Search Results for author:
Found 12 papers, 5 papers with code
OpenEval: Benchmarking Chinese LLMs across Capability, Alignment and Safety
In addition to these benchmarks, we have implemented a phased public evaluation and benchmark update strategy to ensure that OpenEval is in line with the development of Chinese LLMs or even able to provide cutting-edge benchmark datasets to guide the development of Chinese LLMs.
Privacy for Fairness: Information Obfuscation for Fair Representation Learning with Local Differential Privacy
Based on the proposed method, we further develop a variational representation encoding approach that simultaneously achieves fairness and LDP.
PACE: A Pragmatic Agent for Enhancing Communication Efficiency Using Large Language Models
This paper proposes an image pragmatic communication framework based on a Pragmatic Agent for Communication Efficiency (PACE) using Large Language Models (LLM).
EVCap: Retrieval-Augmented Image Captioning with External Visual-Name Memory for Open-World Comprehension
Instead of relying on large amounts of data and/or scaling up network parameters, we introduce a highly effective retrieval-augmented image captioning method that prompts LLMs with object names retrieved from External Visual--name memory (EVCap).
Evaluating Large Language Models: A Comprehensive Survey
We hope that this comprehensive overview will stimulate further research interests in the evaluation of LLMs, with the ultimate goal of making evaluation serve as a cornerstone in guiding the responsible development of LLMs.
Partition-and-Debias: Agnostic Biases Mitigation via A Mixture of Biases-Specific Experts
However, most of these methods implicitly assume that a given image contains only one type of known or unknown bias, failing to consider the complexities of real-world biases.
Counterfactual reasoning: Testing language models' understanding of hypothetical scenarios
Current pre-trained language models have enabled remarkable improvements in downstream tasks, but it remains difficult to distinguish effects of statistical correlation from more systematic logical reasoning grounded on the understanding of real world.
A unified information-theoretic model of EEG signatures of human language processing
We advance an information-theoretic model of human language processing in the brain, in which incoming linguistic input is processed at two levels, in terms of a heuristic interpretation and in terms of error correction.
Counterfactual reasoning: Do language models need world knowledge for causal understanding?
Current pre-trained language models have enabled remarkable improvements in downstream tasks, but it remains difficult to distinguish effects of statistical correlation from more systematic logical reasoning grounded on understanding of the real world.
Anti-Asian Hate Speech Detection via Data Augmented Semantic Relation Inference
We design a novel framework SRIC that simultaneously performs two tasks: (1) semantic relation inference between online posts and sentiment hashtags, and (2) sentiment classification on these posts.
Multi-scale Sparse Representation-Based Shadow Inpainting for Retinal OCT Images
To address these challenges, we propose a novel multi-scale shadow inpainting framework for OCT images by synergically applying sparse representation and deep learning: sparse representation is used to extract features from a small amount of training images for further inpainting and to regularize the image after the multi-scale image fusion, while convolutional neural network (CNN) is employed to enhance the image quality.
Multi-scale GCN-assisted two-stage network for joint segmentation of retinal layers and disc in peripapillary OCT images
An accurate and automated tissue segmentation algorithm for retinal optical coherence tomography (OCT) images is crucial for the diagnosis of glaucoma.
Medical Image Segmentation
Retinal OCT Layer Segmentation
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