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Found 35 papers, 14 papers with code
An Empirical Study on Multiple Information Sources for Zero-Shot Fine-Grained Entity Typing
Auxiliary information from multiple sources has been demonstrated to be effective in zero-shot fine-grained entity typing (ZFET).
Fine-grained Entity Typing without Knowledge Base
Existing work on Fine-grained Entity Typing (FET) typically trains automatic models on the datasets obtained by using Knowledge Bases (KB) as distant supervision.
Learning from Sibling Mentions with Scalable Graph Inference in Fine-Grained Entity Typing
In this paper, we firstly empirically find that existing models struggle to handle hard mentions due to their insufficient contexts, which consequently limits their overall typing performance.
Empowering Users in Digital Privacy Management through Interactive LLM-Based Agents
This paper presents a novel application of large language models (LLMs) to enhance user comprehension of privacy policies through an interactive dialogue agent.
GLBench: A Comprehensive Benchmark for Graph with Large Language Models
To bridge this gap, we introduce GLBench, the first comprehensive benchmark for evaluating GraphLLM methods in both supervised and zero-shot scenarios.
CORM: Cache Optimization with Recent Message for Large Language Model Inference
Our validation shows that CORM reduces the inference memory usage of KV cache by up to 70\% with negligible performance degradation across six tasks in LongBench.
MIKE: A New Benchmark for Fine-grained Multimodal Entity Knowledge Editing
Multimodal knowledge editing represents a critical advancement in enhancing the capabilities of Multimodal Large Language Models (MLLMs).
MR-GSM8K: A Meta-Reasoning Benchmark for Large Language Model Evaluation
In this work, we introduce a novel evaluation paradigm for Large Language Models (LLMs) that compels them to transition from a traditional question-answering role, akin to a student, to a solution-scoring role, akin to a teacher.
When Graph Data Meets Multimodal: A New Paradigm for Graph Understanding and Reasoning
Graph data is ubiquitous in the physical world, and it has always been a challenge to efficiently model graph structures using a unified paradigm for the understanding and reasoning on various graphs.
StrategyLLM: Large Language Models as Strategy Generators, Executors, Optimizers, and Evaluators for Problem Solving
Most existing prompting methods suffer from the issues of generalizability and consistency, as they often rely on instance-specific solutions that may not be applicable to other instances and lack task-level consistency across the selected few-shot examples.
Hint-enhanced In-Context Learning wakes Large Language Models up for knowledge-intensive tasks
In-context learning (ICL) ability has emerged with the increasing scale of large language models (LLMs), enabling them to learn input-label mappings from demonstrations and perform well on downstream tasks.
A Benchmark for Text Expansion: Datasets, Metrics, and Baselines
This work presents a new task of Text Expansion (TE), which aims to insert fine-grained modifiers into proper locations of the plain text to concretize or vivify human writings.
Towards Visual Taxonomy Expansion
Specifically, on the Chinese taxonomy dataset, our method significantly improves accuracy by 8. 75 %.
DoG-Instruct: Towards Premium Instruction-Tuning Data via Text-Grounded Instruction Wrapping
In particular, compared to the best-performing baseline, the LLM trained using our generated dataset exhibits a 10\% relative improvement in performance on AlpacaEval, despite utilizing only 1/5 of its training data.
Disco-Bench: A Discourse-Aware Evaluation Benchmark for Language Modelling
Modeling discourse -- the linguistic phenomena that go beyond individual sentences, is a fundamental yet challenging aspect of natural language processing (NLP).
Sen2Pro: A Probabilistic Perspective to Sentence Embedding from Pre-trained Language Model
Sentence embedding is one of the most fundamental tasks in Natural Language Processing and plays an important role in various tasks.
Frequency-aware Dimension Selection for Static Word Embedding by Mixed Product Distance
Static word embedding is still useful, particularly for context-unavailable tasks, because in the case of no context available, pre-trained language models often perform worse than static word embeddings.
Exploring the Use of Large Language Models for Reference-Free Text Quality Evaluation: An Empirical Study
Evaluating the quality of generated text is a challenging task in NLP, due to the inherent complexity and diversity of text.
Is ChatGPT A Good Keyphrase Generator? A Preliminary Study
Based on our findings, we conclude that ChatGPT has great potential for keyphrase generation.
TextShield: Beyond Successfully Detecting Adversarial Sentences in Text Classification
A recent line of work, detection-based defense, aims to distinguish adversarial sentences from benign ones.
Zero-Shot Rumor Detection with Propagation Structure via Prompt Learning
The spread of rumors along with breaking events seriously hinders the truth in the era of social media.
Large Language Models Meet Harry Potter: A Bilingual Dataset for Aligning Dialogue Agents with Characters
In this paper, we introduce the Harry Potter Dialogue (HPD) dataset, designed to advance the study of dialogue agents and character alignment.
Effidit: Your AI Writing Assistant
In Effidit, we significantly expand the capacities of a writing assistant by providing functions in five categories: text completion, error checking, text polishing, keywords to sentences (K2S), and cloud input methods (cloud IME).
FL-Tuning: Layer Tuning for Feed-Forward Network in Transformer
Specifically, we focus on layer tuning for feed-forward network in the Transformer, namely FL-tuning.
Context Enhanced Short Text Matching using Clickthrough Data
The short text matching task employs a model to determine whether two short texts have the same semantic meaning or intent.
On the Evaluation Metrics for Paraphrase Generation
In this paper we revisit automatic metrics for paraphrase evaluation and obtain two findings that disobey conventional wisdom: (1) Reference-free metrics achieve better performance than their reference-based counterparts.
Rethink the Evaluation for Attack Strength of Backdoor Attacks in Natural Language Processing
It has been shown that natural language processing (NLP) models are vulnerable to a kind of security threat called the Backdoor Attack, which utilizes a `backdoor trigger' paradigm to mislead the models.
TexSmart: A System for Enhanced Natural Language Understanding
This paper introduces TexSmart, a text understanding system that supports fine-grained named entity recognition (NER) and enhanced semantic analysis functionalities.
A Question-answering Based Framework for Relation Extraction Validation
However, few works have focused on how to validate and correct the results generated by the existing relation extraction models.
TexSmart: A Text Understanding System for Fine-Grained NER and Enhanced Semantic Analysis
This technique report introduces TexSmart, a text understanding system that supports fine-grained named entity recognition (NER) and enhanced semantic analysis functionalities.
InSRL: A Multi-view Learning Framework Fusing Multiple Information Sources for Distantly-supervised Relation Extraction
We see information sources as multiple views and fusing them to construct an intact space with sufficient information.
Complex Relation Extraction: Challenges and Opportunities
In recent years, many complex relation extraction tasks, i. e., the variants of simple binary relation extraction, are proposed to meet the complex applications in practice.
Ensuring Readability and Data-fidelity using Head-modifier Templates in Deep Type Description Generation
A type description is a succinct noun compound which helps human and machines to quickly grasp the informative and distinctive information of an entity.
Deep Short Text Classification with Knowledge Powered Attention
For the purpose of measuring the importance of knowledge, we introduce attention mechanisms and propose deep Short Text Classification with Knowledge powered Attention (STCKA).
