Advances in Neural Semantic Parsing and Language Model Development

The field of natural language processing is witnessing significant advancements in neural semantic parsing and language model development. Researchers are exploring the capabilities and limitations of neural semantic parsers in resolving complex linguistic phenomena, such as ellipsis resolution. Meanwhile, the development of language models is being studied through the lens of embryology, revealing the emergence of internal computational structures and novel mechanisms. The neurocognitive mechanisms underlying syntax are also being investigated, with findings suggesting that distinct mechanisms support different types of syntactic constructions. Noteworthy papers in this area include:

  • The paper on Embryology of a Language Model, which introduces a novel approach to visualizing the structural development of language models.
  • The paper on Evaluation of LLMs in AMR Parsing, which demonstrates the competitive performance of finetuned large language models in semantic parsing tasks.
  • The paper on Pruning Large Language Models by Identifying and Preserving Functional Networks, which proposes a method for efficient model pruning by preserving functional networks within language models.

Sources

Is neural semantic parsing good at ellipsis resolution, or isn't it?

Embryology of a Language Model

Merge-based syntax is mediated by distinct neurocognitive mechanisms: A clustering analysis of comprehension abilities in 84,000 individuals with language deficits across nine languages

Probing Syntax in Large Language Models: Successes and Remaining Challenges

Evaluation of LLMs in AMR Parsing

Pruning Large Language Models by Identifying and Preserving Functional Networks

Optimal Brain Connection: Towards Efficient Structural Pruning

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