# Call2Instruct: Automated Pipeline for Generating Q&A Datasets from Call Center Recordings for LLM Fine-Tuning

arXiv: https://arxiv.org/abs/2601.14263 · HTML: https://mlsanity.com/paper/2601.14263/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 24 of its 49 references that are in the map.
- First posted on arXiv 1 Dec 2025.

## Builds on (most cited first, 24 of 24)

- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [Robust Speech Recognition via Large-Scale Weak Supervision](https://mlsanity.com/paper/2212.04356/) (2022)
- [Finetuned Language Models Are Zero-Shot Learners](https://mlsanity.com/paper/2109.01652/) (2021)
- [FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness](https://mlsanity.com/paper/2205.14135/) (2022)
- [A Survey of Large Language Models](https://mlsanity.com/paper/2303.18223/) (2023)
- [Real Time Speech Enhancement in the Waveform Domain](https://mlsanity.com/paper/2006.12847/) (2020)
- [Best Practices and Lessons Learned on Synthetic Data](https://mlsanity.com/paper/2404.07503/) (2024)
- [Data-Juicer: A One-Stop Data Processing System for Large Language Models](https://mlsanity.com/paper/2309.02033/) (2023)
- [Generating Diverse and Consistent QA pairs from Contexts with Information-Maximizing Hierarchical Conditional VAEs](https://mlsanity.com/paper/2005.13837/) (2020)
- [Text-based Question Answering from Information Retrieval and Deep Neural Network Perspectives: A Survey](https://mlsanity.com/paper/2002.06612/) (2020)
- [Towards Faithful and Robust LLM Specialists for Evidence-Based Question-Answering](https://mlsanity.com/paper/2402.08277/) (2024)
- [An Effective, Performant Named Entity Recognition System for Noisy Business Telephone Conversation Transcripts](https://mlsanity.com/paper/2209.13736/) (2022)
- [May I Ask Who's Calling? Named Entity Recognition on Call Center Transcripts for Privacy Law Compliance](https://mlsanity.com/paper/2010.15598/) (2020)
- [Automated question generation and question answering from Turkish texts](https://mlsanity.com/paper/2111.06476/) (2021)
- [Graph Guided Question Answer Generation for Procedural Question-Answering](https://mlsanity.com/paper/2401.13594/) (2024)
- [Intent Recognition in Conversational Recommender Systems](https://mlsanity.com/paper/2212.03721/) (2022)
- [A New Benchmark for Evaluating Automatic Speech Recognition in the Arabic Call Domain](https://mlsanity.com/paper/2403.04280/) (2024)
- [Revisiting Semantic Representation and Tree Search for Similar Question Retrieval](https://mlsanity.com/paper/1908.08326/) (2019)
- [Speaker conditioned acoustic modeling for multi-speaker conversational ASR](https://mlsanity.com/paper/2104.01882/) (2021)
- [A Universality-Individuality Integration Model for Dialog Act Classification](https://mlsanity.com/paper/2204.06185/) (2022)
- [TRScore: A Novel GPT-based Readability Scorer for ASR Segmentation and Punctuation model evaluation and selection](https://mlsanity.com/paper/2210.15104/) (2022)
- [End-to-end Joint Punctuated and Normalized ASR with a Limited Amount of Punctuated Training Data](https://mlsanity.com/paper/2311.17741/) (2023)

## Abstract (arXiv)

The adaptation of Large-Scale Language Models (LLMs) to specific domains depends on high-quality fine-tuning datasets, particularly in instructional format (e.g., Question-Answer - Q&A). However, generating these datasets, particularly from unstructured sources such as call center audio recordings, poses a significant challenge due to the noisy and disorganized nature of the data. This paper presents a solution to this challenge by offering an end-to-end automated pipeline for generating Q&A instructional datasets from such recordings. The methodology developed comprises sequential steps of audio processing (including diarization, noise removal and automatic transcription), textual processing (cleaning, normalization, and anonymization), semantic extraction of customer demands and attendant responses using vector embeddings, and matching via semantic search to form the final Q&A pairs. As a result, the complete pipeline was successfully implemented, generating a dataset specifically formatted for Instruct Fine Tuning. The practical value and feasibility of the generated dataset were substantiated and functionally demonstrated through the successful fine-tuning of an LLM model (based on Llama 2 7B). The conclusion of the paper states that the proposed approach is viable for converting unstructured conversational data from call centers into valuable resources for training LLMs. This development has the potential to open up avenues for creating more effective AI systems for Q&A tasks in the customer service domain. The developed codes have been made publicly available to promote reproducibility and future research.