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Original Paper: https://arxiv.org/abs/2311.11261 By: Jiaming Zhang, Xingjun Ma, Xin Wang, Lingyu Qiu, Jiaqi Wang, Yu-Gang Jiang, Jitao Sang Abstract: With the rapid advancement of multimodal learning, pre-trained Vision-Language Models (VLMs) such as CLIP have demonstrated remarkable capacities in bridging the gap between visual and language
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Original Paper: https://arxiv.org/abs/2312.13115 By: Youjia Li, Jianjun Shi, Zheng Zhang Abstract: Code generation stands as a powerful technique in modern software development, improving development efficiency, reducing errors, and fostering standardization and consistency. Recently, ChatGPT has exhibited immense potential in automatic code generation. However, existing researches
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Original Paper: https://arxiv.org/abs/2312.12037 By: Sichao Xiong, Yigit Ihlamur Abstract: This research introduces an innovative evaluation method for the "founder-idea" fit in early-stage startups, utilizing advanced large language model techniques to assess founders' profiles against their startup ideas to enhance decision-making. Embeddings, self-play,
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Original Paper: https://arxiv.org/abs/2312.12416 By: Shweta Mahajan, Tanzila Rahman, Kwang Moo Yi, Leonid Sigal Abstract: The quality of the prompts provided to text-to-image diffusion models determines how faithful the generated content is to the user's intent, often requiring `prompt engineering'. To harness visual
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Original Paper: https://arxiv.org/abs/2310.14845 By: Mouxiang Chen, Zemin Liu, Chenghao Liu, Jundong Li, Qiheng Mao, Jianling Sun Abstract: Recent research has demonstrated the efficacy of pre-training graph neural networks (GNNs) to capture the transferable graph semantics and enhance the performance of various downstream tasks. However, the
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Original Paper: https://arxiv.org/abs/2305.18396 By: Xuanqi Liu, Zhuotao Liu Abstract: The community explored to build private inference frameworks for transformer-based large language models (LLMs) in a server-client setting, where the server holds the model parameters and the client inputs its private data (or prompt) for inference.
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Original Paper: https://arxiv.org/abs/2303.04998 By: Ning Liao, Bowen Shi, Xiaopeng Zhang, Min Cao, Junchi Yan, Qi Tian Abstract: Prompt learning has achieved great success in efficiently exploiting large-scale pre-trained models in natural language processing (NLP). It reformulates the downstream tasks as the generative pre-training ones to
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Original Paper: https://arxiv.org/abs/2312.08317 By: Pei Yan, Shunquan Tan, Miaohui Wang, Jiwu Huang Abstract: Dynamic analysis methods effectively identify shelled, wrapped, or obfuscated malware, thereby preventing them from invading computers. As a significant representation of dynamic malware behavior, the API (Application Programming Interface) sequence, comprised of
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Original Paper: https://arxiv.org/abs/2312.11513 By: Ahmed Salem, Andrew Paverd, Boris Köpf Abstract: Prompt injection has emerged as a serious security threat to large language models (LLMs). At present, the current best-practice for defending against newly-discovered prompt injection techniques is to add additional guardrails to the system
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Original Paper: https://arxiv.org/abs/2312.05180 By: Olga Golovneva, Sean O'Brien, Ramakanth Pasunuru, Tianlu Wang, Luke Zettlemoyer, Maryam Fazel-Zarandi, Asli Celikyilmaz Abstract: With recent advancements in large language models, methods like chain-of-thought prompting to elicit reasoning chains have been shown to improve results on reasoning tasks.
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Original Paper: https://arxiv.org/abs/2311.06062 By: Wenjie Fu, Huandong Wang, Chen Gao, Guanghua Liu, Yong Li, Tao Jiang Abstract: Membership Inference Attacks (MIA) aim to infer whether a target data record has been utilized for model training or not. Prior attempts have quantified the privacy risks of
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Original Paper: https://arxiv.org/abs/2312.04344 By: Pengcheng Chen, Ziyan Huang, Zhongying Deng, Tianbin Li, Yanzhou Su, Haoyu Wang, Jin Ye, Yu Qiao, Junjun He Abstract: OpenAI's latest large vision-language model (LVLM), GPT-4V(ision), has piqued considerable interest for its potential in medical applications. Despite its
