mlsanityData to 7 Oct 2026

Fine-tuning on simulated data outperforms prompting for agent tone of voice

arXiv 2507.04889 ↗First posted 7 Jul 2025cs.LGcited by 0

On the map: LLM agents and tool use → General LLM agent frameworks and benchmarks

Abstract (arXiv)

Deploying language models (LMs) in customer-facing speech applications requires conversational fluency and adherence to specific stylistic guidelines. This can be challenging to achieve reliably using complex system prompts due to issues like instruction following limitations and in-context bias. This study investigates the effectiveness of fine-tuning versus system prompting for aligning LMs with a specific behavioral target: responding in a natural, conversational tone suitable for voice interactions. We fine-tuned a small, open-weights model (`Llama3.2-1B-Instruct`) using Low-Rank Adaptation (LoRA) on a synthetically generated dataset derived from Wikipedia. Additionally, we fine-tuned two closed-source models (`gpt-4o-mini`, `gpt-4.1-mini`). Our results demonstrate that fine-tuning outperformed system prompting, achieving a high percentage of conversational responses, even when trained on only 100 data samples. Semantic similarity analysis confirmed that fine-tuning did not degrade content quality. Interestingly, fine-tuning with 8-bit integer quantization converged faster towards the target style than using bfloat16 precision, potentially due to implicit regularization effects. We conclude that fine-tuning small, open-weights LMs on simulated data is a highly effective and data-efficient method for instilling specific stylistic behaviors, offering a preferable alternative to complex system prompting for practical applications requiring nuanced response styles.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in General LLM agent frameworks and benchmarks, one of 16 directions in LLM agents and tool use. Builds on 9 of its 11 references that are in the map. First posted on arXiv 7 Jul 2025.

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9 of 11 references.

References · 9

  1. 2020Language Models are Few-Shot Learnerscited by 32,199
  2. 2021LoRA: Low-Rank Adaptation of Large Language Modelscited by 14,084
  3. 2022Robust Speech Recognition via Large-Scale Weak Supervisioncited by 3,972
  4. 2021Calibrate Before Use: Improving Few-Shot Performance of Language Modelscited by 1,371
  5. 2021Fantastically Ordered Prompts and Where to Find Them: Overcoming Few-Shot Prompt Order Sensitivitycited by 1,122
  6. 2023Towards General Text Embeddings with Multi-stage Contrastive Learningcited by 705
  7. 2024Benchmarking Complex Instruction-Following with Multiple Constraints Compositioncited by 147
  8. 2024Efficient multi-prompt evaluation of LLMscited by 72
  9. 2025Predictive Prompt Analysiscited by 1

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  2. 2023An Emulator for Fine-Tuning Large Language Models using Small Language Models
  3. 2023FireAct: Toward Language Agent Fine-tuning
  4. 2024The Ultimate Guide to Fine-Tuning LLMs from Basics to Breakthroughs: An Exhaustive Review of Technologies, Research, Best Practices, Applied Research Challenges and Opportunities
  5. 2023Understanding Catastrophic Forgetting in Language Models via Implicit Inference
  6. 2024Scaling Data Diversity for Fine-Tuning Language Models in Human Alignment
  7. 2025PAFT: Prompt-Agnostic Fine-Tuning
  8. 2025KnowsLM: A framework for evaluation of small language models for knowledge augmentation and humanised conversations