Papers by Zhenghan Yu

2 papers
A Dual-Perspective NLG Meta-Evaluation Framework with Automatic Benchmark and Better Interpretability (2025.acl-long)

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Challenge: Existing evaluation metrics are insufficient to meet requirements for natural language generation.
Approach: They propose a dual-perspective NLG meta-evaluation framework that focuses on different evaluation capabilities and a method of automatically constructing benchmarks without requiring new human annotations.
Outcome: The proposed framework improves interpretability and provides better performance for 16 representative LLMs.
HAD: HAllucination Detection Language Models Based on a Comprehensive Hallucination Taxonomy (2026.acl-industry)

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Challenge: relying on large language models for information has raised concerns about reliability and accuracy of outputs.
Approach: They propose a hallucination taxonomy with 11 categories for various NLG tasks and propose HAllucination Detection models which integrate hallucinism detection, span-level identification, and correction into a single inference process.
Outcome: The proposed models outperform baselines on HaluEval, FactCHD, and FaithBench, confirming their robustness and versatility.

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