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Found 16 papers, 9 papers with code
AMR-DA: Data Augmentation by Abstract Meaning Representation
To evaluate the effectiveness of our method, we apply it to the tasks of semantic textual similarity (STS) and text classification.
Learning to Edit: Aligning LLMs with Knowledge Editing
Knowledge editing techniques, aiming to efficiently modify a minor proportion of knowledge in large language models (LLMs) without negatively impacting performance across other inputs, have garnered widespread attention.
MT-Eval: A Multi-Turn Capabilities Evaluation Benchmark for Large Language Models
Large language models (LLMs) are increasingly relied upon for complex multi-turn conversations across diverse real-world applications.
Towards Efficient 3D Object Detection in Bird's-Eye-View Space for Autonomous Driving: A Convolutional-Only Approach
3D object detection in Bird's-Eye-View (BEV) space has recently emerged as a prevalent approach in the field of autonomous driving.
FollowBench: A Multi-level Fine-grained Constraints Following Benchmark for Large Language Models
To fill this research gap, in this paper, we propose FollowBench, a Multi-level Fine-grained Constraints Following Benchmark for LLMs.
Scenimefy: Learning to Craft Anime Scene via Semi-Supervised Image-to-Image Translation
The challenges of this task lie in the complexity of the scenes, the unique features of anime style, and the lack of high-quality datasets to bridge the domain gap.
Exploiting Correlations Between Contexts and Definitions with Multiple Definition Modeling
In this paper, we carefully design a new task called Multiple Definition Modeling (MDM) that pool together all contexts and definition of target words.
Lion: Adversarial Distillation of Proprietary Large Language Models
The practice of transferring knowledge from a sophisticated, proprietary large language model (LLM) to a compact, open-source LLM has garnered considerable attention.
ChatGPT Evaluation on Sentence Level Relations: A Focus on Temporal, Causal, and Discourse Relations
This paper aims to quantitatively evaluate the performance of ChatGPT, an interactive large language model, on inter-sentential relations such as temporal relations, causal relations, and discourse relations.
Weighted Sampling for Masked Language Modeling
Experiments on the Semantic Textual Similarity benchmark (STS) show that WSBERT significantly improves sentence embeddings over BERT.
Global and Local Hierarchy-aware Contrastive Framework for Implicit Discourse Relation Recognition
Due to the absence of explicit connectives, implicit discourse relation recognition (IDRR) remains a challenging task in discourse analysis.
Improved Universal Sentence Embeddings with Prompt-based Contrastive Learning and Energy-based Learning
To this end, we propose to integrate an Energy-based Hinge loss to enhance the pairwise discriminative power, inspired by the connection between the NT-Xent loss and the Energy-based Learning paradigm.
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Multi-modal application: Image Memes Generation
An Internet meme commonly takes the form of an image and is created by combining a meme template (image) and a caption (natural language sentence).
A convolutional neural network for prestack fracture detection
From prestack seismic gathers, anisotropic analysis and inversion were commonly applied to characterize the dominant orientations and relative intensities of fractures.
XRJL-HKUST at SemEval-2021 Task 4: WordNet-Enhanced Dual Multi-head Co-Attention for Reading Comprehension of Abstract Meaning
This paper presents our submitted system to SemEval 2021 Task 4: Reading Comprehension of Abstract Meaning.
Hybrid-DNNs: Hybrid Deep Neural Networks for Mixed Inputs
Regarding as a combination of feature learning and target learning, the new proposed networks provide great capacity in high-hierarchy feature extraction and in-depth data mining.
