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Found 19 papers, 15 papers with code
EasyInstruct: An Easy-to-use Instruction Processing Framework for Large Language Models
In recent years, instruction tuning has gained increasing attention and emerged as a crucial technique to enhance the capabilities of Large Language Models (LLMs).
OceanGPT: A Large Language Model for Ocean Science Tasks
Ocean science, which delves into the oceans that are reservoirs of life and biodiversity, is of great significance given that oceans cover over 70% of our planet's surface.
When Do Program-of-Thoughts Work for Reasoning?
Although there are effective methods like program-of-thought prompting for LLMs which uses programming language to tackle complex reasoning tasks, the specific impact of code data on the improvement of reasoning capabilities remains under-explored.
CodeKGC: Code Language Model for Generative Knowledge Graph Construction
However, large generative language model trained on structured data such as code has demonstrated impressive capability in understanding natural language for structural prediction and reasoning tasks.
Tele-Knowledge Pre-training for Fault Analysis
In this work, we share our experience on tele-knowledge pre-training for fault analysis, a crucial task in telecommunication applications that requires a wide range of knowledge normally found in both machine log data and product documents.
Multi-modal Protein Knowledge Graph Construction and Applications
Existing data-centric methods for protein science generally cannot sufficiently capture and leverage biology knowledge, which may be crucial for many protein tasks.
Relphormer: Relational Graph Transformer for Knowledge Graph Representations
To this end, we propose a new variant of Transformer for knowledge graph representations dubbed Relphormer.
Ranked #3 on
Link Prediction
on FB15k-237
OntoProtein: Protein Pretraining With Gene Ontology Embedding
We construct a novel large-scale knowledge graph that consists of GO and its related proteins, and gene annotation texts or protein sequences describe all nodes in the graph.
Improving Knowledge Graph Representation Learning by Structure Contextual Pre-training
Representation learning models for Knowledge Graphs (KG) have proven to be effective in encoding structural information and performing reasoning over KGs.
Learning to Ask for Data-Efficient Event Argument Extraction
Event argument extraction (EAE) is an important task for information extraction to discover specific argument roles.
Differentiable Prompt Makes Pre-trained Language Models Better Few-shot Learners
Large-scale pre-trained language models have contributed significantly to natural language processing by demonstrating remarkable abilities as few-shot learners.
Ranked #1 on
Few-Shot Learning
on CR
CBLUE: A Chinese Biomedical Language Understanding Evaluation Benchmark
Artificial Intelligence (AI), along with the recent progress in biomedical language understanding, is gradually changing medical practice.
Ranked #1 on
Semantic Similarity
on CHIP-STS
Interventional Aspect-Based Sentiment Analysis
Recent neural-based aspect-based sentiment analysis approaches, though achieving promising improvement on benchmark datasets, have reported suffering from poor robustness when encountering confounder such as non-target aspects.
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
Disentangled Contrastive Learning for Learning Robust Textual Representations
Although the self-supervised pre-training of transformer models has resulted in the revolutionizing of natural language processing (NLP) applications and the achievement of state-of-the-art results with regard to various benchmarks, this process is still vulnerable to small and imperceptible permutations originating from legitimate inputs.
Text-guided Legal Knowledge Graph Reasoning
In this paper, we propose a novel legal application of legal provision prediction (LPP), which aims to predict the related legal provisions of affairs.
Normal vs. Adversarial: Salience-based Analysis of Adversarial Samples for Relation Extraction
Recent neural-based relation extraction approaches, though achieving promising improvement on benchmark datasets, have reported their vulnerability towards adversarial attacks.
OpenUE: An Open Toolkit of Universal Extraction from Text
We introduce a prototype model and provide an open-source and extensible toolkit called OpenUE for various extraction tasks.
Ranked #3 on
Joint Entity and Relation Extraction
on WebNLG
On Robustness and Bias Analysis of BERT-based Relation Extraction
Fine-tuning pre-trained models have achieved impressive performance on standard natural language processing benchmarks.
Out-of-Time-Order Correlation in Marginal Many-Body Localized Systems
We show that the out-of-time-order correlation (OTOC) $ \langle W(t)^\dagger V(0)^\dagger W(t)V(0)\rangle$ in many-body localized (MBL) and marginal MBL systems can be efficiently calculated by the spectrum bifurcation renormalization group (SBRG).
Strongly Correlated Electrons Disordered Systems and Neural Networks
