Neural topic models are widely used to uncover latent thematic structure in text corpora, but often produce topics with limited semantic coherence and weak alignment between topics and documents. Recent refinement approaches based on large language models (LLMs) have shown promising improvements, yet typically rely on repeated generative inference, resulting in high computational cost and practical issues such as sensitivity to prompting, stochastic variability, and the introduction of out-of-vocabulary content. We propose BERT-NTM, an encoder-based semantic refinement framework for neural topic modeling that avoids generative inference altogether. Instead of generating or rewriting topic words, BERT-NTM leverages a pretrained BERT encoder to provide discriminative semantic signals during a refinement phase: topic–word distributions are refined by scoring candidate words within a topic using masked language modeling, and document–topic representations are improved by aligning document embeddings with mixtures of topic embeddings via a Maximum Mean Discrepancy (MMD) objective. Both refinement components are fully parallelizable, stable, and operate strictly within the model vocabulary. Experiments on standard topic modeling benchmarks demonstrate that BERT-NTM consistently improves topic coherence and document–topic alignment while substantially reducing runtime and GPU memory usage compared to LLM-based refinement methods.

Representation learning is fundamental to NLP, but building embeddings that work well at different computational budgets is challenging. Matryoshka Representation Learning (MRL) offers a flexible inference paradigm through nested embeddings; however, learning such structures requires explicit coordination of how information is arranged across embedding dimensionality and model depth. In this work, we propose MIPIC (Matryoshka Representation Learning via Self-Distilled Intra-Relational Alignment and Progressive Information Chaining), a unified training framework designed to produce structurally coherent and semantically compact Matryoshka representations. MIPIC promotes cross-dimensional structural consistency through Self-Distilled Intra-Relational Alignment (SIA), which aligns token-level geometric and attention-driven relations between full and truncated representations using top-k CKA self-distillation. Complementarily, it enables depth-wise semantic consolidation via Progressive Information Chaining (PIC), a scaffolded alignment strategy that incrementally transfers mature task semantics from deeper layers into earlier layers. Extensive experiments on STS, NLI, and classification benchmarks (spanning models from TinyBERT to BGEM3, Qwen3) demonstrate that MIPIC yields Matryoshka representations that are highly competitive across all capacities, with significant performance advantages observed under extreme low-dimensional.

Matryoshka Representation Learning (MRL) produces nested embeddings whose prefixes remain semantically meaningful, allowing representations to be truncated at inference time and enabling a single model to operate across multiple dimensional budgets. This paradigm has been adopted in several modern embedding models such as Qwen3-Embedding and OpenAI text-embedding-3. However, distilling MRL teachers into compact students remains largely unexplored, as conventional knowledge distillation methods focus on aligning single-scale embeddings and do not account for the multi-scale structure of Matryoshka representations. We introduce Matryoshka Representation Distillation and propose SUGAR, a new framework that preserves the structural properties of MRL embeddings during knowledge distillation. SUGAR aligns point-wise semantic fidelity, spectral capacity via effective-rank matching, and global subspace geometry through Grassmannian alignment. Our extensive experiments show that SUGAR produces compact students that retain strong performance across multiple embedding dimensions while preserving deployment flexibility.