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Can Post-Training Turn LLMs into Good Medical Coders? An Empirical Study of Generative ICD Coding
Arxiv
0+阅读 · 6月11日
Prompt engineering for bibliographic web-scraping
Arxiv
0+阅读 · 1月9日
Augment Engineering: A Methodology for Multi-Tool AI Orchestration Across Professional Domains
Arxiv
0+阅读 · 5月22日
APEX: Automated Prompt Engineering eXpert with Dynamic Data Selection
Arxiv
0+阅读 · 6月9日
Navigating the Prompt Space: Improving LLM Classification of Social Science Texts Through Prompt Engineering
Arxiv
0+阅读 · 3月26日
Optimizing LLM Prompt Engineering with DSPy Based Declarative Learning
Arxiv
0+阅读 · 4月6日
Inference-Time Distillation: Cost-Efficient Agents Without Fine-Tuning or Manual Prompt Engineering
Arxiv
0+阅读 · 4月20日
SYMDIREC: A Neuro-Symbolic Divide-Retrieve-Conquer Framework for Enhanced RTL Synthesis and Summarization
SYMDIREC: A Neuro-Symbolic Divide-Retrieve-Conquer Framework for Enhanced RTL Synthesis and Summarization
Arxiv
0+阅读 · 3月17日
Prompt Programming for Cultural Bias and Alignment of Large Language Models
Arxiv
0+阅读 · 3月17日
Generation of Programming Exam Question and Answer Using ChatGPT Based on Prompt Engineering
Arxiv
0+阅读 · 3月16日
Prompt Readiness Levels (PRL): a maturity scale and scoring framework for production grade prompt assets
Arxiv
0+阅读 · 3月16日
Customizing ChatGPT for Second Language Speaking Practice: Genuine Support or Just a Marketing Gimmick?
Arxiv
0+阅读 · 3月16日
What You Prompt is What You Get: Increasing Transparency of Prompting Using Prompt Cards
Arxiv
0+阅读 · 3月13日
Evaluating Prompt Engineering Strategies for Sentiment Control in AI-Generated Texts
Arxiv
0+阅读 · 2月6日
Paraphrase Types Elicit Prompt Engineering Capabilities
Arxiv
0+阅读 · 2月15日
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