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**Practical Benefits and Limitations.** The most significant benefit comes from the reduction in memory and storage usage. For a large Transformer trained with Adam, we reduce that VRAM usage by up to 2/3 if *r* < *d<sub>model</sub>* as we do not need to store the optimizer states for the frozen parameters. On GPT-3 17... |
LORA also has its limitations. For example, it is not straightforward to batch inputs to different tasks with different A and B in a single forward pass, if one chooses to absorb A and B into W to eliminate additional inference latency. Though it is possible to not merge the weights and dynamically choose the LORA modu... |
**5 EMPIRICAL EXPERIMENTS** |
We evaluate the downstream task performance of LoRA on RoBERTa (Liu et al., 2019), DeBERTa (He et al., 2021), and GPT-2 (Radford et al., b), before scaling up to GPT-3 175B (Brown et al., 2020). Our experiments cover a wide range of tasks, from natural language understanding (NLU) to generation (NLG). Specifically, we ... |
**5.1 BASELINES** |
To compare with other baselines broadly, we replicate the setups used by prior work and reuse their reported numbers whenever possible. This, however, means that some baselines might only appear in certain experiments. |
**Fine-Tuning (FT)** is a common approach for adaptation. During fine-tuning, the model is initialized to the pre-trained weights and biases, and all model parameters undergo gradient updates. A simple variant is to update only some layers while freezing others. We include one such baseline reported in prior work (Li &... |
**Bias-only or BitFit** is a baseline where we only train the bias vectors while freezing everything else. Contemporarily, this baseline has also been studied by BitFit (Zaken et al., 2021). |
**Prefix-embedding tuning (PreEmbed)** inserts special tokens among the input tokens. These special tokens have trainable word embeddings and are generally not in the model's vocabulary. Where to place such tokens can have an impact on performance. We focus on “prefixing”, which prepends such tokens to the prompt, and ... |
**Prefix-layer tuning (PreLayer)** is an extension to prefix-embedding tuning. Instead of just learning the word embeddings (or equivalently, the activations after the embedding layer) for some special tokens, we learn the activations after every Transformer layer. The activations computed from previous layers are simp... |
**Adapter tuning** as proposed in Houlsby et al. (2019) inserts adapter layers between the self-attention module (and the MLP module) and the subsequent residual connection. There are two fully connected layers with biases in an adapter layer with a nonlinearity in between. We call this original design Adapter<sup>H</s... |
**LoRA** adds trainable pairs of rank decomposition matrices in parallel to existing weight matrices. As mentioned in Section 4.2, we only apply LoRA to W<sub>q</sub> and W<sub>v</sub> in most experiments for simplicity. The number of trainable parameters is determined by the rank *r* and the shape of the original weig... |
**5.2 ROBERTA BASE/LARGE** |
RoBERTa (Liu et al., 2019) optimized the pre-training recipe originally proposed in BERT (Devlin et al., 2019a) and boosted the latter's task performance without introducing many more trainable parameters. While ROBERTa has been overtaken by much larger models on NLP leaderboards such as the GLUE benchmark (Wang et al.... |
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<img src="lora_table2.png" width="600"/> |
<p>Table 2: ROBERTabase, ROBERTalarge, and DeBERTaxxL with different adaptation methods on the GLUE benchmark. We report the overall (matched and mismatched) accuracy for MNLI, Matthew's correlation for CoLA, Pearson correlation for STS-B, and accuracy for other tasks. Higher is better for all metrics. * indicates nu... |
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**5.3 DEBERTA XXL** |
DeBERTa (He et al., 2021) is a more recent variant of BERT that is trained on a much larger scale and performs very competitively on benchmarks such as GLUE (Wang et al., 2019) and SuperGLUE (Wang et al., 2020). We evaluate if LoRA can still match the performance of a fully fine-tuned DeBERTa<sub>XXL</sub> (1.5B) on GL... |
**5.4 GPT-2 MEDIUM/LARGE** |
Having shown that LoRA can be a competitive alternative to full fine-tuning on NLU, we hope to answer if LORA still prevails on NLG models, such as GPT-2<sub>medium</sub> and <sub>large</sub> (Radford et al., b). We keep our setup as close as possible to Li & Liang (2021) for a direct comparison. Due to space constrain... |
