FIAT: Fusing learning paradigms with Instruction-Accelerated Tuning
Xinyi Wang, John Wieting, Jonathan H. Clark
TL;DR
FIAT introduces a new learning paradigm that combines in-context learning and fine-tuning, enabling flexible and efficient use of large language models across different scales and tasks.
Contribution
The paper proposes FIAT, a novel approach that fuses in-context learning and fine-tuning, improving performance and versatility of LLMs across various data regimes.
Findings
FIAT outperforms ICL and fine-tuning on multilingual tasks.
FIAT is effective with 100-10,000 training examples.
FIAT enables prompt engineering and parameter-efficient tuning.
Abstract
Learning paradigms for large language models (LLMs) currently tend to fall within either in-context learning (ICL) or full fine-tuning. Each of these comes with their own trade-offs based on available data, model size, compute cost, ease-of-use, and final quality with neither solution performing well across-the-board. In this article, we first describe ICL and fine-tuning paradigms in a way that highlights their natural connections. Based on these connections, we propose a new learning paradigm called FIAT that fuses the best of these paradigms together, enabling prompt-engineered instructions and chain-of-thought reasoning with the very largest models while also using similar methods to perform parameter updates on a modestly-sized LLM with parameter-efficient tuning. We evaluate FIAT's effectiveness on a variety of multilingual tasks and observe that FIAT performs better than both ICL…
Peer Reviews
Decision·ICLR 2024 Conference Withdrawn Submission
The paper studies an important problem that how to better combine different learning paradigms for better performance with less resources.
1. Although the studied problem is interesting and important, the authors should dive deeper into how to better combine them instead of simply adding them altogether. For example, how to better choose $I_\beta$ to achieve better training of $\tau$? How to write the $I_\tau$? There are many unexplored problems in this paper. 2. Presentation of this paper is unclear, especially in Figure 2. What does the $y$ refer to in $\hat{y}_{\beta} = \text{argmax}_y P(y| x;\theta, I)$? $y$ is defined as a ta
The proposed approach is designed to reduce the computational cost of adapting LLMs on downstream tasks.
1. **(Novelty)**. The proposed method provides little novel contribution, the basic idea is to use an expert LLM to generate data to improve downstream models has already been explored in very similar scenarios. For example, [1] an expert LLM to generate possible continuations that are then filtered (to improve quality) and used for subsequent fine-tuning to get the final downstream model. Retrieval Augmented Generation methods (e.g. [2]) follow the same idea but, rather than generating new samp
1. This paper reviews the strengths and weaknesses of two popular paradigms for adapting LLM to specific downstream tasks, i.e., ICL and PEFT. The proposed FIAT combines the advantages of ICL and PEFT. On the one hand, it can leverage the knowledge from the most capable LLMs, and on the other hand, it utilizes PEFT from the modestly-sized LLM with training data from the downstream task. The experiments demonstrate that FIAT indeed benefits from these two aspects and achieves performance improvem
The authors claim that the proposed FIAT provides a way of harnessing the full potential of LLMs without making a choice between In-Context Learning and Fine-Tuning. Personally, this might be a bit of an overclaim for the contribution of this work. Although this work combines the in-context inference of large models and the parameter-efficient fine-tuning of medium models, it primarily holds true for limited computation costs. Under limited compute cost, for large models like the PaLM L model, w
- The paper clearly explains the strengths and weaknesses of both ICL and parameter tuning. It makes it to grasp the idea and motivation more easily. - It is easy to read. - The ablation study for techniques included in the paradigm was conducted.
- While some are empirically proven in the Ablation study section, when describing the need for various techniques included in the algorithm, it seems somewhat heuristic, giving a feeling of subjective choice. For example, I don't quite understand the part that describes the reason for including instructions in the tunable model in Section 2. - The explanation for why fine-tuning performs exceptionally well despite the very small training dataset size of XOR-ATTRIQA is insufficient. Given the a
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Taxonomy
TopicsTopic Modeling · Natural Language Processing Techniques · Speech and dialogue systems