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Found 33 papers, 13 papers with code
Enhancing Confidence Expression in Large Language Models Through Learning from Past Experience
Specifically, we first identify three key problems: (1) How to capture the inherent confidence of the LLM?
Improving Recall of Large Language Models: A Model Collaboration Approach for Relational Triple Extraction
The framework includes an evaluation model that can extract related entity pairs with high precision.
ToNER: Type-oriented Named Entity Recognition with Generative Language Model
In recent years, the fine-tuned generative models have been proven more powerful than the previous tagging-based or span-based models on named entity recognition (NER) task.
Large Language Model Can Continue Evolving From Mistakes
Continual Learning (CL) is a commonly used method to address this issue.
Reason from Fallacy: Enhancing Large Language Models' Logical Reasoning through Logical Fallacy Understanding
Large Language Models (LLMs) have demonstrated good performance in many reasoning tasks, but they still struggle with some complicated reasoning tasks including logical reasoning.
Is There a One-Model-Fits-All Approach to Information Extraction? Revisiting Task Definition Biases
Definition bias is a negative phenomenon that can mislead models.
Laying the Foundation First? Investigating the Generalization from Atomic Skills to Complex Reasoning Tasks
In our experiments, we find that atomic skills can not spontaneously generalize to compositional tasks.
Small Language Model Can Self-correct
In this paper, we introduce the \underline{I}ntrinsic \underline{S}elf-\underline{C}orrection (ISC) in generative language models, aiming to correct the initial output of LMs in a self-triggered manner, even for those small LMs with 6 billion parameters.
ConcEPT: Concept-Enhanced Pre-Training for Language Models
In this paper, we propose ConcEPT, which stands for Concept-Enhanced Pre-Training for language models, to infuse conceptual knowledge into PLMs.
Enhancing Quantitative Reasoning Skills of Large Language Models through Dimension Perception
Hence, we present a framework to enhance the quantitative reasoning ability of language models based on dimension perception.
Source Prompt: Coordinated Pre-training of Language Models on Diverse Corpora from Multiple Sources
Pre-trained language models (PLMs) have established the new paradigm in the field of NLP.
Can Large Language Models Understand Real-World Complex Instructions?
To bridge this gap, we propose CELLO, a benchmark for evaluating LLMs' ability to follow complex instructions systematically.
Towards Visual Taxonomy Expansion
Specifically, on the Chinese taxonomy dataset, our method significantly improves accuracy by 8. 75 %.
KnowledGPT: Enhancing Large Language Models with Retrieval and Storage Access on Knowledge Bases
Large language models (LLMs) have demonstrated impressive impact in the field of natural language processing, but they still struggle with several issues regarding, such as completeness, timeliness, faithfulness and adaptability.
Adaptive Ordered Information Extraction with Deep Reinforcement Learning
Information extraction (IE) has been studied extensively.
HAUSER: Towards Holistic and Automatic Evaluation of Simile Generation
Similes play an imperative role in creative writing such as story and dialogue generation.
ANALOGYKB: Unlocking Analogical Reasoning of Language Models with A Million-scale Knowledge Base
Analogical reasoning is a fundamental cognitive ability of humans.
Causality-aware Concept Extraction based on Knowledge-guided Prompting
The prompt adopts the topic of the given entity from the existing knowledge in KGs to mitigate the spurious co-occurrence correlations between entities and biased concepts.
GANTEE: Generative Adversatial Network for Taxonomy Entering Evaluation
The previous methods suffer from low-efficiency since they waste much time when most of the new coming concepts are indeed noisy concepts.
Sem4SAP: Synonymous Expression Mining From Open Knowledge Graph For Language Model Synonym-Aware Pretraining
The model's ability to understand synonymous expression is crucial in many kinds of downstream tasks.
BBT-Fin: Comprehensive Construction of Chinese Financial Domain Pre-trained Language Model, Corpus and Benchmark
Our aim is to facilitate research in the development of NLP within the Chinese financial domain.
MAPS-KB: A Million-scale Probabilistic Simile Knowledge Base
The ability to understand and generate similes is an imperative step to realize human-level AI.
Generative Entity Typing with Curriculum Learning
To overcome these drawbacks, we propose a novel generative entity typing (GET) paradigm: given a text with an entity mention, the multiple types for the role that the entity plays in the text are generated with a pre-trained language model (PLM).
Large-scale Multi-granular Concept Extraction Based on Machine Reading Comprehension
In order to supply existing KGs with more fine-grained and new concepts, we propose a novel concept extraction framework, namely MRC-CE, to extract large-scale multi-granular concepts from the descriptive texts of entities.
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.
Language Models as Knowledge Embeddings
In this paper, we propose LMKE, which adopts Language Models to derive Knowledge Embeddings, aiming at both enriching representations of long-tail entities and solving problems of prior description-based methods.
Ranked #3 on
Link Prediction
on WN18RR
Tackling Math Word Problems with Fine-to-Coarse Abstracting and Reasoning
Math Word Problems (MWP) is an important task that requires the ability of understanding and reasoning over mathematical text.
Rule Mining over Knowledge Graphs via Reinforcement Learning
Knowledge graphs (KGs) are an important source repository for a wide range of applications and rule mining from KGs recently attracts wide research interest in the KG-related research community.
Semantic-based Data Augmentation for Math Word Problems
In this paper, we propose a set of novel data augmentation approaches to supplement existing datasets with such data that are augmented with different kinds of local variances, and help to improve the generalization ability of current neural models.
Revisiting the Negative Data of Distantly Supervised Relation Extraction
Next, we formulate the problem of relation extraction into as a positive unlabeled learning task to alleviate false negative problem.
Ranked #1 on
Relation Extraction
on NYT11-HRL
Collective Loss Function for Positive and Unlabeled Learning
On the contrary, traditional machine learning algorithms often rely on negative examples, otherwise the model would be prone to collapse and always-true predictions.
CN-Probase: A Data-driven Approach for Large-scale Chinese Taxonomy Construction
Taxonomies play an important role in machine intelligence.
