Papers by Lechen Zhang
VeriFact: Enhancing Long-Form Factuality Evaluation with Refined Fact Extraction and Reference Facts (2025.emnlp-main)
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| Challenge: | Prior work focuses on accuracy and precision, but factuality evaluation is difficult due to inter-sentence dependencies. |
| Approach: | They introduce a factuality evaluation framework to enhance fact extraction . they also introduce 'factRBench' that evaluates both precision and recall . |
| Outcome: | The proposed framework enhances fact extraction by identifying incomplete and missing facts . it also evaluates precision and recall in long-form models, whereas prior work focuses on precision. |
Toward Global AI Inclusivity: A Large-Scale Multilingual Terminology Dataset (GIST) (2025.findings-acl)
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Jiarui Liu, Iman Ouzzani, Wenkai Li, Lechen Zhang, Tianyue Ou, Houda Bouamor, Zhijing Jin, Mona T. Diab
| Challenge: | Despite advances in machine translation, domain-specific terminology translation remains challenging. |
| Approach: | They propose a large-scale multilingual AI terminology dataset that combines LLMs for extraction with human expertise for translation. |
| Outcome: | The proposed framework combines human translation expertise with LLMs to improve translation accuracy and improve BLEU and COMET scores. |
FactBench: A Dynamic Benchmark for In-the-Wild Language Model Factuality Evaluation (2025.acl-long)
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| Challenge: | Language models (LMs) generate false or unverifiable content, often known as hallucination, despite ongoing efforts to enhance their factuality. |
| Approach: | They propose a tool that measures LMs’ factuality in real-world user interactions by evaluating their factual accuracy and categorizing content units as Supported, Unsupported, or Undecidable based on Web-retrieved evidence. |
| Outcome: | The proposed evaluation pipeline measures language models’ factuality in real-world user interactions. |
You don’t need a personality test to know these models are unreliable: Assessing the Reliability of Large Language Models on Psychometric Instruments (2024.naacl-long)
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Bangzhao Shu, Lechen Zhang, Minje Choi, Lavinia Dunagan, Lajanugen Logeswaran, Moontae Lee, Dallas Card, David Jurgens
| Challenge: | Large Language Models (LLMs) are popular for research in social sciences . currently, prompting LLMs is insufficient to accurately and reliably capture model perceptions, and we discuss potential alternatives to improve this. |
| Approach: | They construct a dataset that contains 693 questions encompassing 39 different instruments of persona measurement on 115 persona axes and a set of questions containing minor variations. |
| Outcome: | The proposed model can generate answers and negate statements in a consistent and robust manner. |
Logit Arithmetic Elicits Long Reasoning Capabilities Without Training (2026.findings-acl)
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Yunxiang Zhang, Muhammad Khalifa, Lechen Zhang, Xin Liu, Ayoung Lee, Xinliang Frederick Zhang, Farima Fatahi Bayat, Lu Wang
| Challenge: | Large reasoning models exhibit long chain-of-thought reasoning with complex strategies such as backtracking and self-verification, yet, these capabilities typically require resource-intensive post-training. |
| Approach: | They propose a decoding-time approach which transfers long chain-of-thought reasoning capabilities from a substantially smaller reasoning guider to a large non-reasoning target. |
| Outcome: | The proposed method improves performance over a model 21x smaller than the target model by 21.5% and 24.2% over the model. |
Skill-Aware Data Selection and Fine-Tuning for Data-Efficient Reasoning Distillation (2026.acl-short)
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| Challenge: | Large reasoning models such as DeepSeek-R1 and their distilled variants achieve impressive performance on complex reasoning tasks, yet their costs remain substantial. |
| Approach: | They propose a skill-centric distillation framework that efficiently transfers reasoning ability to weaker models with two components: (1) Skill-based data selection, which prioritizes examples targeting the student model’s weaker skills, and (2) Skillaware fine-tuning, which encourages explicit skill decomposition during problem solving. |
| Outcome: | The proposed framework surpasses baselines on Qwen3-4B and Qwend3-8B and focuses on skills emphasized during training. |
Real or Robotic? Assessing Whether LLMs Accurately Simulate Qualities of Human Responses in Human-LLM Dialogue (2026.findings-acl)
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Jonathan Ivey, Shivani Kumar, Jiayu Liu, Hua Shen, Sushrita Rakshit, Rohan Raju, Haotian Zhang, Aparna Ananthasubramaniam, Junghwan Kim, Bowen Yi, Dustin Wright, Abraham Israeli, Anders Giovanni Møller, Lechen Zhang, David Jurgens
| Challenge: | Recent work has sought to use large language models to simulate human-human and human-LLM interactions. |
| Approach: | They use a large-scale dataset to generate a paired LLM-LLM and human-LLm dialogues from the WildChat dataset and quantify how well they align with their human counterparts. |
| Outcome: | The proposed models perform similarly in simulating English, Chinese, and Russian dialogues. |
Causally Modeling the Linguistic and Social Factors that Predict Email Response (2025.naacl-long)
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Yinuo Xu, Hong Chen, Sushrita Rakshit, Aparna Ananthasubramaniam, Omkar Yadav, Mingqian Zheng, Michael Jiang, Lechen Zhang, Bowen Yi, Kenan Alkiek, Abraham Israeli, Bangzhao Shu, Hua Shen, Jiaxin Pei, Haotian Zhang, Miriam Schirmer, David Jurgens
| Challenge: | a key intent behind many emails is to get a reply from the recipient. |
| Approach: | They propose to model the intents, expectations, and responsiveness in email exchanges by using a dataset containing 1800 emails annotated with nuanced types of intents and expectations. |
| Outcome: | The proposed model is based on 1800 emails annotated with nuanced types of intents and expectations . it shows that social status, argumentation, and strength of social connection influence email response rates . |