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Found 11 papers, 9 papers with code
EmRel: Joint Representation of Entities and Embedded Relations for Multi-triple Extraction
Multi-triple extraction is a challenging task due to the existence of informative inter-triple correlations, and consequently rich interactions across the constituent entities and relations. While existing works only explore entity representations, we propose to explicitly introduce relation representation, jointly represent it with entities, and novelly align them to identify valid triples. We perform comprehensive experiments on document-level relation extraction and joint entity and relation extraction along with ablations to demonstrate the advantage of the proposed method.
Document-level Relation Extraction
Joint Entity and Relation Extraction
+2
Benchmarking Large Language Models on Controllable Generation under Diversified Instructions
While large language models (LLMs) have exhibited impressive instruction-following capabilities, it is still unclear whether and to what extent they can respond to explicit constraints that might be entailed in various instructions.
On the Calibration of Large Language Models and Alignment
As large language models attract increasing attention and find widespread application, concurrent challenges of reliability also arise at the same time.
Self-Evolved Diverse Data Sampling for Efficient Instruction Tuning
The key to our data sampling technique lies in the enhancement of diversity in the chosen subsets, as the model selects new data points most distinct from any existing ones according to its current embedding space.
Qwen Technical Report
Large language models (LLMs) have revolutionized the field of artificial intelligence, enabling natural language processing tasks that were previously thought to be exclusive to humans.
Ranked #3 on
Multi-Label Text Classification
on CC3M-TagMask
ExpertPrompting: Instructing Large Language Models to be Distinguished Experts
The answering quality of an aligned large language model (LLM) can be drastically improved if treated with proper crafting of prompts.
$k$NN Prompting: Beyond-Context Learning with Calibration-Free Nearest Neighbor Inference
In-Context Learning (ICL), which formulates target tasks as prompt completion conditioned on in-context demonstrations, has become the prevailing utilization of LLMs.
UniRel: Unified Representation and Interaction for Joint Relational Triple Extraction
Relational triple extraction is challenging for its difficulty in capturing rich correlations between entities and relations.
Ranked #1 on
Relation Extraction
on WebNLG
Building Chinese Biomedical Language Models via Multi-Level Text Discrimination
In this work we introduce eHealth, a Chinese biomedical PLM built from scratch with a new pre-training framework.
Entity Structure Within and Throughout: Modeling Mention Dependencies for Document-Level Relation Extraction
Our experiments demonstrate the usefulness of the proposed entity structure and the effectiveness of SSAN.
Ranked #3 on
Relation Extraction
on DocRED
Curriculum Learning for Natural Language Understanding
With the great success of pre-trained language models, the pretrain-finetune paradigm now becomes the undoubtedly dominant solution for natural language understanding (NLU) tasks.
