# Balancing Specialized and General Skills in LLMs: The Impact of Modern Tuning and Data Strategy

arXiv: https://arxiv.org/abs/2310.04945 · HTML: https://mlsanity.com/paper/2310.04945/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 31 references that are in the map.
- First posted on arXiv 7 Oct 2023.

## Builds on (most cited first, 21 of 21)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding](https://mlsanity.com/paper/1804.07461/) (2018)
- [Large Language Models are Zero-Shot Reasoners](https://mlsanity.com/paper/2205.11916/) (2022)
- [Finetuned Language Models Are Zero-Shot Learners](https://mlsanity.com/paper/2109.01652/) (2021)
- [Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback](https://mlsanity.com/paper/2204.05862/) (2022)
- [Self-Instruct: Aligning Language Models with Self-Generated Instructions](https://mlsanity.com/paper/2212.10560/) (2022)
- [SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems](https://mlsanity.com/paper/1905.00537/) (2019)
- [A Survey on Evaluation of Large Language Models](https://mlsanity.com/paper/2307.03109/) (2023)
- [OpenAssistant Conversations -- Democratizing Large Language Model Alignment](https://mlsanity.com/paper/2304.07327/) (2023)
- [C-Eval: A Multi-Level Multi-Discipline Chinese Evaluation Suite for Foundation Models](https://mlsanity.com/paper/2305.08322/) (2023)
- [Cross-Task Generalization via Natural Language Crowdsourcing Instructions](https://mlsanity.com/paper/2104.08773/) (2021)
- [Pre-trained Models for Natural Language Processing: A Survey](https://mlsanity.com/paper/2003.08271/) (2020)
- [MetaICL: Learning to Learn In Context](https://mlsanity.com/paper/2110.15943/) (2021)
- [Scalable Multi-Hop Relational Reasoning for Knowledge-Aware Question Answering](https://mlsanity.com/paper/2005.00646/) (2020)
- [A Preliminary Evaluation of ChatGPT for Zero-shot Dialogue Understanding](https://mlsanity.com/paper/2304.04256/) (2023)
- [Dynamic Relevance Graph Network for Knowledge-Aware Question Answering](https://mlsanity.com/paper/2209.09947/) (2022)

## Built on by (most cited first, 18 of 18)

- [Large Language Models in Cybersecurity: State-of-the-Art](https://mlsanity.com/paper/2402.00891/) (2024)
- [How to Train Your Advisor: Steering Black-Box LLMs with Advisor Models](https://mlsanity.com/paper/2510.02453/) (2025)
- [Evaluating Large Language Models as Virtual Annotators for Time-series Physical Sensing Data](https://mlsanity.com/paper/2403.01133/) (2024)
- [ChIRAAG: ChatGPT Informed Rapid and Automated Assertion Generation](https://mlsanity.com/paper/2402.00093/) (2024)
- [Balancing Enhancement, Harmlessness, and General Capabilities: Enhancing Conversational LLMs with Direct RLHF](https://mlsanity.com/paper/2403.02513/) (2024)
- [Large Model Strategic Thinking, Small Model Efficiency: Transferring Theory of Mind in Large Language Models](https://mlsanity.com/paper/2408.05241/) (2024)
- [LlamaTurk: Adapting Open-Source Generative Large Language Models for Low-Resource Language](https://mlsanity.com/paper/2405.07745/) (2024)
- [Leveraging the Power of LLMs: A Fine-Tuning Approach for High-Quality Aspect-Based Summarization](https://mlsanity.com/paper/2408.02584/) (2024)
- [Investigating Tax Evasion Emergence Using Dual Large Language Model and Deep Reinforcement Learning Powered Agent-based Simulation](https://mlsanity.com/paper/2501.18177/) (2025)
- [DebateBench: A Challenging Long Context Reasoning Benchmark For Large Language Models](https://mlsanity.com/paper/2502.06279/) (2025)
- [Towards EnergyGPT: A Large Language Model Specialized for the Energy Sector](https://mlsanity.com/paper/2509.07177/) (2025)
- [Evaluating the Efficacy of Large Language Models in Identifying Phishing Attempts](https://mlsanity.com/paper/2404.15485/) (2024)
- [OnlySportsLM: Optimizing Sports-Domain Language Models with SOTA Performance under Billion Parameters](https://mlsanity.com/paper/2409.00286/) (2024)
- [From Hallucinations to Facts: Enhancing Language Models with Curated Knowledge Graphs](https://mlsanity.com/paper/2412.18672/) (2024)
- [ICE-GRT: Instruction Context Enhancement by Generative Reinforcement based Transformers](https://mlsanity.com/paper/2401.02072/) (2024)
- [Mistral-C2F: Coarse to Fine Actor for Analytical and Reasoning Enhancement in RLHF and Effective-Merged LLMs](https://mlsanity.com/paper/2406.08657/) (2024)
- [EvalYaks: Instruction Tuning Datasets and LoRA Fine-tuned Models for Automated Scoring of CEFR B2 Speaking Assessment Transcripts](https://mlsanity.com/paper/2408.12226/) (2024)
- [Open-ended Commonsense Reasoning with Unrestricted Answer Scope](https://mlsanity.com/paper/2310.11672/) (2023)

## Abstract (arXiv)

This paper introduces a multifaceted methodology for fine-tuning and evaluating large language models (LLMs) for specialized monetization tasks. The goal is to balance general language proficiency with domain-specific skills. The methodology has three main components: 1) Carefully blending in-domain and general-purpose data during fine-tuning to achieve an optimal balance between general and specialized capabilities; 2) Designing a comprehensive evaluation framework with 45 questions tailored to assess performance on functionally relevant dimensions like reliability, consistency, and business impact; 3) Analyzing how model size and continual training influence metrics to guide efficient resource allocation during fine-tuning. The paper details the design, data collection, analytical techniques, and results validating the proposed frameworks. It aims to provide businesses and researchers with actionable insights on effectively adapting LLMs for specialized contexts. We also intend to make public the comprehensive evaluation framework, which includes the 45 tailored questions and their respective scoring guidelines, to foster transparency and collaboration in adapting LLMs for specialized tasks.