The paper proposes OmniPred, a framework for training language models as universal regressors over evaluation data from various real-world experiments. This approach, which uses data from Google Vizier, demonstrates that…
Continue readingThe article introduces Q-probing, a method that adapts a pre-trained language model to maximize a task-specific reward function. This approach sits between heavier methods like finetuning and lighter ones like…
Continue readingThe article presents a study on how feedback loops in language models can cause in-context reward hacking (ICRH). This occurs when a language model optimizes an objective but creates negative…
Continue readingThe article presents a study from IBM Research AI on creating efficient models for detecting hate, abuse, and profanity (HAP) in Large Language Models (LLMs). The authors highlight the importance…
Continue readingThe study focuses on the use of pre-trained language models for text-to-code generation. The researchers explore how sequence tokens can be adapted and represented differently depending on their modality. They…
Continue readingThis article investigates Machine Unlearning (MU), which is focused on neural models inadvertently retaining personal or sensitive data. A novel approach is introduced to achieve precise and selective forgetting within…
Continue readingThe article introduces PromptCrypt, a novel encryption mechanism designed to enhance user privacy in cloud-based language models (LLMs) like ChatGPT. The method encrypts user inputs with Emoji, making the data…
Continue readingThe article presents LLaDA, a tool that enables autonomous vehicles and human drivers to adapt to different traffic rules in new environments. LLaDA leverages the capabilities of large language models…
Continue readingThe study presents a novel approach to legal reasoning, integrating AI to navigate complexities and biases in global jurisprudence. It leverages advanced language models and a human-AI collaborative framework. The…
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