Research map / Large language models / Language, safety and interpretability
Recurrent Hierarchical Topic-Guided RNN for Language Generation
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| add recurrent sentence-context topic transitions to the hierarchical topic model (ablation) | GBN-RNN · APNEWS, IMDB, and BNC test | perplexity: 44.35 -> 42.71; 54.53 -> 51.36; 80.25 -> 79.13 | Helped |
| swap the locations of topic vectors at layers 3 and 1 (ablation) | rGBN-RNN · APNEWS, IMDB, and BNC test | perplexity: 42.71 -> 43.55; 51.36 -> 53.28; 79.13 -> 81.12 | Hurt |
| increase the recurrent topic-guided model from one to three layers (ablation) | rGBN-RNN · APNEWS test | perplexity: 46.35 -> 43.26 -> 42.71 | Helped |
| increase the recurrent topic-guided model from one to three layers (ablation) | rGBN-RNN · IMDB test | perplexity: 55.76 -> 53.82 -> 51.36 | Helped |
| increase the recurrent topic-guided model from one to three layers (ablation) | rGBN-RNN · BNC test | perplexity: 81.94 -> 80.25 -> 79.13 | Helped |
Closest papers by meaning
- TopicRNN: A Recurrent Neural Network with Long-Range Semantic Dependency (ICLR 2017 · similarity 0.91)
- textTOvec: DEEP CONTEXTUALIZED NEURAL AUTOREGRESSIVE TOPIC MODELS OF LANGUAGE WITH DISTRIBUTED COMPOSITIONAL PRIOR (ICLR 2019 · similarity 0.89)
- Modeling Local Dependence in Natural Language with Multi-Channel Recurrent Neural Networks (AAAI 2019 · similarity 0.88)
- Focused Hierarchical RNNs for Conditional Sequence Processing (ICML 2018 · similarity 0.87)
- M$^2$RNN: Non-Linear RNNs with Matrix-Valued States for Scalable Language Modeling (NeurIPS 2026 · similarity 0.86)
- RAT: Bridging RNN Efficiency and Attention Accuracy via Chunk-based Sequence Modeling (NeurIPS 2025 · similarity 0.85)
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