Papers by Keng-Te Liao
Neuron-Level Differentiation of Memorization and Generalization in Large Language Models (2025.emnlp-main)
Copied to clipboard
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. |
Explaining Word Embeddings via Disentangled Representation (2020.aacl-main)
Copied to clipboard
| Challenge: | Disentangled representations are known to represent interpretable factors in separated dimensions. |
| Approach: | They propose to transform dense word vectors into disentangled embeddings with improved interpretability by encoding polysemous semantics separately. |
| Outcome: | The proposed model can be encoded into multiple sub-embeddings or sub-areas and generates more efficient and effective features for natural language processing. |
Word Relation Autoencoder for Unseen Hypernym Extraction Using Word Embeddings (D18-1)
Copied to clipboard
| 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. |
Explainable and Sparse Representations of Academic Articles for Knowledge Exploration (2020.coling-main)
Copied to clipboard
| Challenge: | a system for summarizing academic articles by concept tagging has shown great coverage and high accuracy of concept identification. |
| Approach: | They propose to transform tagged concepts into sparse vectors as representations of academic documents. |
| Outcome: | The proposed system can be applied to a broader class of applications. |