Papers by Kanjian Zhang
Toward Human-Like Evaluation for Natural Language Generation with Error Analysis (2023.acl-long)
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| Challenge: | Pre-trained language models (PLMs) have been used to evaluate language generation tasks . pretrained error analysis can be used to refine the generated sentence toward higher confidence . |
| Approach: | They propose to combine pretrained language model based metrics with human-like error analysis to improve sentence confidence. |
| Outcome: | The proposed method outperforms top-scoring metrics in 19/25 settings. |
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. |
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. |
Error Analysis Prompting Enables Human-Like Translation Evaluation in Large Language Models (2024.findings-acl)
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| Challenge: | Recent research shows that large language models (LLMs) perform poorly at segment level. |
| Approach: | They propose a new prompting method that emulates the commonly accepted human evaluation framework . they will release their code and scripts to facilitate the community . |
| Outcome: | The proposed method is based on the human evaluation framework MQM and produces explainable and reliable MT evaluations at both the system and segment level. |
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. |