mlsanityData to 7 Oct 2026

Meta-Tuning LLMs to Leverage Lexical Knowledge for Generalizable Language Style Understanding

arXiv 2305.14592 ↗First posted 24 May 2023cs.CL cs.LGcited by 4

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Abstract (arXiv)

Language style is often used by writers to convey their intentions, identities, and mastery of language. In this paper, we show that current large language models struggle to capture some language styles without fine-tuning. To address this challenge, we investigate whether LLMs can be meta-trained based on representative lexicons to recognize new styles they have not been fine-tuned on. Experiments on 13 established style classification tasks, as well as 63 novel tasks generated using LLMs, demonstrate that meta-training with style lexicons consistently improves zero-shot transfer across styles. We release the code and data at http://github.com/octaviaguo/Style-LLM .

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 32 of its 57 references that are in the map. First posted on arXiv 24 May 2023.

Builds on

32 of 57 references.

References · 32

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20 more
  1. 2022The case for 4-bit precision: k-bit Inference Scaling Lawscited by 247
  2. 2018Dear Sir or Madam, May I introduce the GYAFC Dataset: Corpus, Benchmarks and Metrics for Formality Style Transfercited by 209
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 3; 2026: 1.

4 papers.

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  2. 2025Tree-based Dialogue Reinforced Policy Optimization for Red-Teaming Attackscited by 8
  3. 2025Bilevel ZOFO: Efficient LLM Fine-Tuning and Meta-Trainingcited by 5
  4. 2026Investigating and Alleviating Harm Amplification in LLM Interactionscited by 2