# Multi-Task Instruction Tuning of LLaMa for Specific Scenarios: A Preliminary Study on Writing Assistance

arXiv: https://arxiv.org/abs/2305.13225 · HTML: https://mlsanity.com/paper/2305.13225/

## Key facts

- Cited by 33 papers in the map (data through 14 Oct 2026).
- Builds on 50 of its 69 references that are in the map.
- First posted on arXiv 22 May 2023.

## Builds on (most cited first, 30 of 50)

- [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)
- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Scaling Laws for Neural Language Models](https://mlsanity.com/paper/2001.08361/) (2020)
- [PaLM: Scaling Language Modeling with Pathways](https://mlsanity.com/paper/2204.02311/) (2022)
- [Finetuned Language Models Are Zero-Shot Learners](https://mlsanity.com/paper/2109.01652/) (2021)
- [OPT: Open Pre-trained Transformer Language Models](https://mlsanity.com/paper/2205.01068/) (2022)
- [Scaling Instruction-Finetuned Language Models](https://mlsanity.com/paper/2210.11416/) (2022)
- [Self-Instruct: Aligning Language Models with Self-Generated Instructions](https://mlsanity.com/paper/2212.10560/) (2022)
- [BLOOM: A 176B-Parameter Open-Access Multilingual Language Model](https://mlsanity.com/paper/2211.05100/) (2022)
- [LIMA: Less Is More for Alignment](https://mlsanity.com/paper/2305.11206/) (2023)
- [SemEval-2017 Task 1: Semantic Textual Similarity - Multilingual and Cross-lingual Focused Evaluation](https://mlsanity.com/paper/1708.00055/) (2017)
- [BloombergGPT: A Large Language Model for Finance](https://mlsanity.com/paper/2303.17564/) (2023)
- [A Multitask, Multilingual, Multimodal Evaluation of ChatGPT on Reasoning, Hallucination, and Interactivity](https://mlsanity.com/paper/2302.04023/) (2023)
- [Instruction Tuning with GPT-4](https://mlsanity.com/paper/2304.03277/) (2023)
- [Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model](https://mlsanity.com/paper/2201.11990/) (2022)
- [Pre-trained Models for Natural Language Processing: A Survey](https://mlsanity.com/paper/2003.08271/) (2020)
- [Is ChatGPT a General-Purpose Natural Language Processing Task Solver?](https://mlsanity.com/paper/2302.06476/) (2023)
- [Unnatural Instructions: Tuning Language Models with (Almost) No Human Labor](https://mlsanity.com/paper/2212.09689/) (2022)
- [Towards Expert-Level Medical Question Answering with Large Language Models](https://mlsanity.com/paper/2305.09617/) (2023)
- [Check Your Facts and Try Again: Improving Large Language Models with External Knowledge and Automated Feedback](https://mlsanity.com/paper/2302.12813/) (2023)
- [Multi-Task Deep Neural Networks for Natural Language Understanding](https://mlsanity.com/paper/1901.11504/) (2019)
- [ChatIE: Zero-Shot Information Extraction via Chatting with ChatGPT](https://mlsanity.com/paper/2302.10205/) (2023)
- [BlenderBot 3: a deployed conversational agent that continually learns to responsibly engage](https://mlsanity.com/paper/2208.03188/) (2022)
- [GECToR -- Grammatical Error Correction: Tag, Not Rewrite](https://mlsanity.com/paper/2005.12592/) (2020)

