# Unveiling the Generalization Power of Fine-Tuned Large Language Models

arXiv: https://arxiv.org/abs/2403.09162 · HTML: https://mlsanity.com/paper/2403.09162/

## Key facts

- Cited by 36 papers in the map (data through 14 Oct 2026).
- Builds on 32 of its 45 references that are in the map.
- First posted on arXiv 14 Mar 2024.

## Builds on (most cited first, 30 of 32)

- [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)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [PaLM: Scaling Language Modeling with Pathways](https://mlsanity.com/paper/2204.02311/) (2022)
- [GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding](https://mlsanity.com/paper/1804.07461/) (2018)
- [Finetuned Language Models Are Zero-Shot Learners](https://mlsanity.com/paper/2109.01652/) (2021)
- [A Broad-Coverage Challenge Corpus for Sentence Understanding through Inference](https://mlsanity.com/paper/1704.05426/) (2017)
- [Learning to summarize from human feedback](https://mlsanity.com/paper/2009.01325/) (2020)
- [Don't Give Me the Details, Just the Summary! Topic-Aware Convolutional Neural Networks for Extreme Summarization](https://mlsanity.com/paper/1808.08745/) (2018)
- [LIMA: Less Is More for Alignment](https://mlsanity.com/paper/2305.11206/) (2023)
- [Teaching Machines to Read and Comprehend](https://mlsanity.com/paper/1506.03340/) (2015)
- [Crowdsourcing Multiple Choice Science Questions](https://mlsanity.com/paper/1707.06209/) (2017)
- [It's Not Just Size That Matters: Small Language Models Are Also Few-Shot Learners](https://mlsanity.com/paper/2009.07118/) (2020)
- [Learning To Retrieve Prompts for In-Context Learning](https://mlsanity.com/paper/2112.08633/) (2021)
- [PAWS: Paraphrase Adversaries from Word Scrambling](https://mlsanity.com/paper/1904.01130/) (2019)
- [Understanding the Effects of RLHF on LLM Generalisation and Diversity](https://mlsanity.com/paper/2310.06452/) (2023)
- [XL-Sum: Large-Scale Multilingual Abstractive Summarization for 44 Languages](https://mlsanity.com/paper/2106.13822/) (2021)
- [The False Promise of Imitating Proprietary LLMs](https://mlsanity.com/paper/2305.15717/) (2023)
- [Recall and Learn: Fine-tuning Deep Pretrained Language Models with Less Forgetting](https://mlsanity.com/paper/2004.12651/) (2020)
- [InstructUIE: Multi-task Instruction Tuning for Unified Information Extraction](https://mlsanity.com/paper/2304.08085/) (2023)
- [The Multilingual Amazon Reviews Corpus](https://mlsanity.com/paper/2010.02573/) (2020)
- [A Dataset of Peer Reviews (PeerRead): Collection, Insights and NLP Applications](https://mlsanity.com/paper/1804.09635/) (2018)
- [Few-shot Fine-tuning vs. In-context Learning: A Fair Comparison and Evaluation](https://mlsanity.com/paper/2305.16938/) (2023)
- [From Amateurs to Connoisseurs: Modeling the Evolution of User Expertise through Online Reviews](https://mlsanity.com/paper/1303.4402/) (2013)
- [TWEETQA: A Social Media Focused Question Answering Dataset](https://mlsanity.com/paper/1907.06292/) (2019)
- [Two-stage LLM Fine-tuning with Less Specialization and More Generalization](https://mlsanity.com/paper/2211.00635/) (2022)
- [Understanding In-Context Learning via Supportive Pretraining Data](https://mlsanity.com/paper/2306.15091/) (2023)
- [Multi-Task Instruction Tuning of LLaMa for Specific Scenarios: A Preliminary Study on Writing Assistance](https://mlsanity.com/paper/2305.13225/) (2023)
- [COSPLAY: Concept Set Guided Personalized Dialogue Generation Across Both Party Personas](https://mlsanity.com/paper/2205.00872/) (2022)
- [Specialist or Generalist? Instruction Tuning for Specific NLP Tasks](https://mlsanity.com/paper/2310.15326/) (2023)

