Papers by Jiahua Liu
Semantically Comprehensive Token Pruning in LVLMs via Maximizing Concept Coverage (2026.acl-long)
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| Challenge: | Existing visual token pruning methods leverage simple metrics derived from human experience, such as attention or similarity, to rank and select tokens within a highly entangled feature space. |
| Approach: | They propose a novel visual token pruning method that uses a concept-driven paradigm to quantify the Marginal Semantic Gain of each token's contribution to uncovered concepts. |
| Outcome: | The proposed method outperforms state-of-the-art methods in a concept-driven model while maintaining semantic completeness. |
XQA: A Cross-lingual Open-domain Question Answering Dataset (P19-1)
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| Challenge: | Open-domain question answering aims to answer questions through text retrieval and reading comprehension . but, the success of these models relies on a massive volume of training data, which is not available in other languages . a new dataset aims at investigating cross-lingual OpenQA . |
| Approach: | They propose to use a dataset for cross-lingual OpenQA research to test models . they use XQA dataset to train models with large volumes of labeled data . |
| Outcome: | The proposed model achieves best results in almost all target languages while the performance is lower than that of English. |
Semantic Novelty Detection in Natural Language Descriptions (2021.emnlp-main)
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Nianzu Ma, Alexander Politowicz, Sahisnu Mazumder, Jiahua Chen, Bing Liu, Eric Robertson, Scott Grigsby
| Challenge: | Existing novelty detection algorithms are coarse-grained, working at the document or topic level. |
| Approach: | They propose to use a fine-grained semantic novelty detection problem to solve a novel novel scene problem. |
| Outcome: | The proposed model outperforms baseline models on the proposed task by large margins. |
A Knowledge-Driven Approach to Classifying Object and Attribute Coreferences in Opinion Mining (2020.findings-emnlp)
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| Challenge: | Existing methods to classify and resolve coreferences in opinionated reviews require domain-specific knowledge. |
| Approach: | They propose to automatically mine domain-specific knowledge for opinionated reviews by combining it with commonsense knowledge. |
| Outcome: | The proposed approach extracts domain-specific knowledge from unlabeled review data and trains a knowledgeaware neural coreference classification model to leverage commonsense knowledge for the task. |
Parameter-Efficient Prompt Tuning Makes Generalized and Calibrated Neural Text Retrievers (2023.findings-emnlp)
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| Challenge: | Prompt tuning is a technique that updates few parameters in pre-trained models for language understanding and generation tasks. |
| Approach: | They propose to leverage prompt tuning for neural text retrieval to improve generalization and cross-domain generalization. |
| Outcome: | The proposed approach can mitigate the two issues faced by fine-tuning retrieval methods and improve the out-of-domain zero-shot generalization of the retrieval models. |
A Multi-answer Multi-task Framework for Real-world Machine Reading Comprehension (D18-1)
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| Challenge: | Existing models of machine reading comprehension (MRC) are based on cloze style questions or crowdworkers given a short passage from well-edited sources. |
| Approach: | They propose a multi-answer multi-task framework that uses multiple reference answers for multiple questions. |
| Outcome: | The proposed model increases the ROUGE-L score on the DuReader dataset from 44.18, the previous state-of-the-art, to 51.09 . |
Route to Rome Attack: Directing LLM Routers to Expensive Models via Adversarial Suffix Optimization (2026.acl-long)
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| Challenge: | Existing routing attacks depend on white-box access or heuristic prompts, rendering them ineffective in real-world black-box scenarios. |
| Approach: | They propose a cost-aware routing strategy that routes queries to the least-cost model . they propose heuristic prompts that are ineffective in real-world black-box scenarios . |
| Outcome: | The proposed approach significantly increases the routing rate to expensive models on queries of different distributions. |