arXiv AI
Jun 16
Attribute Inference from Interactive Targeted Ads
arXiv:2606. 15209v1 Announce Type: new Abstract: Targeted advertising systems can pair audiences selected by advertisers with ad units that expose visible user actions.
Ad Insertion in LLM-Generated Responses
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arXiv AI
Aug 17
Ads in AI Chatbots? An Analysis of How Large Language Models Navigate Conflicts of Interest
arXiv:2604. 08525v2 Announce Type: replace Abstract: Large language models (LLMs) are trained to align with user preferences through methods like reinforcement learning.
arXiv Machine Learning
Aug 4
LLM-OSDA: An Optimal-Stopping Dynamic Auction for Native Advertising in Multi-Turn LLM Conversations
arXiv:2608. 00123v1 Announce Type: cross Abstract: LLM-native advertising embeds sponsored content directly into model-generated responses, shifting the unit of sale from a fixed slot to a moment within an evolving conversation.
arXiv Computation and Language
Sep 1
WildSEEK: Evaluating Language Models for Information-Seeking
WildSEEK is a new dataset of 3,000 real user information‑seeking queries, manually annotated for risk‑sensitive domains and whether the query is factoid or analytical. The accompanying evaluation framework tests LLM responses against four failure criteria—sycophantic behavior, overreliance, a default US‑centric perspective, and poor handling of vulnerable populations—finding higher failure rates for analytical queries. The authors also train classifiers on WildSEEK to analyze over 1.8 million realistic queries, revealing that more than a third are high‑risk and often analytical.
arXiv AI
Jul 24
Benchmarking the Personalization Capabilities of Large Language Models
arXiv:2607. 20471v1 Announce Type: new Abstract: Personalization, the act of varying a message to induce action from a specific receiver while keeping sender, channel, and time fixed, has a long tradition in psychology and marketing as a two-party problem in which sender and receiver have independent objectives.
arXiv Machine Learning
Aug 28
Token-Level Advertising
The paper introduces the Latent Advertiser Mixture Auction (LAMA), a token‑level advertising framework that integrates advertiser influence directly into the text generation process. Advertisers provide local continuation values that shape next‑token policies, and the platform decodes these through a latent mixture while updating an allocation posterior. LAMA is shown to satisfy Markov DSIC and IR, achieve near‑optimal KL‑regularized welfare, and, in proof‑of‑concept experiments on commercial‑search queries, improve platform welfare and revenue without compromising user‑facing response quality.
arXiv Machine Learning
Oct 5
Intent-Hiding Jailbreaks: An Information-Theoretic Framework for Compositional Attacks
The paper introduces an information‑theoretic framework for studying intent‑hiding jailbreaks against large language models. It defines prior‑posterior matching, where auxiliary tasks are chosen so that the average probability of harmful intent in a bundle matches the overall prior, thereby concealing a harmful target. The authors analyze both query‑independent and query‑dependent settings, proving computational hardness for exact matching, deriving a water‑filling solution for fractional weights, and evaluating the trade‑off between bundle size and target preservation across several open‑source models.
arXiv AI
Sep 12
Demystifying the Privacy-Utility Trade-off in LLM Interactions
The paper investigates how privacy-preserving sanitization of user context in large language model (LLM) interactions affects downstream performance. It identifies three mechanisms—Context‑Dependent Utility, Strategic Adaptation, and Combinatorial Interplay—that explain when and how to sanitize data. Based on these insights, the authors propose an intent‑driven local protection framework using a lightweight model (Veilmind‑4B) to dynamically extract, sanitize, and restore context, achieving lower privacy leakage while maintaining higher utility than existing baselines.
Hugging Face Trending Papers
Jun 22
Have You Ever Seen Them? Entity-level Membership Inference through Interrogating Large Language Models
Large Language Models (LLMs) raise growing concerns about privacy leakage and copyright compliance. Membership inference is a key tool for assessing such risks, but existing studies mainly focus on whether specific samples or sample-based data units are used for training.
arXiv AI
Jul 7
Enhancement of E-commerce Sponsored Search Relevancy with LLM
arXiv:2607. 03886v1 Announce Type: cross Abstract: Sponsored search plays a crucial role as a revenue stream for search engines, wherein advertisers competitively bid on keywords that align with the users' search queries.
arXiv Machine Learning
Jul 14
One Token Is Enough: Fingerprinting and Verifying Large Language Models from Single-Token Output Distributions
arXiv:2607. 10252v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly consumed through opaque serving chains - API aggregators, resellers, and inference providers - in which the client has no technical means to confirm that the model answering is the model advertised, and recent audits show that a substantial fraction of commercial endpoints deviate from the vendor's reference weights.
arXiv AI
Oct 7
A Systematic Investigation of Bias in Large Language Models for Advertising Relevance
arXiv:2610.07544v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used to judge how well an advertisement matches a query, but the fairness of these judgments has received...