Papers with functional

2 papers
ArchCode: Incorporating Software Requirements in Code Generation with Large Language Models (2024.acl-long)

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Challenge: Despite the critical role of software requirements, these criteria have not been studied actively in previous code generation works.
Approach: They propose a framework that leverages in-context learning to organize and extrapolate unexpressed requirements from textual descriptions.
Outcome: The proposed framework generates functional requirements from textual descriptions and extrapolates unexpressed requirements from them.
From Language to Cognition: How LLMs Outgrow the Human Language Network (2025.emnlp-main)

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Challenge: Large language models exhibit remarkable similarity to neural activity in the human language network, but their properties remain unclear.
Approach: They benchmark 34 training checkpoints spanning 300B tokens across 8 different model sizes . they find that brain alignment tracks the development of formal linguistic competence more closely than functional linguistic competency.
Outcome: The results show that large language models exhibit similarity to human language networks . they show that the correlation between next-word prediction and brain alignment fades once models surpass human language proficiency.

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