Papers by Zixin Zeng

3 papers
Automatic Prompt Engineering for Scalable Prompt Inversion in Text-to-Image Ad Generation (2026.acl-industry)

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Challenge: PRISM-DUEL is a black-box framework that formalizes prompt optimization as Automatic Prompt Engineering (APE) PRIMS-DUEl is motivated by advertising workflows requiring low-latency, diverse variants faithful to a human-designed ad.
Approach: They propose a black-box framework that formalizes prompt optimization as Automatic Prompt Engineering (APE) they obtain label-free pairwise preferences and rationales from an LLM judge over pairs of generated images and use a dueling-bandit optimizer to optimize a prompt for generating controlled variations while matching the reference ad's visual content.
Outcome: The proposed framework preserves visual similarity and semantic faithfulness while increasing diversity.
For-Value: Efficient Forward-Only Data Valuation for finetuning LLMs and VLMs (2026.acl-long)

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Challenge: Existing methods for data valuation rely on gradient computations, making them prohibitive for billion-parameter models.
Approach: They propose a forward-only data valuation framework that enables efficient batch-scalable value estimation while maintaining effectiveness.
Outcome: The proposed framework matches or outperforms gradient-based baselines in detecting influential data and mislabeled data while achieving significant efficiency improvements.
Extract and Attend: Improving Entity Translation in Neural Machine Translation (2023.findings-acl)

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Challenge: Existing methods to improve entity translation in Neural machine translation still suffer from inaccurate translation of entities due to the lack of entity training instances.
Approach: They propose an extract-and-tend approach to enhance entity translation in NMT by extracting entities from a dictionary and attending to them with a prefix.
Outcome: Experiments on En-Zh and En-Ru show that the proposed approach improves translation accuracy and translation quality.

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