## Built on by (most cited first, 30 of 33)

- [Instruction Tuning for Large Language Models: A Survey](https://mlsanity.com/paper/2308.10792/) (2023)
- [EvoPrompt: Connecting LLMs with Evolutionary Algorithms Yields Powerful Prompt Optimizers](https://mlsanity.com/paper/2309.08532/) (2023)
- [Alleviating Hallucinations of Large Language Models through Induced Hallucinations](https://mlsanity.com/paper/2312.15710/) (2023)
- [MoRAL: MoE Augmented LoRA for LLMs' Lifelong Learning](https://mlsanity.com/paper/2402.11260/) (2024)
- [On the (In)Effectiveness of Large Language Models for Chinese Text Correction](https://mlsanity.com/paper/2307.09007/) (2023)
- [Unveiling the Generalization Power of Fine-Tuned Large Language Models](https://mlsanity.com/paper/2403.09162/) (2024)
- [Explore-Instruct: Enhancing Domain-Specific Instruction Coverage through Active Exploration](https://mlsanity.com/paper/2310.09168/) (2023)
- [Query Performance Prediction using Relevance Judgments Generated by Large Language Models](https://mlsanity.com/paper/2404.01012/) (2024)
- [GPT-3.5 for Grammatical Error Correction](https://mlsanity.com/paper/2405.08469/) (2024)
- [Specialist or Generalist? Instruction Tuning for Specific NLP Tasks](https://mlsanity.com/paper/2310.15326/) (2023)
- [CyberPal.AI: Empowering LLMs with Expert-Driven Cybersecurity Instructions](https://mlsanity.com/paper/2408.09304/) (2024)
- [EdgeLoRA: An Efficient Multi-Tenant LLM Serving System on Edge Devices](https://mlsanity.com/paper/2507.01438/) (2025)
- [NaSGEC: a Multi-Domain Chinese Grammatical Error Correction Dataset from Native Speaker Texts](https://mlsanity.com/paper/2305.16023/) (2023)
- [Rethinking the Roles of Large Language Models in Chinese Grammatical Error Correction](https://mlsanity.com/paper/2402.11420/) (2024)
- [Pillars of Grammatical Error Correction: Comprehensive Inspection Of Contemporary Approaches In The Era of Large Language Models](https://mlsanity.com/paper/2404.14914/) (2024)
- [RobustGEC: Robust Grammatical Error Correction Against Subtle Context Perturbation](https://mlsanity.com/paper/2310.07299/) (2023)
- [DEFT: Data Efficient Fine-Tuning for Pre-Trained Language Models via Unsupervised Core-Set Selection](https://mlsanity.com/paper/2310.16776/) (2023)
- [Dynamics of Instruction Fine-Tuning for Chinese Large Language Models](https://mlsanity.com/paper/2310.19651/) (2023)
- [Instruction Matters: A Simple yet Effective Task Selection for Optimized Instruction Tuning of Specific Tasks](https://mlsanity.com/paper/2404.16418/) (2024)
- [Alirector: Alignment-Enhanced Chinese Grammatical Error Corrector](https://mlsanity.com/paper/2402.04601/) (2024)
- [Towards Alignment-Centric Paradigm: A Survey of Instruction Tuning in Large Language Models](https://mlsanity.com/paper/2508.17184/) (2025)
- [KwaiYiiMath: Technical Report](https://mlsanity.com/paper/2310.07488/) (2023)
- [From Linguistic Giants to Sensory Maestros: A Survey on Cross-Modal Reasoning with Large Language Models](https://mlsanity.com/paper/2409.18996/) (2024)
- [Fine-tuning Large Language Models with Limited Data: A Survey and Practical Guide](https://mlsanity.com/paper/2411.09539/) (2024)
- [To Err Is Human, but Llamas Can Learn It Too](https://mlsanity.com/paper/2403.05493/) (2024)
- [Pitfalls of Conversational LLMs on News Debiasing](https://mlsanity.com/paper/2404.06488/) (2024)
- [FOCUS: Forging Originality through Contrastive Use in Self-Plagiarism for Language Models](https://mlsanity.com/paper/2406.00839/) (2024)
- [Carbon and Silicon, Coexist or Compete? A Survey on Human-AI Interactions in Agent-based Modeling and Simulation](https://mlsanity.com/paper/2502.18145/) (2025)
- [A Toolbox for Improving Evolutionary Prompt Search](https://mlsanity.com/paper/2511.05120/) (2025)
- [Optimizing Cross-Client Domain Coverage for Federated Instruction Tuning of Large Language Models](https://mlsanity.com/paper/2409.20135/) (2024)

## Abstract (arXiv)

Proprietary Large Language Models (LLMs), such as ChatGPT, have garnered significant attention due to their exceptional capabilities in handling a diverse range of tasks. Recent studies demonstrate that open-sourced smaller foundational models, such as 7B-size LLaMA, can also display remarkable proficiency in tackling diverse tasks when fine-tuned using instruction-driven data. In this work, we investigate a practical problem setting where the primary focus is on one or a few particular tasks rather than general-purpose instruction following, and explore whether LLMs can be beneficial and further improved for such targeted scenarios. We choose the writing-assistant scenario as the testbed, which includes seven writing tasks. We collect training data for these tasks, reframe them in an instruction-following format, and subsequently refine the LLM, specifically LLaMA, via instruction tuning. Experimental results show that fine-tuning LLaMA on writing instruction data significantly improves its ability on writing tasks. We also conduct more experiments and analyses to offer insights for future work on effectively fine-tuning LLaMA for specific scenarios. Finally, we initiate a discussion regarding the necessity of employing LLMs for only one targeted task, taking into account the efforts required for tuning and the resources consumed during deployment.