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Found 8 papers, 4 papers with code
InferAligner: Inference-Time Alignment for Harmlessness through Cross-Model Guidance
With the rapid development of large language models (LLMs), they are not only used as general-purpose AI assistants but are also customized through further fine-tuning to meet the requirements of different applications.
Watermarking LLMs with Weight Quantization
Abuse of large language models reveals high risks as large language models are being deployed at an astonishing speed.
SeqXGPT: Sentence-Level AI-Generated Text Detection
Therefore, it is important to build strong AI-generated text (AIGT) detectors.
PerturbScore: Connecting Discrete and Continuous Perturbations in NLP
Through experimental results, we find that we can build a connection between discrete and continuous perturbations and use the proposed PerturbScore to learn such correlation, surpassing previous methods used in discrete perturbation measuring.
Origin Tracing and Detecting of LLMs
The extraordinary performance of large language models (LLMs) heightens the importance of detecting whether the context is generated by an AI system.
Kidney Exchange with Inhomogeneous Edge Existence Uncertainty
To the best of our knowledge, the state-of-the-art approaches are only tractable when failure probabilities are identical.
Recommendation Engine for Lower Interest Borrowing on Peer to Peer Lending (P2PL) Platform
Many recommendation systems have been developed for lenders to achieve higher interest rates and avoid defaulting loans.
A Robust AUC Maximization Framework with Simultaneous Outlier Detection and Feature Selection for Positive-Unlabeled Classification
The positive-unlabeled (PU) classification is a common scenario in real-world applications such as healthcare, text classification, and bioinformatics, in which we only observe a few samples labeled as "positive" together with a large volume of "unlabeled" samples that may contain both positive and negative samples.
