Automatic Prompt Engineering with No Task Cues and No Tuning
This paper presents a system for automatic prompt engineering that is much simpler in both design and application and yet as effective as the existing approaches. It requires no tuning and no explicit clues about the task. We evaluated our approach on cryptic column name expansion (CNE) in database tables, a task which is critical for tabular data search, access, and understanding and yet there has been very little existing work. We evaluated on datasets in two languages, English and German. This is the first work to report on the application of automatic prompt engineering for the CNE task. To the best of our knowledge, this is also the first work on the application of automatic prompt engineering for a language other than English.
Code (0)
등록된 구현이 없습니다.
Tasks
Prompt EngineeringSimilar Papers 제목 키워드 기반
Beyond Scores: A Modular RAG-Based System for Automatic Short Answer Scoring with Feedback
Automatic short answer scoring (ASAS) helps reduce the grading burden on educators but often lacks detailed, explainable feedback. Existing methods in ASAS with feedback (ASAS-F) rely on fine-tuning language models with …
Few-Shot LearningPrompt EngineeringRAGRetrieval-augmented GenerationPromptomatix: An Automatic Prompt Optimization Framework for Large Language Models
Large Language Models (LLMs) perform best with well-crafted prompts, yet prompt engineering remains manual, inconsistent, and inaccessible to non-experts. We introduce Promptomatix, an automatic prompt optimization frame…
Prompt EngineeringCQARE: Contrastive Question-Answering for Few-shot Relation Extraction with Prompt Tuning
Prompt tuning with pre-trained language models (PLM) has exhibited outstanding performance by closing the gap between pre-training tasks and various downstream applications, without the need for uninitialized parameters …
Cross-Domain Few-ShotPrompt EngineeringQuestion AnsweringRelation+1A Survey of Automatic Prompt Engineering: An Optimization Perspective
The rise of foundation models has shifted focus from resource-intensive fine-tuning to prompt engineering, a paradigm that steers model behavior through input design rather than weight updates. While manual prompt engine…
cross-modal alignmentPrompt EngineeringSurveyOptimizing Prompts for Text-to-Image Generation
Well-designed prompts can guide text-to-image models to generate amazing images. However, the performant prompts are often model-specific and misaligned with user input. Instead of laborious human engineering, we propose…
Language ModelingLanguage ModellingPrompt Engineeringreinforcement-learning+3