# LAB: Large-Scale Alignment for ChatBots

arXiv: https://arxiv.org/abs/2403.01081 · HTML: https://mlsanity.com/paper/2403.01081/

## Key facts

- Cited by 39 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 22 references that are in the map.
- First posted on arXiv 2 Mar 2024.

## Builds on (most cited first, 16 of 16)

- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [Training Verifiers to Solve Math Word Problems](https://mlsanity.com/paper/2110.14168/) (2021)
- [Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena](https://mlsanity.com/paper/2306.05685/) (2023)
- [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://mlsanity.com/paper/2305.18290/) (2023)
- [Measuring Massive Multitask Language Understanding](https://mlsanity.com/paper/2009.03300/) (2020)
- [Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge](https://mlsanity.com/paper/1803.05457/) (2018)
- [HellaSwag: Can a Machine Really Finish Your Sentence?](https://mlsanity.com/paper/1905.07830/) (2019)
- [Finetuned Language Models Are Zero-Shot Learners](https://mlsanity.com/paper/2109.01652/) (2021)
- [Self-Instruct: Aligning Language Models with Self-Generated Instructions](https://mlsanity.com/paper/2212.10560/) (2022)
- [WizardLM: Empowering large pre-trained language models to follow complex instructions](https://mlsanity.com/paper/2304.12244/) (2023)
- [Zephyr: Direct Distillation of LM Alignment](https://mlsanity.com/paper/2310.16944/) (2023)
- [MAmmoTH: Building Math Generalist Models through Hybrid Instruction Tuning](https://mlsanity.com/paper/2309.05653/) (2023)
- [The False Promise of Imitating Proprietary LLMs](https://mlsanity.com/paper/2305.15717/) (2023)
- [Learning to Mine Aligned Code and Natural Language Pairs from Stack Overflow](https://mlsanity.com/paper/1805.08949/) (2018)
- [Orca 2: Teaching Small Language Models How to Reason](https://mlsanity.com/paper/2311.11045/) (2023)

## Built on by (most cited first, 30 of 39)

- [On LLMs-Driven Synthetic Data Generation, Curation, and Evaluation: A Survey](https://mlsanity.com/paper/2406.15126/) (2024)
- [Programming Refusal with Conditional Activation Steering](https://mlsanity.com/paper/2409.05907/) (2024)
- [SEAL: Safety-enhanced Aligned LLM Fine-tuning via Bilevel Data Selection](https://mlsanity.com/paper/2410.07471/) (2024)
- [Granite-Function Calling Model: Introducing Function Calling Abilities via Multi-task Learning of Granular Tasks](https://mlsanity.com/paper/2407.00121/) (2024)
- [On Data Engineering for Scaling LLM Terminal Capabilities](https://mlsanity.com/paper/2602.21193/) (2026)
- [A Survey on Data Synthesis and Augmentation for Large Language Models](https://mlsanity.com/paper/2410.12896/) (2024)
- [Unveiling the Secret Recipe: A Guide For Supervised Fine-Tuning Small LLMs](https://mlsanity.com/paper/2412.13337/) (2024)
- [Queue management for slo-oriented large language model serving](https://mlsanity.com/paper/2407.00047/) (2024)
- [Docling Technical Report](https://mlsanity.com/paper/2408.09869/) (2024)
- [DesignQA: A Multimodal Benchmark for Evaluating Large Language Models' Understanding of Engineering Documentation](https://mlsanity.com/paper/2404.07917/) (2024)
- [Towards AI-Native Software Engineering (SE 3.0): A Vision and a Challenge Roadmap](https://mlsanity.com/paper/2410.06107/) (2024)
- [Large Language Models Meet Symbolic Provers for Logical Reasoning Evaluation](https://mlsanity.com/paper/2502.06563/) (2025)
- [Holmes: A Benchmark to Assess the Linguistic Competence of Language Models](https://mlsanity.com/paper/2404.18923/) (2024)
- [Distributional Preference Alignment of LLMs via Optimal Transport](https://mlsanity.com/paper/2406.05882/) (2024)
- [Self-MoE: Towards Compositional Large Language Models with Self-Specialized Experts](https://mlsanity.com/paper/2406.12034/) (2024)
- [Naturally Occurring Feedback is Common, Extractable and Useful](https://mlsanity.com/paper/2407.10944/) (2024)
- [textitTrans-LoRA: towards data-free Transferable Parameter Efficient Finetuning](https://mlsanity.com/paper/2405.17258/) (2024)
- [Systematic Knowledge Injection into Large Language Models via Diverse Augmentation for Domain-Specific RAG](https://mlsanity.com/paper/2502.08356/) (2025)
- [Knowledge Base Construction for Knowledge-Augmented Text-to-SQL](https://mlsanity.com/paper/2505.22096/) (2025)
- [Multi-Document Grounded Multi-Turn Synthetic Dialog Generation](https://mlsanity.com/paper/2409.11500/) (2024)
- [Middo: Model-Informed Dynamic Data Optimization for Enhanced LLM Fine-Tuning via Closed-Loop Learning](https://mlsanity.com/paper/2508.21589/) (2025)
- [A Survey of LLM × DATA](https://mlsanity.com/paper/2505.18458/) (2025)
- [Scopes of Alignment](https://mlsanity.com/paper/2501.12405/) (2025)
- [Establishing a Scale for Kullback-Leibler Divergence in Language Models Across Various Settings](https://mlsanity.com/paper/2505.15353/) (2025)
- [Localizing Persona Representations in LLMs](https://mlsanity.com/paper/2505.24539/) (2025)
- [OneShield -- the Next Generation of LLM Guardrails](https://mlsanity.com/paper/2507.21170/) (2025)
- [OnlySportsLM: Optimizing Sports-Domain Language Models with SOTA Performance under Billion Parameters](https://mlsanity.com/paper/2409.00286/) (2024)
- [Retrieval Augmented Generation-Based Incident Resolution Recommendation System for IT Support](https://mlsanity.com/paper/2409.13707/) (2024)
- [Reducing the Scope of Language Models](https://mlsanity.com/paper/2410.21597/) (2024)
- [Feedback-Driven Vision-Language Alignment with Minimal Human Supervision](https://mlsanity.com/paper/2501.04568/) (2025)

## Abstract (arXiv)

This work introduces LAB (Large-scale Alignment for chatBots), a novel methodology designed to overcome the scalability challenges in the instruction-tuning phase of large language model (LLM) training. Leveraging a taxonomy-guided synthetic data generation process and a multi-phase tuning framework, LAB significantly reduces reliance on expensive human annotations and proprietary models like GPT-4. We demonstrate that LAB-trained models can achieve competitive performance across several benchmarks compared to models trained with traditional human-annotated or GPT-4 generated synthetic data. Thus offering a scalable, cost-effective solution for enhancing LLM capabilities and instruction-following behaviors without the drawbacks of catastrophic forgetting, marking a step forward in the efficient training of LLMs for a wide range of applications.