Papers by Shou-De Lin
Neuron-Level Differentiation of Memorization and Generalization in Large Language Models (2025.emnlp-main)
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Ko-Wei Huang, Yi-Fu Fu, Ching-Yu Tsai, Yu-Chieh Tu, Tzu-ling Cheng, Cheng-Yu Lin, Yi-Ting Yang, Heng-Yi Liu, Keng-Te Liao, Da-Cheng Juan, Shou-De Lin
| Challenge: | Existing models exhibit memorization and generalization behaviors in ways that are not easily interpretable or controllable. |
| Approach: | They propose to use a GPT-2 and LLaMA-3.2 model to identify distinct neuron subsets responsible for each behavior to steer the model toward memorization or generalization. |
| Outcome: | The proposed models show that inference-time interventions on these neurons can steer the model’s behavior toward memorization or generalization. |
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 . |
Role-Sensitive Neurons: A Neuron-Level Gain Control Mechanism for Confidence Steering (2026.findings-acl)
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| Challenge: | Large language models (LLMs) exhibit striking behavioral flexibility. |
| Approach: | They propose to identify a sparse sub-network of Role-Sensitive Neurons (RSNs) that governs the transition from hesitation to action. |
| Outcome: | The proposed framework allows precise regulation of abstention behavior by intervention on this subspace. |
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. |
Self-Discriminative Learning for Unsupervised Document Embedding (N19-1)
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| Challenge: | Existing methods for document embedding learning do not consider inter-document relationships. |
| Approach: | They propose to exploit the inter-document information and directly model the relations of documents in embedding space with a discriminative network and a novel objective. |
| Outcome: | The proposed method has errors that are 5 to 13% lower than state-of-the-art models and is even more pronounced in scarce label setting. |
Transferable Embedding Inversion Attack: Uncovering Privacy Risks in Text Embeddings without Model Queries (2024.acl-long)
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| Challenge: | Recent advances in text embedding models have significantly streamlined the process of generating embeddables. |
| Approach: | They develop a transfer attack method that uses a surrogate model to mimic the victim model's behavior and infers sensitive information from embeddings without direct access. |
| Outcome: | The proposed method outperforms existing methods and reveals potential privacy vulnerabilities in embedding technologies. |
Multiple Text Style Transfer by using Word-level Conditional Generative Adversarial Network with Two-Phase Training (D19-1)
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| Challenge: | Generative adversarial network (GAN) is a popular model for text style transfer . but, training GAN often suffers from mode collapse problem, which causes that the transferred text is little related to the original text. |
| Approach: | They propose a non-parallel text style transfer model with a word-level conditional architecture and a two-phase training procedure to maintain style-unrelated words while changing others. |
| Outcome: | The proposed model outperforms state-of-the-art models on three real-world datasets in transfer accuracy and fluency. |
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 . |
Word Relation Autoencoder for Unseen Hypernym Extraction Using Word Embeddings (D18-1)
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| Challenge: | Lexicon relation extraction given distributional representation of words is an important topic in NLP. |
| Approach: | They propose to use a word relation autoencoder to extract hypernyms from vocabularies . they propose to analyze the pollution and construct an indicator to measure it . |
| Outcome: | The proposed model outperforms the competitors on several hypernym-like lexicon datasets. |
Controlling Sequence-to-Sequence Models - A Demonstration on Neural-based Acrostic Generator (D19-3)
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| Challenge: | acrostic is a form of writing that the first token of each line forms a meaningful sequence. |
| Approach: | They propose a generalized acrostic generation system that can hide certain messages in a flexible pattern specified by the users. |
| Outcome: | The proposed system can hide certain messages in a flexible pattern specified by the users. |