Vision-Language Models (VLMs) perform well on general multimodal tasks, yet applying them to real-world advertisement (ad) evaluation is challenging due to strong brand specificity and limited labeled data. We introduce a new practical task, brand-specific ad ranking, which aims to rank ads for a target brand prior to deployment by modeling brand-specific effectiveness. To this end, we propose ADvisor, which derives explicit brand-aware decision criteria using VLMs, augments limited brand context with ads from similar brands, and applies reflection-based scoring for ranking. Experiments on real-world advertising data from 10 brands, collected through actual ad campaigns, show that ADvisor outperforms strong baselines by up to 7.2%. Further analyses show the generated criteria capture meaningful brand specificity, and ADvisor also performs strongly in online A/B testing. Our code is available at https://github.com/K-Kyungho/ADvisor.

In Spoken Question Answering (SQA), automatic speech recognition (ASR) outputs are often relayed to language models for QA. However, constructing such a cascaded framework with large language models (LLMs) in a real-time SQA setting involves realistic challenges, such as noise in the ASR output, the limited context length of LLMs, and latency in processing large models. This paper proposes a novel model-agnostic framework, RT-VQ2A2, to address these challenges. RT-VQ2A2 consists of three steps: codebook preparation, quantized semantic vector extractor, and dual segment selector. We construct a codebook from clustering, removing outliers on a text corpus derived from ASR to mitigate the influence of ASR error. Extracting quantized semantic vectors through a pre-built codebook shows significant speed and performance improvements in relevant context retrieval. Dual segment selector considers both semantic and lexical aspects to deal with ASR error. The efficacy of RT-VQ2A2 is validated on the widely used Spoken-SQuAD dataset.

Existing multi-hop question generation (QG) methods treat answer-irrelevant documents as non-essential and remove them as impurities. However, this approach can create a training-inference discrepancy when impurities cannot be completely removed, which can lead to a decrease in model performance. To overcome this problem, we propose an auxiliary task, called order-agnostic, which leverages non-essential data in the training phase to create a robust model and extract the consistent embeddings in real-world inference environments. Additionally, we use a single LM to perform both ranker and generator through a prompt-based approach without applying additional external modules. Furthermore, we discover that appropriate utilization of the non-essential components can achieve a significant performance increase. Finally, experiments conducted on HotpotQA dataset achieve state-of-the-art.

Text classification with extremely weak supervision (EWS) imposes stricter supervision constraints compared to regular weakly supervise classification. Absolutely no labeled training samples or hand-crafted rules specific to the evaluation data are allowed. Such restrictions limit state-of-the-art EWS classification methods to indirect weak labeling techniques that assign unnatural label uncertainty estimates. We present PLAT, a framework that creates weak labels by leveraging recent developments in zero-shot text classification. PLAT employs models trained for sub-tasks other than classification to label documents. Most importantly, PLAT refrains from assigning overly confident weak labels and improves soft-label training performance for downstream classifiers. Classifiers trained with PLAT significantly outperform those trained on weak labels generated by the previous state-of-the-art in extremely weakly supervised text classification.

This paper studies the problem of generatinglikely queries for multimodal documents withimages. Our application scenario is enablingefficient “first-stage retrieval” of relevant doc-uments, by attaching generated queries to doc-uments before indexing. We can then indexthis expanded text to efficiently narrow downto candidate matches using inverted index, sothat expensive reranking can follow. Our eval-uation results show that our proposed multi-modal representation meaningfully improvesrelevance ranking. More importantly, ourframework can achieve the state of the art inthe first stage retrieval scenarios