Papers by Pei-Fu Guo
Benchmarking Uncertainty Metrics for LLM Target-Aware Search (2025.findings-emnlp)
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| Challenge: | Existing uncertainty metrics for LLM search methods do not capture the diverse types of uncertainty needed to guide different optimization goals. |
| Approach: | They propose a framework for uncertainty benchmarking that captures four different uncertainty types . the uncertainty types Answer, Correctness, Aleatoric, and Epistemic serve different optimization goals . |
| Outcome: | The proposed framework identifies four different uncertainty types . the uncertainty types serve different optimization goals in LLM search . |
Beyond Facts- Benchmarking Distributional Reading Comprehension in Large Language Models (2026.findings-acl)
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Pei-Fu Guo, Ya An Tsai, Chun-Chia Hsu, Kai-Xin Chen, Yun-Da Tsai, Kai-Wei Chang, Nanyun Peng, Mi-Yen Yeh, Shou-De Lin
| Challenge: | Existing reading comprehension benchmarks focus on factual information, but many real-world tasks require distributional knowledge expressed across text. |
| Approach: | They propose a reading comprehension benchmark for LLMs to evaluate their ability to infer distributional knowledge from natural language. |
| Outcome: | Experiments with multiple LLMs show that the model outperforms baselines, but performance varies widely across distribution types and characteristics. |
LiveCLKTBench: Towards Reliable Evaluation of Cross-Lingual Knowledge Transfer in Multilingual LLMs (2026.acl-long)
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Pei-Fu Guo, Yun-Da Tsai, Chun-Chia Hsu, Kai-Xin Chen, Ya An Tsai, Kai-Wei Chang, Nanyun Peng, Mi-Yen Yeh, Shou-De Lin
| Challenge: | Evaluating cross-lingual knowledge transfer in large language models is challenging, as correct answers in a target language may arise either from genuine transfer or from prior exposure during pre-training. |
| Approach: | They propose a pipeline to isolate and measure cross-lingual knowledge transfer by identifying self-contained, time-sensitive knowledge entities from real-world domains and generating factual questions. |
| Outcome: | The proposed pipeline analyzes multiple LLMs across five languages and shows that cross-lingual transfer is strongly influenced by linguistic distance and often asymmetric across language directions. |
Text-centric Alignment for Bridging Test-time Unseen Modality (2025.findings-emnlp)
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| Challenge: | a text-centric alignment method is used to handle unseen modalities and dynamic modality combinations at test time. |
| Approach: | They propose a text-centric alignment method that unifies different input modalities into a single semantic text representation by leveraging in-context learning with Large Language Models and uni-modal foundation models. |
| Outcome: | The proposed method unifies input modalities into a single semantic representation . it significantly improves the ability to manage unseen, diverse, and unpredictable modality combinations . |