arXiv AI

Ad Headline Generation using Self-Critical Masked Language Model

arXiv:2607. 06818v1 Announce Type: cross Abstract: For any E-commerce website it is a nontrivial problem to build enduring advertisements that attract shoppers.

arXiv AI

Jul 10

COBART: Controlled, Optimized, Bidirectional and Auto-Regressive Transformer for Ad Headline Generation

arXiv:2607. 08071v1 Announce Type: cross Abstract: Online ads are essential to all businesses and ad headlines are one of their core creative component.

By Yashal Shakti Kanungo, Gyanendra Das, Pooja A, Sumit Negi

llms fine-tuning

arXiv AI

2d ago

AdsWorldEngine: A Self-Evolving Conversational Advertising Agent through Orchestrator and Tool Coevolution

arXiv:2608. 13833v1 Announce Type: cross Abstract: Conversational advertising aims to deliver useful ads within multi-turn assistant interactions.

By Simiao Zuo, Chenhui Xu, Yimeng Jia, Qiang Lou, Jian Jiao, Denis Charles

agents reinforcement-learning fine-tuning

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.

By Md Omar Faruk Rokon, Andrei Simion, Weizhi Du, Musen Wen, Hong Yao, Kuang-chih Lee

llms fine-tuning safety

arXiv AI

Jul 24

ClickGuard: Detecting and Spoiling Clickbait News with Informativeness Measures and Large Language Models

arXiv:2607. 20463v1 Announce Type: new Abstract: This paper presents an AI-driven browser extension that identifies clickbait to help users avoid misleading Internet articles.

By Wojciech Michaluk, Tymoteusz Urban, Mateusz Kubita, Soveatin Kuntur, Anna Wr\'oblewska

llms rag nlp

arXiv Machine Learning

Jun 4

Cross-Prompt Generalization in Detecting AI-Generated Fake News Using Interpretable Linguistic Features

arXiv:2606. 04199v1 Announce Type: cross Abstract: The increasing use of large language models has raised concerns about the spread of AI-generated fake news, particularly under varying prompting strategies.

By Aya Vera-Jimenez, Samuel Jaeger, Calvin Ibenye, Dhrubajyoti Ghosh

llms

arXiv Machine Learning

Jun 15

DRIVE: Distributional and Retrieval-Augmented Bidding with Value Evaluation

arXiv:2606. 14192v1 Announce Type: new Abstract: Auto-bidding is a core component of real-time advertising systems, where decisions must optimize long-term performance under budget and cost constraints, while online exploration is prohibitively risky.

By Miduo Cui, Haochen Wang, Shangqin Mao, Xun Yang, Qianlong Xie, Xingxing Wang, Xuri Ge, Ying Zhou, Zhiwei Xu

llms rag reinforcement-learning benchmarks

arXiv Machine Learning

Jun 4

Data Attribution in Large Language Models via Bidirectional Gradient Optimization

arXiv:2606. 04928v1 Announce Type: new Abstract: Large Language Models (LLMs) are increasingly deployed across diverse applications, raising critical questions for governance, accountability, and data provenance.

By Fr\'ed\'eric Berdoz, Luca A. Lanzend\"orfer, Kaan Bayraktar, Roger Wattenhofer

llms safety

arXiv Machine Learning

Jul 21

Token-Level Off-Policy Learning for Faithful Generation Under Distribution Shift

arXiv:2607. 17524v1 Announce Type: cross Abstract: We propose Token-Level Off-Policy Labeling (TOPL), an off-policy training paradigm that reframes post-training as a token-level correctness prediction task.

By Zitong Huang, Gustavo Lucas Carvalho, Deqing Fu, Robin Jia

nlp fine-tuning benchmarks

Hugging Face Trending Papers

Jul 20

Token-Level Off-Policy Learning for Faithful Generation Under Distribution Shift

We propose Token-Level Off-Policy Labeling (TOPL), an off-policy training paradigm that reframes post-training as a token-level correctness prediction task. Our key intuition is that by training the model to distinguish good and bad tokens in a response, we naturally guide the model towards generating good tokens, while avoiding the pitfalls that come with directly training the model to generate off-policy tokens.

nlp fine-tuning benchmarks

arXiv Machine Learning

Jul 28

SMART: LLM-Augmented Hybrid Retrieval for Dynamic Product Ads

arXiv:2607. 23121v1 Announce Type: cross Abstract: Dynamic Product Ads (DPA) require retrieving relevant items from multi-million product catalogs, balancing two competing objectives: retargeting (re-surfacing known interests) and prospecting (discovering new categories).

By Congfei Zhang, Jingxiao Ma, Xiaodong Liu, Hsiang-wei Chao, Siman Wang, Ge Liu, Shantanu Aggarwal, Vincent Zhang, Meghana Missula, Rachel Liao, Zichu Li, Xiao Bai, Yunzhi Zhou, Yajun Wang, Zhe Liu, Jinchao Li, Yu Zhang

llms rag

arXiv AI

6d ago

Learning from Online User Feedback for Shopping Agents

arXiv:2608. 11604v1 Announce Type: new Abstract: Large language model-based shopping agents are increasingly deployed in real-world e-commerce platforms, generating massive amounts of user interaction logs that provide valuable supervision for improving these agents.

By Haobo Zhang, Kelong Mao, Sulong Xu, Simiu Gu, Zhicheng Dou

llms agents reinforcement-learning efficiency safety

arXiv AI

Jul 28

In-Context Learning as Implicit Policy Gradient

arXiv:2607. 23153v1 Announce Type: cross Abstract: Recent work has shown that large language models (LLMs) can iteratively improve their outputs by incorporating generated samples and their corresponding evaluation scores as in-context examples.

By Masahiro Kaneko, Timothy Baldwin

llms reinforcement-learning

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