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Found 9 papers, 4 papers with code
XL$^2$Bench: A Benchmark for Extremely Long Context Understanding with Long-range Dependencies
However, prior benchmarks create datasets that ostensibly cater to long-text comprehension by expanding the input of traditional tasks, which falls short to exhibit the unique characteristics of long-text understanding, including long dependency tasks and longer text length compatible with modern LLMs' context window size.
Towards Verifiable Text Generation with Evolving Memory and Self-Reflection
Despite the remarkable ability of large language models (LLMs) in language comprehension and generation, they often suffer from producing factually incorrect information, also known as hallucination.
Exploring the Potential of Large Language Models (LLMs) in Learning on Graphs
The most popular pipeline for learning on graphs with textual node attributes primarily relies on Graph Neural Networks (GNNs), and utilizes shallow text embedding as initial node representations, which has limitations in general knowledge and profound semantic understanding.
Boosting Event Extraction with Denoised Structure-to-Text Augmentation
Event extraction aims to recognize pre-defined event triggers and arguments from texts, which suffer from the lack of high-quality annotations.
Multi-Modal Relational Graph for Cross-Modal Video Moment Retrieval
Toward this end, we contribute a multi-modal relational graph to capture the interactions among objects from the visual and textual content to identify the differences among similar video moment candidates.
Neural Chinese Word Segmentation as Sequence to Sequence Translation
In this paper, we cast the CWS as a sequence translation problem and propose a novel sequence-to-sequence CWS model with an attention-based encoder-decoder framework.
Multi-task Learning for Low-resource Second Language Acquisition Modeling
Second language acquisition (SLA) modeling is to predict whether second language learners could correctly answer the questions according to what they have learned.
Distant Supervision for Relation Extraction with Linear Attenuation Simulation and Non-IID Relevance Embedding
Distant supervision for relation extraction is an efficient method to reduce labor costs and has been widely used to seek novel relational facts in large corpora, which can be identified as a multi-instance multi-label problem.
Task-oriented Word Embedding for Text Classification
In this paper, we propose a task-oriented word embedding method and apply it to the text classification task.
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