SRICL combines semantic retrieval from ESCO, in-context learning, fine-tuning, and output verification to achieve higher STRICT-F1 scores and fewer invalid or hallucinated skill spans than GPT-3.5 baselines on six public job-ad datasets.
Sketch- guided constrained decoding for boosting blackbox large language models without logit access
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TreeCoder improves LLM code generation accuracy by representing decoding as an optimizable tree search over programs with first-class constraints for syntax, style, and execution, outperforming baselines on MBPP and SQL-Spider.
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Job Skill Extraction via LLM-Centric Multi-Module Framework
SRICL combines semantic retrieval from ESCO, in-context learning, fine-tuning, and output verification to achieve higher STRICT-F1 scores and fewer invalid or hallucinated skill spans than GPT-3.5 baselines on six public job-ad datasets.
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TreeCoder: Systematic Exploration and Optimisation of Decoding and Constraints for LLM Code Generation
TreeCoder improves LLM code generation accuracy by representing decoding as an optimizable tree search over programs with first-class constraints for syntax, style, and execution, outperforming baselines on MBPP and SQL-Spider.