Unveiling the Generalization Power of Fine-Tuned Large Language Models

• URL: http://arxiv.org/abs/2403.09162v1
• Date: Thu, 14 Mar 2024 08:18:59 GMT
• Title: Unveiling the Generalization Power of Fine-Tuned Large Language Models
• Authors: Haoran Yang, Yumeng Zhang, Jiaqi Xu, Hongyuan Lu, Pheng Ann Heng, Wai Lam,
• Abstract summary: We investigate whether fine-tuning affects the intrinsic generalization ability intrinsic to Large Language Models (LLMs) Our main findings reveal that models fine-tuned on generation and classification tasks exhibit dissimilar behaviors in generalizing to different domains and tasks. We observe that integrating the in-context learning strategy during fine-tuning on generation tasks can enhance the model's generalization ability.
• Score: 81.70754292058258
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• The Inherent Limits of Pretrained LLMs: The Unexpected Convergence of Instruction Tuning and In-Context Learning Capabilities [51.594836904623534]
  We investigate whether instruction-tuned models possess fundamentally different capabilities from base models that are prompted using in-context examples. We show that the performance of instruction-tuned models is significantly correlated with the in-context performance of their base counterparts. Specifically, we extend this understanding to instruction-tuned models, suggesting that their pretraining data similarly sets a limiting boundary on the tasks they can solve.
  arXiv  Detail & Related papers  (2025-01-15T10:57:55Z)
• Learn from Downstream and Be Yourself in Multimodal Large Language Model Fine-Tuning [104.27224674122313]
  Fine-tuning MLLM has become a common practice to improve performance on specific downstream tasks. To balance the trade-off between generalization and specialization, we propose measuring the parameter importance for both pre-trained and fine-tuning distributions.
  arXiv  Detail & Related papers  (2024-11-17T01:16:37Z)
• Aggregation Artifacts in Subjective Tasks Collapse Large Language Models' Posteriors [74.04775677110179]
  In-context Learning (ICL) has become the primary method for performing natural language tasks with Large Language Models (LLMs) In this work, we examine whether this is the result of the aggregation used in corresponding datasets, where trying to combine low-agreement, disparate annotations might lead to annotation artifacts that create detrimental noise in the prompt. Our results indicate that aggregation is a confounding factor in the modeling of subjective tasks, and advocate focusing on modeling individuals instead.
  arXiv  Detail & Related papers  (2024-10-17T17:16:00Z)
• Mixing It Up: The Cocktail Effect of Multi-Task Fine-Tuning on LLM Performance -- A Case Study in Finance [0.32985979395737774]
  We present a detailed analysis of fine-tuning large language models (LLMs) for domain-specific tasks. We find that in domain-specific cases, fine-tuning exclusively on the target task is not always the most effective strategy. We demonstrate how this approach enables a small model, such as Phi-3-Mini, to achieve state-of-the-art results.
  arXiv  Detail & Related papers  (2024-10-01T22:35:56Z)
• Fine-tuning Large Language Models for Entity Matching [3.7277730514654555]
  Generative large language models (LLMs) are a promising alternative to pre-trained language models for entity matching. This paper explores the potential of fine-tuning LLMs for entity matching.
  arXiv  Detail & Related papers  (2024-09-12T16:20:57Z)
• Benchmarking General-Purpose In-Context Learning [19.40952728849431]
  In-context learning (ICL) empowers generative models to address new tasks effectively and efficiently on the fly. In this paper, we study extending ICL to address a broader range of tasks with an extended learning horizon and higher improvement potential. We introduce two benchmarks specifically crafted to train and evaluate GPICL functionalities.
  arXiv  Detail & Related papers  (2024-05-27T14:50:42Z)
• Supervised Knowledge Makes Large Language Models Better In-context Learners [94.89301696512776]
  Large Language Models (LLMs) exhibit emerging in-context learning abilities through prompt engineering. The challenge of improving the generalizability and factuality of LLMs in natural language understanding and question answering remains under-explored. We propose a framework that enhances the reliability of LLMs as it: 1) generalizes out-of-distribution data, 2) elucidates how LLMs benefit from discriminative models, and 3) minimizes hallucinations in generative tasks.
  arXiv  Detail & Related papers  (2023-12-26T07:24:46Z)
• Specialist or Generalist? Instruction Tuning for Specific NLP Tasks [58.422495509760154]
  We investigate whether incorporating broad-coverage generalist instruction tuning can contribute to building a specialist model. Our experiments assess four target tasks with distinct coverage levels. The effect is particularly pronounced when the amount of task-specific training data is limited.
  arXiv  Detail & Related papers  (2023-10-23T19:46:48Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.