Papers by Jinxia Zhang

5 papers
MQM-APE: Toward High-Quality Error Annotation Predictors with Automatic Post-Editing in LLM Translation Evaluators (2025.coling-main)

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Challenge: Large Language Models (LLMs) have shown significant potential as judges for Machine Translation (MT) quality assessment.
Approach: They propose a framework that automatically post-edits the original translation based on each error, thereby filtering out non-impactful errors.
Outcome: The proposed framework improves reliability and quality of error spans against GEMBA-MQM, across eight LLMs in both high- and low-resource languages.
Enhancing Metaphor Detection by Gloss-based Interpretations (2021.findings-acl)

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Challenge: Existing approaches to metaphor detection are limited by ambiguous meanings of metaphorical substitute words.
Approach: They propose a model that utilizes glosses to interpret metaphorical words by enhancing three datasets with gloss annotations.
Outcome: The proposed model outperforms state-of-the-art models on three enhanced datasets and that gloss-based interpretation benefits metaphor detection.
Runaway is Ashamed, But Helpful: On the Early-Exit Behavior of Large Language Model-based Agents in Embodied Environments (2025.findings-emnlp)

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Challenge: Experiments with 4 different LLMs across 5 embodied environments show significant efficiency improvements, with only minor drops in agent performance.
Approach: They propose an intrinsic method that injects exit instructions during generation and an extransic system that verifies task completion to determine when to halt an agent’s trial.
Outcome: The proposed method injects exit instructions during generation and an exit method verifies task completion to determine when to halt an agent’s trial.
Natural SQL: Making SQL Easier to Infer from Natural Language Specifications (2021.findings-emnlp)

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Challenge: Existing models that do not support executable SQL generation can generate executable queries.
Approach: They propose an SQL intermediate representation called Natural SQL (NatSQL) they propose to preserve the core functionalities of SQL while simplifying the queries .
Outcome: The proposed model outperforms existing models on a text-to-SQL benchmark . it significantly improves the performance of previous models on the same dataset .
The Bitter Lesson of Diffusion Language Models for Agentic Workflows: A Comprehensive Reality Check (2026.acl-long)

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Challenge: Embodied and Tool-Calling agents are effective in planning and complex reasoning, but require causal, precise, and logically grounded reasoning mechanisms to be viable for agentic tasks.
Approach: They propose a framework that integrates dLLMs as plug-and-play cognitive cores.
Outcome: The proposed model breaks the sequential latency bottleneck in agentic interactions.

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