Data poisoning considers cases when an adversary manipulates the behavior of machine learning algorithms through malicious training data.

However, this is not always the case, as different tasks can compete with each other and lead to weaker models.

However, this is not always the case, as different tasks can compete with each other and lead to weaker models.

Bungie's Marathon arrives on March 5 How to claim Verizon's $20 outage credit Similar verification tools have led to high-profile errors recently for other platforms. OpenAI is the latest company to hop on the bandwagon of gating access by users' age. The AI business is beginning a global rollout of an age prediction tool to determine whether or not a user is a minor. "The model looks at a combination of behavioral and account-level signals, including how long an account has existed, typical times of day when someone is active, usage patterns over time,and a user's stated age," the company's announcement states. If an individual is incorrectly characterized by ChatGPT as underage, they will need to submit a selfie to correct the mistake through the Persona age verification platform.

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The manual prompt design process for LLMs at the reasoning phase is tedious and an automated prompt optimization method is desired.

Data poisoning considers cases when an adversary manipulates the behavior of machine learning algorithms through malicious training data. Existing threat models of data poisoning center around a single metric, the number of poisoned samples. In consequence, if attackers can poison more samples than expected with affordable overhead, as in many practical scenarios, they may be able to render existing defenses ineffective in a short time. To address this issue, we leverage timestamps denoting the birth dates of data, which are often available but neglected in the past. Benefiting from these timestamps, we propose a temporal threat model of data poisoning with two novel metrics, earliness and duration, which respectively measure how long an attack started in advance and how long an attack lasted. Using these metrics, we define the notions of temporal robustness against data poisoning, providing a meaningful sense of protection even with unbounded amounts of poisoned samples when the attacks are temporally bounded. We present a benchmark with an evaluation protocol simulating continuous data collection and periodic deployments of updated models, thus enabling empirical evaluation of temporal robustness. Lastly, we develop and also empirically verify a baseline defense, namely temporal aggregation, offering provable temporal robustness and highlighting the potential of our temporal threat model for data poisoning.

Recommender systems aim to predict personalized rankings based on user preference data. With the rise of Language Models (LMs), LM-based recommenders have been widely explored due to their extensive world knowledge and powerful reasoning abilities. Most of the LM-based recommenders convert historical interactions into language prompts, pairing with a positive item as the target response and fine-tuning LM with a language modeling loss. However, the current objective fails to fully leverage preference data and is not optimized for personalized ranking tasks, which hinders the performance of LM-based recommenders. Inspired by the current advancement of Direct Preference Optimization (DPO) in human preference alignment and the success of softmax loss in recommendations, we propose Softmax-DPO (\textbf{S-DPO}) to instill ranking information into the LM to help LM-based recommenders distinguish preferred items from negatives, rather than solely focusing on positives. Specifically, we incorporate multiple negatives in user preference data and devise an alternative version of DPO loss tailored for LM-based recommenders, which is extended from the traditional full-ranking Plackett-Luce (PL) model to partial rankings and connected to softmax sampling strategies. Theoretically, we bridge S-DPO with the softmax loss over negative sampling and find that it has an inherent benefit of mining hard negatives, which assures its exceptional capabilities in recommendation tasks. Empirically, extensive experiments conducted on three real-world datasets demonstrate the superiority of S-DPO to effectively model user preference and further boost recommendation performance while providing better rewards for preferred items.

Breakthroughs, discoveries, and DIY tips sent every weekday. A newborn star 15,000 light-years from Earth is fascinating astronomers with its dual blasts of superheated plasma jets . The rare sight captured in stunning detail by the James Webb Space Telescope (JWST) isn't only a display of cosmic forces. It's helping solve a decades' long debate about the origins of massive stellar objects. Located at the edge of the Milky Way galaxy inside a nebula known as Sharpless 2-284 (Sh2-284), the young protostar is already upwards of 10 times the mass of our sun .