Task-oriented Prompt Enhancement via Script Generation

Large Language Models (LLMs) have demonstrated remarkable abilities across various tasks, leveraging advanced reasoning. Yet, they struggle with task-oriented prompts due to a lack of specific prior knowledge of the task answers. The current state-of-the-art approach, PAL, utilizes code generation to address this issue. However, PAL depends on manually crafted prompt templates and examples while still producing inaccurate results. In this work, we present TITAN-a novel strategy designed to enhance LLMs' performance on task-oriented prompts. TITAN achieves this by generating scripts using a universal approach and zero-shot learning. Unlike existing methods, TITAN eliminates the need for detailed task-specific instructions and extensive manual efforts. TITAN enhances LLMs' performance on various tasks by utilizing their analytical and code-generation capabilities in a streamlined process. TITAN employs two key techniques: (1) step-back prompting to extract the task's input specifications and (2) chain-of-thought prompting to identify required procedural steps. This information is used to improve the LLMs' code-generation process. TITAN further refines the generated script through post-processing and the script is executed to retrieve the final answer. Our comprehensive evaluation demonstrates TITAN's effectiveness in a diverse set of tasks. On average, TITAN outperforms the state-of-the-art zero-shot approach by 7.6% and 3.9% when paired with GPT-3.5 and GPT-4. Overall, without human annotation, TITAN achieves state-of-the-art performance in 8 out of 11 cases while only marginally losing to few-shot approaches (which needed human intervention) on three occasions by small margins. This work represents a significant advancement in addressing task-oriented prompts, offering a novel solution for effectively utilizing LLMs in everyday life tasks.

Selection of Prompt Engineering Techniques for Code Generation through Predicting Code Complexity

Large Language Models (LLMs) have demonstrated impressive performance in software engineering tasks. However, improving their accuracy in generating correct and reliable code remains challenging. Numerous prompt engineering techniques (PETs) have been developed to address this, but no single approach is universally optimal. Selecting the right PET for each query is difficult for two primary reasons: (1) interactive prompting techniques may not consistently deliver the expected benefits, especially for simpler queries, and (2) current automated prompt engineering methods lack adaptability and fail to fully utilize multi-stage responses. To overcome these challenges, we propose PET-Select, a PET-agnostic selection model that uses code complexity as a proxy to classify queries and select the most appropriate PET. By incorporating contrastive learning, PET-Select effectively distinguishes between simple and complex problems, allowing it to choose PETs that are best suited for each query's complexity level. Our evaluations on the MBPP and HumanEval benchmarks using GPT-3.5 Turbo and GPT-4o show up to a 1.9% improvement in pass@1 accuracy, along with a 74.8% reduction in token usage. Additionally, we provide both quantitative and qualitative results to demonstrate how PET-Select effectively selects the most appropriate techniques for each code generation query, further showcasing its efficiency in optimizing PET selection.

Can ChatGPT Support Developers? An Empirical Evaluation of Large Language Models for Code Generation

Large language models (LLMs) have demonstrated notable proficiency in code generation, with numerous prior studies showing their promising capabilities in various development scenarios. However, these studies mainly provide evaluations in research settings, which leaves a significant gap in understanding how effectively LLMs can support developers in real-world. To address this, we conducted an empirical analysis of conversations in DevGPT, a dataset collected from developers' conversations with ChatGPT (captured with the Share Link feature on platforms such as GitHub). Our empirical findings indicate that the current practice of using LLM-generated code is typically limited to either demonstrating high-level concepts or providing examples in documentation, rather than to be used as production-ready code. These findings indicate that there is much future work needed to improve LLMs in code generation before they can be integral parts of modern software development.