Metaphoric expressions are widespread in natural language, posing a significant challenge for various natural language processing tasks such as Machine Translation.
ABDN at SemEval-2018 Task 10: Recognising Discriminative Attributes using Context Embeddings and WordNet
This paper describes the system that we submitted for SemEval-2018 task 10: capturing discriminative attributes.
