Papers by Zhaoxin Huan
ReportLogic: Evaluating Logical Quality in Deep Research Reports (2026.acl-long)
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| Challenge: | Existing evaluation frameworks that evaluate large language models for Deep Research largely ignore this requirement. |
| Approach: | They propose a benchmark that quantifies report-level logical quality through a reader-centric lens of auditability. |
| Outcome: | The proposed model quantifies logical quality through a reader-centric lens of auditability. |
BOSE: A Systematic Evaluation Method Optimized for Base Models (2025.findings-acl)
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| Challenge: | Existing evaluation methods for large language models (LLMs) are inadequate to provide solid conclusions for key experiments such as data ablation and scaling law. |
| Approach: | They propose a method specifically designed to optimize the evaluation of base models by incorporating two innovations: In-Context Light-instruction Prompt and Blank-ppl for multi-choice tasks with candidate options. |
| Outcome: | The proposed method significantly improves stability and consistency of evaluations during pre-training and consistency between base and instruct models. |