# Specialist or Generalist? Instruction Tuning for Specific NLP Tasks

arXiv: https://arxiv.org/abs/2310.15326 · HTML: https://mlsanity.com/paper/2310.15326/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- Builds on 29 of its 48 references that are in the map.
- First posted on arXiv 23 Oct 2023.

## Builds on (most cited first, 29 of 29)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [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)
- [Scaling Instruction-Finetuned Language Models](https://mlsanity.com/paper/2210.11416/) (2022)
- [TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehension](https://mlsanity.com/paper/1705.03551/) (2017)
- [The Curious Case of Neural Text Degeneration](https://mlsanity.com/paper/1904.09751/) (2019)
- [Learning to summarize from human feedback](https://mlsanity.com/paper/2009.01325/) (2020)
- [Character-level Convolutional Networks for Text Classification](https://mlsanity.com/paper/1509.01626/) (2015)
- [A large annotated corpus for learning natural language inference](https://mlsanity.com/paper/1508.05326/) (2015)
- [CommonsenseQA: A Question Answering Challenge Targeting Commonsense Knowledge](https://mlsanity.com/paper/1811.00937/) (2018)
- [WizardLM: Empowering large pre-trained language models to follow complex instructions](https://mlsanity.com/paper/2304.12244/) (2023)
- [LIMA: Less Is More for Alignment](https://mlsanity.com/paper/2305.11206/) (2023)
- [Super-NaturalInstructions: Generalization via Declarative Instructions on 1600+ NLP Tasks](https://mlsanity.com/paper/2204.07705/) (2022)
- [RACE: Large-scale ReAding Comprehension Dataset From Examinations](https://mlsanity.com/paper/1704.04683/) (2017)
- [Program Induction by Rationale Generation : Learning to Solve and Explain Algebraic Word Problems](https://mlsanity.com/paper/1705.04146/) (2017)
- [PandaGPT: One Model To Instruction-Follow Them All](https://mlsanity.com/paper/2305.16355/) (2023)
- [The False Promise of Imitating Proprietary LLMs](https://mlsanity.com/paper/2305.15717/) (2023)
- [A Contrastive Framework for Neural Text Generation](https://mlsanity.com/paper/2202.06417/) (2022)
- [InstructUIE: Multi-task Instruction Tuning for Unified Information Extraction](https://mlsanity.com/paper/2304.08085/) (2023)
- [A comprehensive evaluation of ChatGPT's zero-shot Text-to-SQL capability](https://mlsanity.com/paper/2303.13547/) (2023)
- [Is ChatGPT a Highly Fluent Grammatical Error Correction System? A Comprehensive Evaluation](https://mlsanity.com/paper/2304.01746/) (2023)
- [Contrastive Search Is What You Need For Neural Text Generation](https://mlsanity.com/paper/2210.14140/) (2022)
- [Adapting Large Language Models to Domains via Reading Comprehension](https://mlsanity.com/paper/2309.09530/) (2023)
- [Multi-Task Instruction Tuning of LLaMa for Specific Scenarios: A Preliminary Study on Writing Assistance](https://mlsanity.com/paper/2305.13225/) (2023)
- [A Frustratingly Simple Decoding Method for Neural Text Generation](https://mlsanity.com/paper/2305.12675/) (2023)

## Built on by (most cited first, 17 of 17)

- [InsCL: A Data-efficient Continual Learning Paradigm for Fine-tuning Large Language Models with Instructions](https://mlsanity.com/paper/2403.11435/) (2024)
- [SciRIFF: A Resource to Enhance Language Model Instruction-Following over Scientific Literature](https://mlsanity.com/paper/2406.07835/) (2024)
- [Unveiling the Generalization Power of Fine-Tuned Large Language Models](https://mlsanity.com/paper/2403.09162/) (2024)
- [CommonIT: Commonality-Aware Instruction Tuning for Large Language Models via Data Partitions](https://mlsanity.com/paper/2410.03077/) (2024)
- [LazyReview A Dataset for Uncovering Lazy Thinking in NLP Peer Reviews](https://mlsanity.com/paper/2504.11042/) (2025)
- [Mitigating Reversal Curse in Large Language Models via Semantic-aware Permutation Training](https://mlsanity.com/paper/2403.00758/) (2024)
- [KnowMol: Advancing Molecular Large Language Models with Multi-Level Chemical Knowledge](https://mlsanity.com/paper/2510.19484/) (2025)
- [Hint-enhanced In-Context Learning wakes Large Language Models up for knowledge-intensive tasks](https://mlsanity.com/paper/2311.01949/) (2023)
- [Instruction Matters: A Simple yet Effective Task Selection for Optimized Instruction Tuning of Specific Tasks](https://mlsanity.com/paper/2404.16418/) (2024)
- [Stay Tuned: An Empirical Study of the Impact of Hyperparameters on LLM Tuning in Real-World Applications](https://mlsanity.com/paper/2407.18990/) (2024)
- [SPICA: Retrieving Scenarios for Pluralistic In-Context Alignment](https://mlsanity.com/paper/2411.10912/) (2024)
- [ConsumerBench: Benchmarking Generative AI Applications on End-User Devices](https://mlsanity.com/paper/2506.17538/) (2025)
- [Colon-X: Advancing Intelligent Colonoscopy toward Clinical Reasoning](https://mlsanity.com/paper/2512.03667/) (2025)
- [Group Selection as a Safeguard Against AI Substitution](https://mlsanity.com/paper/2602.03541/) (2026)
- [An Energy-based Model for Word-level AutoCompletion in Computer-aided Translation](https://mlsanity.com/paper/2407.20083/) (2024)
- [FineInstructions: Scaling Synthetic Instructions to Pre-Training Scale](https://mlsanity.com/paper/2601.22146/) (2026)
- [From Abstract to Contextual: What LLMs Still Cannot Do in Mathematics](https://mlsanity.com/paper/2601.23048/) (2026)

## Abstract (arXiv)

The potential of large language models (LLMs) to simultaneously perform a wide range of natural language processing (NLP) tasks has been the subject of extensive research. Although instruction tuning has proven to be a data-efficient method for transforming LLMs into such generalist models, their performance still lags behind specialist models trained exclusively for specific tasks. In this paper, we investigate whether incorporating broad-coverage generalist instruction tuning can contribute to building a specialist model. We hypothesize that its efficacy depends on task specificity and skill requirements. Our experiments assess four target tasks with distinct coverage levels, revealing that integrating generalist instruction tuning consistently enhances model performance when the task coverage is broad. The effect is particularly pronounced when the amount of task-specific training data is limited. Further investigation into three target tasks focusing on different capabilities demonstrates that generalist instruction tuning improves understanding and reasoning abilities. However, for tasks requiring factual knowledge, generalist data containing hallucinatory information may negatively affect the model's performance. Overall, our work provides a systematic guide for developing specialist models with general instruction tuning. Our code and other related resources can be found at https://github.com/DavidFanzz/Generalist_or_Specialist.