Papers by Gaifan Zhang
CASE – Condition-Aware Sentence Embeddings for Conditional Semantic Textual Similarity Measurement (2026.eacl-long)
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| Challenge: | Recent approaches use semantic similarity to improve the quality of sentence embeddings, but it is difficult to measure the similarity between sentences. |
| Approach: | They propose a condition-aware sentence embedding method that uses an LLM encoder to create an embeddable sentence under a given condition. |
| Outcome: | The proposed method improves the performance of LLM-based embeddings and the isotropy of the embeddable space despite requiring a small number of dimensions. |
Evaluating Unsupervised Dimensionality Reduction Methods for Pretrained Sentence Embeddings (2024.lrec-main)
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| Challenge: | Sentence embeddings produced by pretrained language models are high dimensional (ca. 1024-4096) this is problematic when representing large numbers of sentences in memory- or compute-constrained devices. |
| Approach: | They propose to use Principal Component Analysis to reduce the dimensionality of sentence embeddings produced by pretrained language models to reduce their complexity. |
| Outcome: | The proposed methods reduce the dimensionality of sentence embeddings by 50% without incurring significant loss in performance in multiple downstream tasks. |
Map of Encoders – Mapping Sentence Encoders using Quantum Relative Entropy (2026.acl-long)
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| Challenge: | a method to compare and visualise sentence encoders at scale is proposed . we map encoder LLMs using QRE-based feature vectors, which are then projected to 2D . |
| Approach: | They propose a method to compare and visualise sentence encoders at scale by creating a map of encoder . they construct a QRE-based map of sentences covering 1101 publicly available sentence encoded sentences . |
| Outcome: | The proposed method compares sentence encoders at scale by creating a map of encoder models . it shows that the map accurately reflects relationships between encoder and unit base encoder . |
Annotating Training Data for Conditional Semantic Textual Similarity Measurement using Large Language Models (2025.emnlp-main)
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| Challenge: | Semantic similarity between two sentences depends on the aspects considered between those sentences. |
| Approach: | They propose a Conditional Semantic Textual Similarity task which measures the similarity between two sentences under a specified condition. |
| Outcome: | The proposed method improves Spearman correlation by 5.4% by training a supervised model on the re-annotated dataset. |