Papers by Liusheng Huang
Top-n𝜎: Eliminating Noise in Logit Space for Robust Token Sampling of LLM (2025.acl-long)
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| Challenge: | Existing sampling methods that are sensitive to temperature scaling fail to distinguish between diversity and noise. |
| Approach: | They propose a method that identifies informative tokens by eliminating noise directly in logit space and a new sampling method that is temperature-invariant. |
| Outcome: | The proposed method outperforms existing methods with significant improvements in reasoning and creative writing tasks. |
Incorporating Latent Meanings of Morphological Compositions to Enhance Word Embeddings (P18-1)
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| Challenge: | Existing word embedding methods learn semantic information at word level while neglecting meaningful inner structures of words like morphemes. |
| Approach: | They propose to use latent meanings of morphological compositions of words to train word embeddings. |
| Outcome: | The proposed models outperform baseline models on word similarity, syntactic analogy and text classification tasks. |