LLMs as Linguistic Probes: A Graduate Student's Guide to Advanced Syntax, Semantics, and Efficient Fine-Tuning
The article provides a guide for graduate students on using large language models (LLMs) as tools for probing advanced syntax, semantics, and efficient fine-tuning in linguistic research. It highlights key benchmarks like Holmes and the Two Word Test (TWT), which reveal that linguistic competence in LLMs scales with model size but plateaus for simpler tasks and that models struggle with contextual disambiguation. The article also discusses architectural trade-offs and introduces SENSE prompting as a method to improve semantic parsing integration.
- ▪The Holmes benchmark shows that linguistic competence in LLMs strongly correlates with model size, especially for syntactic tasks, but performance gains diminish beyond a certain scale.
- ▪The Two Word Test (TWT) reveals that LLMs struggle with contextual disambiguation in simple phrases, indicating limited robust lexical semantics.
- ▪SENSE prompting improves semantic parsing by embedding hints within the prompt structure rather than appending them, preserving the model's holistic comprehension.
- ▪Encoder-decoder models like T5 and BART are recommended for syntax and semantic parsing due to bidirectional understanding, while decoder-only models like GPT and LLaMA suit language generation tasks.
- ▪Hybrid architectures such as Mamba/Transformer offer lower memory costs and balanced performance but have less community support and tooling.
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try { if(localStorage) { let currentUser = localStorage.getItem('current_user'); if (currentUser) { currentUser = JSON.parse(currentUser); if (currentUser.id === 3855371) { document.getElementById('article-show-container').classList.add('current-user-is-article-author'); } } } } catch (e) { console.error(e); } Ismail zamareh Posted on May 17 LLMs as Linguistic Probes: A Graduate Student's Guide to Advanced Syntax, Semantics, and Efficient Fine-Tuning #largelanguagemodels #computationallinguistics #nlpresearch #finetuning The intersection of large language models (LLMs) and advanced linguistics has moved beyond philosophical debate into rigorous empirical territory.
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