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<img src="lora_table3.png" width="600"/> |
<p>Table 3: GPT-2 medium (M) and large (L) with different adaptation methods on the E2E NLG Challenge. For all metrics, higher is better. LoRA outperforms several baselines with comparable or fewer trainable parameters. Confidence intervals are shown for experiments we ran. * indicates numbers published in prior work... |
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**5.5 SCALING UP TO GPT-3 175B** |
As a final stress test for LoRA, we scale up to GPT-3 with 175 billion parameters. Due to the high training cost, we only report the typical standard deviation for a given task over random seeds, as opposed to providing one for every entry. See Section D.4 for details on the hyperparameters used. |
As shown in Table 4, LoRA matches or exceeds the fine-tuning baseline on all three datasets. Note that not all methods benefit monotonically from having more trainable parameters, as shown in Figure 2. We observe a significant performance drop when we use more than 256 special tokens for prefix-embedding tuning or more... |
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<img src="lora_table4.png" width="500"/> |
<p>Table 4: Performance of different adaptation methods on GPT-3 175B. We report the logical form validation accuracy on WikiSQL, validation accuracy on MultiNLI-matched, and Rouge-1/2/L on SAMSum. LoRA performs better than prior approaches, including full fine-tuning. The results on WikiSQL have a fluctuation around... |
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<img src="lora_figure2.png" width="600"/> |
<p>Figure 2: GPT-3 175B validation accuracy vs. number of trainable parameters of several adaptation methods on WikiSQL and MNLI-matched. LoRA exhibits better scalability and task performance. See Section F.2 for more details on the plotted data points.</p> |
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**6 RELATED WORKS** |
**Transformer Language Models.** Transformer (Vaswani et al., 2017) is a sequence-to-sequence architecture that makes heavy use of self-attention. Radford et al. (a) applied it to autoregressive language modeling by using a stack of Transformer decoders. Since then, Transformer-based language models have dominated NLP,... |
**Prompt Engineering and Fine-Tuning.** While GPT-3 175B can adapt its behavior with just a few additional training examples, the result depends heavily on the input prompt (Brown et al., 2020). This necessitates an empirical art of composing and formatting the prompt to maximize a model's performance on a desired task... |
**Parameter-Efficient Adaptation.** Many have proposed inserting adapter layers between existing layers in a neural network (Houlsby et al., 2019; Rebuffi et al., 2017; Lin et al., 2020). Our method uses a similar bottleneck structure to impose a low-rank constraint on the weight updates. The key functional difference ... |
**Low-Rank Structures in Deep Learning.** Low-rank structure is very common in machine learning. A lot of machine learning problems have certain intrinsic low-rank structure (Li et al., 2016; Cai et al., 2010; Li et al., 2018b; Grasedyck et al., 2013). Moreover, it is known that for many deep learning tasks, especially... |
**7 UNDERSTANDING THE LOW-RANK UPDATES** |
Given the empirical advantage of LoRA, we hope to further explain the properties of the low-rank adaptation learned from downstream tasks. Note that the low-rank structure not only lowers the hardware barrier to entry which allows us to run multiple experiments in parallel, but also gives better interpretability of how... |
We perform a sequence of empirical studies to answer the following questions: 1) Given a parameter budget constraint, which subset of weight matrices in a pre-trained Transformer should we adapt to maximize downstream performance? 2) Is the "optimal" adaptation matrix ΔW really rank-deficient? If so, what is a good ran... |
We believe that our answers to question (2) and (3) shed light on the fundamental principles of using pre-trained language models for downstream tasks, which is a critical topic in NLP. |
**7.1 WHICH WEIGHT MATRICES IN TRANSFORMER SHOULD WE APPLY LORA TO?** |
Given a limited parameter budget, which types of weights should we adapt with LoRA to obtain the best performance on downstream tasks? As mentioned in Section 4.2, we only consider weight matrices in the self-attention module. We set a parameter budget of 18M (roughly 35MB if stored in FP16) on GPT-3 175B, which corres... |
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