Auto-Prompting with Retrieval Guidance for Frame Detection in Logistics

• URL: http://arxiv.org/abs/2512.19247v1
• Date: Mon, 22 Dec 2025 10:29:51 GMT
• Title: Auto-Prompting with Retrieval Guidance for Frame Detection in Logistics
• Authors: Do Minh Duc, Quan Xuan Truong, Nguyen Tat Dat, Nguyen Van Vinh,
• Abstract summary: Prompt engineering plays a critical role in adapting large language models (LLMs) to complex reasoning and labeling tasks without the need for extensive fine-tuning.<n>We propose a novel prompt optimization pipeline for frame detection in logistics texts, combining retrieval-augmented generation (RAG), few-shot prompting, chain-of-thought (CoT) reasoning, and automatic CoT synthesis (Auto-CoT) to generate highly effective task-specific prompts.<n>Our framework is evaluated on a real-world logistics text annotation task, where reasoning accuracy and labeling efficiency are critical.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Prompt engineering plays a critical role in adapting large language models (LLMs) to complex reasoning and labeling tasks without the need for extensive fine-tuning. In this paper, we propose a novel prompt optimization pipeline for frame detection in logistics texts, combining retrieval-augmented generation (RAG), few-shot prompting, chain-of-thought (CoT) reasoning, and automatic CoT synthesis (Auto-CoT) to generate highly effective task-specific prompts. Central to our approach is an LLM-based prompt optimizer agent that iteratively refines the prompts using retrieved examples, performance feedback, and internal self-evaluation. Our framework is evaluated on a real-world logistics text annotation task, where reasoning accuracy and labeling efficiency are critical. Experimental results show that the optimized prompts - particularly those enhanced via Auto-CoT and RAG - improve real-world inference accuracy by up to 15% compared to baseline zero-shot or static prompts. The system demonstrates consistent improvements across multiple LLMs, including GPT-4o, Qwen 2.5 (72B), and LLaMA 3.1 (70B), validating its generalizability and practical value. These findings suggest that structured prompt optimization is a viable alternative to full fine-tuning, offering scalable solutions for deploying LLMs in domain-specific NLP applications such as logistics.

Related papers

• ZERA: Zero-init Instruction Evolving Refinement Agent - From Zero Instructions to Structured Prompts via Principle-based Optimization [6.591649491003996]
  ZERA is a novel framework that jointly optimize both system and user prompts.<n>ZERA scores prompts using eight generalizable criteria with automatically inferred weights, and revises prompts based on structured critiques.<n>We evaluate ZERA across five large language models and nine diverse datasets spanning reasoning, summarization, and code generation tasks.
  arXiv  Detail & Related papers  (2025-09-17T01:47:29Z)
• CompassVerifier: A Unified and Robust Verifier for LLMs Evaluation and Outcome Reward [50.97588334916863]
  We develop CompassVerifier, an accurate and robust lightweight verifier model for evaluation and outcome reward.<n>It demonstrates multi-domain competency spanning math, knowledge, and diverse reasoning tasks, with the capability to process various answer types.<n>We introduce VerifierBench benchmark comprising model outputs collected from multiple data sources, augmented through manual analysis of metaerror patterns to enhance CompassVerifier.
  arXiv  Detail & Related papers  (2025-08-05T17:55:24Z)
• Tuning LLM-based Code Optimization via Meta-Prompting: An Industrial Perspective [7.9043190902720974]
  We introduce Meta-Prompted Code Optimization (MPCO), a framework that automatically generates task-specific prompts across diverse large language models (LLMs)<n>MPCO leverages metaprompting to dynamically synthesize context-aware optimization prompts by integrating project metadata, task requirements, and LLM-specific contexts.<n>MPCO achieves overall performance improvements up to 19.06% with the best statistical rank across all systems compared to baseline methods.
  arXiv  Detail & Related papers  (2025-08-02T17:11:40Z)
• Grammar-Guided Evolutionary Search for Discrete Prompt Optimisation [63.97051732013936]
  We propose an evolutionary search approach to automated discrete prompt optimisation consisting of two phases.<n>In the first phase, grammar-guided genetic programming is invoked to synthesise prompt-creating programmes.<n>In the second phase, local search is applied to explore the neighbourhoods of best-performing programmes.
  arXiv  Detail & Related papers  (2025-07-14T14:34:15Z)
• In-context Demonstration Matters: On Prompt Optimization for Pseudo-Supervision Refinement [71.60563181678323]
  Large language models (LLMs) have achieved great success across diverse tasks, and fine-tuning is sometimes needed to further enhance generation quality.<n>To handle these challenges, a direct solution is to generate high-confidence'' data from unsupervised downstream tasks.<n>We propose a novel approach, pseudo-supervised demonstrations aligned prompt optimization (PAPO) algorithm, which jointly refines both the prompt and the overall pseudo-supervision.
  arXiv  Detail & Related papers  (2024-10-04T03:39:28Z)
• Enhancing LLM-Based Text Classification in Political Science: Automatic Prompt Optimization and Dynamic Exemplar Selection for Few-Shot Learning [1.6967824074619953]
  Large language models (LLMs) offer substantial promise for text classification in political science.<n>Our framework enhances LLM performance through automatic prompt optimization, dynamic exemplar selection, and a consensus mechanism.<n>An open-source Python package (PoliPrompt) is available on GitHub.
  arXiv  Detail & Related papers  (2024-09-02T21:05:31Z)
• QPO: Query-dependent Prompt Optimization via Multi-Loop Offline Reinforcement Learning [58.767866109043055]
  We introduce Query-dependent Prompt Optimization (QPO), which iteratively fine-tune a small pretrained language model to generate optimal prompts tailored to the input queries.<n>We derive insights from offline prompting demonstration data, which already exists in large quantities as a by-product of benchmarking diverse prompts on open-sourced tasks.<n> Experiments on various LLM scales and diverse NLP and math tasks demonstrate the efficacy and cost-efficiency of our method in both zero-shot and few-shot scenarios.
  arXiv  Detail & Related papers  (2024-08-20T03:06:48Z)
• Intent-based Prompt Calibration: Enhancing prompt optimization with synthetic boundary cases [2.6159111710501506]
  We introduce a new method for automatic prompt engineering, using a calibration process that iteratively refines the prompt to the user intent. We demonstrate the effectiveness of our method with respect to strong proprietary models on real-world tasks such as moderation and generation.
  arXiv  Detail & Related papers  (2024-02-05T15:28:43Z)
• Robust Prompt Optimization for Large Language Models Against Distribution Shifts [80.6757997074956]
  Large Language Model (LLM) has demonstrated significant ability in various Natural Language Processing tasks. We propose a new problem of robust prompt optimization for LLMs against distribution shifts. This problem requires the prompt optimized over the labeled source group can simultaneously generalize to an unlabeled target group.
  arXiv  Detail & Related papers  (2023-05-23T11:30:43Z)
• Guiding Large Language Models via Directional Stimulus Prompting [114.84930073977672]
  We introduce Directional Stimulus Prompting, a novel framework for guiding black-box large language models (LLMs) toward specific desired outputs. Instead of directly adjusting LLMs, our method employs a small tunable policy model to generate an auxiliary directional stimulus prompt for each input instance.
  arXiv  Detail & Related papers  (2023-02-22T17:44:15Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.