## Built on by (most cited first, 30 of 36)

- [Next-Generation Database Interfaces: A Survey of LLM-based Text-to-SQL](https://mlsanity.com/paper/2406.08426/) (2024)
- [Beyond Examples: High-level Automated Reasoning Paradigm in In-Context Learning via MCTS](https://mlsanity.com/paper/2411.18478/) (2024)
- [Towards Understanding Multi-Task Learning (Generalization) of LLMs via Detecting and Exploring Task-Specific Neurons](https://mlsanity.com/paper/2407.06488/) (2024)
- [Raising the Bar: Investigating the Values of Large Language Models via Generative Evolving Testing](https://mlsanity.com/paper/2406.14230/) (2024)
- [United Minds or Isolated Agents? Exploring Coordination of LLMs under Cognitive Load Theory](https://mlsanity.com/paper/2506.06843/) (2025)
- [Extracting Heuristics from Large Language Models for Reward Shaping in Reinforcement Learning](https://mlsanity.com/paper/2405.15194/) (2024)
- [An Empirical Study on Prompt Compression for Large Language Models](https://mlsanity.com/paper/2505.00019/) (2025)
- [Reasoning BO: Enhancing Bayesian Optimization with Long-Context Reasoning Power of LLMs](https://mlsanity.com/paper/2505.12833/) (2025)
- [RAGRouter: Learning to Route Queries to Multiple Retrieval-Augmented Language Models](https://mlsanity.com/paper/2505.23052/) (2025)
- [Reviving Your MNEME: Predicting The Side Effects of LLM Unlearning and Fine-Tuning via Sparse Model Diffing](https://mlsanity.com/paper/2507.21084/) (2025)
- [Large Model Strategic Thinking, Small Model Efficiency: Transferring Theory of Mind in Large Language Models](https://mlsanity.com/paper/2408.05241/) (2024)
- [Learning 4D Panoptic Scene Graph Generation from Rich 2D Visual Scene](https://mlsanity.com/paper/2503.15019/) (2025)
- [MeMo: Towards Language Models with Associative Memory Mechanisms](https://mlsanity.com/paper/2502.12851/) (2025)
- [Dictionary Insertion Prompting for Multilingual Reasoning on Multilingual Large Language Models](https://mlsanity.com/paper/2411.01141/) (2024)
- [HalluRNN: Mitigating Hallucinations via Recurrent Cross-Layer Reasoning in Large Vision-Language Models](https://mlsanity.com/paper/2506.17587/) (2025)
- [Parameter-Efficient Fine-Tuning for Continual Learning: A Neural Tangent Kernel Perspective](https://mlsanity.com/paper/2407.17120/) (2024)
- [Leveraging the Power of LLMs: A Fine-Tuning Approach for High-Quality Aspect-Based Summarization](https://mlsanity.com/paper/2408.02584/) (2024)
- [Boosting LLM-based Relevance Modeling with Distribution-Aware Robust Learning](https://mlsanity.com/paper/2412.12504/) (2024)
- [Neuroplasticity and Corruption in Model Mechanisms: A Case Study Of Indirect Object Identification](https://mlsanity.com/paper/2503.01896/) (2025)
- [Memorization and Knowledge Injection in Gated LLMs](https://mlsanity.com/paper/2504.21239/) (2025)
- [Menta: A Small Language Model for On-Device Mental Health Prediction](https://mlsanity.com/paper/2512.02716/) (2025)
- [Exploring Language Model Generalization in Low-Resource Extractive QA](https://mlsanity.com/paper/2409.18446/) (2024)
- [Data Doping or True Intelligence? Evaluating the Transferability of Injected Knowledge in LLMs](https://mlsanity.com/paper/2505.17140/) (2025)
- [Impact of Model Size on Fine-tuned LLM Performance in Data-to-Text Generation: A State-of-the-Art Investigation](https://mlsanity.com/paper/2407.14088/) (2024)
- [Toxic Subword Pruning for Dialogue Response Generation on Large Language Models](https://mlsanity.com/paper/2410.04155/) (2024)
- [Detecting Bias and Enhancing Diagnostic Accuracy in Large Language Models for Healthcare](https://mlsanity.com/paper/2410.06566/) (2024)
- [Anchored Alignment for Self-Explanations Enhancement](https://mlsanity.com/paper/2410.13216/) (2024)
- [Tuning without Peeking: Provable Generalization Bounds and Robust LLM Post-Training](https://mlsanity.com/paper/2507.01752/) (2025)
- [Impacts of Continued Legal Pre-Training and IFT on LLMs' Latent Representations of Human-Defined Legal Concepts](https://mlsanity.com/paper/2410.12001/) (2024)
- [Evolution of Thought: Diverse and High-Quality Reasoning via Multi-Objective Optimization](https://mlsanity.com/paper/2412.07779/) (2024)

## Abstract (arXiv)

While Large Language Models (LLMs) have demonstrated exceptional multitasking abilities, fine-tuning these models on downstream, domain-specific datasets is often necessary to yield superior performance on test sets compared to their counterparts without fine-tuning. However, the comprehensive effects of fine-tuning on the LLMs' generalization ability are not fully understood. This paper delves into the differences between original, unmodified LLMs and their fine-tuned variants. Our primary investigation centers on whether fine-tuning affects the generalization ability intrinsic to LLMs. To elaborate on this, we conduct extensive experiments across five distinct language tasks on various datasets. Our main findings reveal that models fine-tuned on generation and classification tasks exhibit dissimilar behaviors in generalizing to different domains and tasks. Intriguingly, we observe that integrating the in-context learning strategy during fine-tuning on generation tasks can enhance the model's generalization ability. Through this systematic investigation, we aim to contribute valuable insights into the evolving landscape of fine-tuning practices for LLMs.