Papers by Tony Zhao

5 papers
Mitigating Gender Bias in Natural Language Processing: Literature Review (P19-1)

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Challenge: NLP models propagate and may even amplify gender bias found in text corpora . methods to mitigate gender bias in NLP are relatively nascent .
Approach: They propose to analyze gender bias based on four forms of representation bias and discuss the advantages and drawbacks of existing gender debiasing methods.
Outcome: The proposed methods are based on four forms of representation bias and have advantages and drawbacks.
Towards Understanding Gender Bias in Relation Extraction (2020.acl-main)

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Challenge: Existing bias mitigation techniques have a negative effect on NRE, a study finds .
Approach: They create a dataset to analyze gender bias in relation extraction systems . they find that existing bias mitigation techniques have a negative effect on NRE .
Outcome: The proposed dataset analyzes gender bias in relation extraction systems using a 10% human annotated test set.
Concealed Data Poisoning Attacks on NLP Models (2021.naacl-main)

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Challenge: In contrast, adversarial attacks can cause model errors by modifying inputs, such as the universal triggers attack.
Approach: They propose a data poisoning attack that allows an adversary to control model predictions whenever a desired trigger phrase is present in the input.
Outcome: The proposed attack can cause model errors by modifying inputs, but it can also cause extra human annotation.
Graph-based Multilingual Product Retrieval in E-Commerce Search (2021.naacl-industry)

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Challenge: Modern e-commerce search systems require product retrieval under multilingual scenarios.
Approach: They propose a universal multilingual retrieval system that captures interactions between search queries and items in e-commerce search.
Outcome: The proposed system outperforms state-of-the-art retrieval models on five countries and has been deployed in production for multiple countries.
OpenRubrics: Towards Scalable Synthetic Rubric Generation for Reward Modeling and LLM Alignment (2026.acl-long)

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Challenge: Existing reward models rely on scalar or pairwise judgments that fail to capture multifaceted nature of human preferences.
Approach: They propose a rubric-based reward model that uses a large collection of prompt, rubric pairs to generate a scalar score or preference label for each response.
Outcome: The proposed model surpasses strong size-matched baselines by 8.4% across multiple benchmarks.

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