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Found 11 papers, 5 papers with code
SparsePO: Controlling Preference Alignment of LLMs via Sparse Token Masks
Preference Optimization (PO) has proven an effective step for aligning language models to human-desired behaviors.
Human-like Episodic Memory for Infinite Context LLMs
Large language models (LLMs) have shown remarkable capabilities, but still struggle with processing extensive contexts, limiting their ability to maintain coherence and accuracy over long sequences.
Text-to-Code Generation with Modality-relative Pre-training
Large pre-trained language models have recently been expanded and applied to programming language tasks with great success, often through further pre-training of a strictly-natural language model--where training sequences typically contain both natural and (linearised) programming language.
EntityCS: Improving Zero-Shot Cross-lingual Transfer with Entity-Centric Code Switching
We use Wikidata and English Wikipedia to construct an entity-centric CS corpus by switching entities to their counterparts in other languages.
Training Dynamics for Curriculum Learning: A Study on Monolingual and Cross-lingual NLU
Curriculum Learning (CL) is a technique of training models via ranking examples in a typically increasing difficulty trend with the aim of accelerating convergence and improving generalisability.
Natural Language Understanding
Zero-Shot Cross-Lingual Transfer
PanGu-Coder: Program Synthesis with Function-Level Language Modeling
We present PanGu-Coder, a pretrained decoder-only language model adopting the PanGu-Alpha architecture for text-to-code generation, i. e. the synthesis of programming language solutions given a natural language problem description.
Distantly Supervised Relation Extraction with Sentence Reconstruction and Knowledge Base Priors
We propose a multi-task, probabilistic approach to facilitate distantly supervised relation extraction by bringing closer the representations of sentences that contain the same Knowledge Base pairs.
Ranked #4 on
Relation Extraction
on NYT Corpus
Connecting the Dots: Document-level Neural Relation Extraction with Edge-oriented Graphs
We thus propose an edge-oriented graph neural model for document-level relation extraction.
Inter-sentence Relation Extraction with Document-level Graph Convolutional Neural Network
Inter-sentence relation extraction deals with a number of complex semantic relationships in documents, which require local, non-local, syntactic and semantic dependencies.
A Walk-based Model on Entity Graphs for Relation Extraction
We present a novel graph-based neural network model for relation extraction.
Ranked #1 on
Relation Extraction
on ACE 2005
(Cross Sentence metric)
