What Happens To BERT Embeddings During Fine-tuning?

04/29/2020 ∙ by Amil Merchant, et al. ∙ Google 0

While there has been much recent work studying how linguistic information is encoded in pre-trained sentence representations, comparatively little is understood about how these models change when adapted to solve downstream tasks. Using a suite of analysis techniques (probing classifiers, Representational Similarity Analysis, and model ablations), we investigate how fine-tuning affects the representations of the BERT model. We find that while fine-tuning necessarily makes significant changes, it does not lead to catastrophic forgetting of linguistic phenomena. We instead find that fine-tuning primarily affects the top layers of BERT, but with noteworthy variation across tasks. In particular, dependency parsing reconfigures most of the model, whereas SQuAD and MNLI appear to involve much shallower processing. Finally, we also find that fine-tuning has a weaker effect on representations of out-of-domain sentences, suggesting room for improvement in model generalization.



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1 Introduction

The introduction of unsupervised pre-training for Transformer architectures Vaswani et al. (2017) has led to significant advances in performance on a range of NLP tasks. Most notably, the popular BERT model Devlin et al. (2019) topped the GLUE Wang et al. (2019) leaderboard when it was released, and similar models have continued to improve scores over the past year Lan et al. (2019); Raffel et al. (2019).

Much recent work has attempted to better understand these models and explain what makes them so powerful. Particularly, behavioral studies (Goldberg, 2019, inter alia), diagnostic probing classifiers (Liu et al., 2019, inter alia), and unsupervised techniques (Voita et al., 2019a, inter alia) have shed light on the representations from the pre-trained models and have shown that they encode a wide variety of linguistic phenomena Tenney et al. (2019b).

However, in the standard recipe for models such as BERT Devlin et al. (2019)

, after a Transformer is initialized with pre-trained weights, it is then trained for a few epochs on a supervised dataset. Considerably less is understood about what happens during this fine-tuning stage. Current understanding is mostly derived from observations about model behavior: fine-tuned Transformers achieve state-of-the-art performance but also can end up learning shallow shortcuts, heuristics, and biases

McCoy et al. (2019b, a); Gururangan et al. (2018); Poliak et al. (2018). Thus, in this work, we seek to understand how the internals of the model–the representation space–change when fine-tuned for downstream tasks. We focus on three widely-used NLP tasks: dependency parsing, natural language inference (MNLI), and reading comprehension (SQuAD), and ask:

  • What happens to the encoding of linguistic features such as syntactic and semantic roles? Are these preserved, reinforced, or forgotten as the encoder learns a new task? (Section 4)

  • Where in the model are changes made? Are parameter updates concentrated in a small number of layers or are there changes throughout? (Section 5)

  • Do these changes generalize or does the newfound behavior only apply to the specific task domain? (Section 6)

We approach these questions with three distinct analysis techniques. Supervised probing classifiers (Tenney et al., 2019b; Hewitt and Manning, 2019) give a positive test for specific linguistic phenomena, while Representational Similarity Analysis (RSA; Kriegeskorte et al., 2008) gives a task-agnostic measurement of the change in model activations. Finally, we corroborate the probing and RSA results with two types of model ablations–truncation and partial freezing–and measure their effect on end-task performance.

Taken together, we draw the following conclusions. First, linguistic features are not lost during fine-tuning. Second, fine-tuning tends to affect only the top few layers of BERT, albeit with significant variation across tasks: SQuAD and MNLI have a relatively shallow effect, while dependency parsing involves deeper changes to the encoder. We confirm this by partial-freezing experiments which test how many layers need

to change to do well on each task and relate this to an estimate of task

difficulty with layer ablations. Finally, we observe that fine-tuning induces large changes on in-domain examples, but the representations of out-of-domain sentences resemble those of the pre-trained encoder.

2 Related Work

Base model

Many recent papers have focused on understanding sentence encoders such as ELMo Peters et al. (2018a) and BERT Devlin et al. (2019), focusing primarily on the “innate” abilities of the pre-trained (“Base”) models. For language models that report perplexity scores, behavioral analyses Goldberg (2019); Marvin and Linzen (2018); Gulordava et al. (2018); Ettinger (2020) have shown that they capture phenomena like number agreement and anaphora. For Transformer-based models Vaswani et al. (2017), analyses of attention weights have shown interpretable patterns in their structure Coenen et al. (2019); Vig and Belinkov (2019); Voita et al. (2019b); Hoover et al. (2019) and found strong correlations to syntax Clark et al. (2019). However, other studies have also cast doubt on what conclusions can be drawn from attention patterns Jain and Wallace (2019); Serrano and Smith (2019); Brunner et al. (2019).

More generally, supervised probing models (also known as diagnostic classifiers) make few assumptions beyond the existence of model activations and can test for the presence of a wide variety of phenomena. Adi et al. (2016); Blevins et al. (2018) tested recurrent networks for sentence length, word context, and syntactic properties, while Conneau et al. (2018); Jawahar et al. (2019) use another 10 probing tasks, including sensitivity to bigram shift. Tenney et al. (2019b); Liu et al. (2019); Peters et al. (2018b) introduced task suites that probe for high-level linguistic phenomena such as part-of-speech, entity types, and coreference, while Tenney et al. (2019a) showed that these phenomena are represented in a hierarchical order in the different layers of BERT. Hewitt and Manning (2019) also used a geometrically-motivated probing model to explore syntactic structures, and Wallace et al. (2019) explored the ability of ELMo and BERT to faithfully encode numerical information.111See Belinkov and Glass (2019) and Rogers et al. (2020) for a survey of probing methods.

While probing models depend on labelled data, parallel work has studied the same encoders using unsupervised techniques. Voita et al. (2019a) used a form of canonical correlation analysis (PW-CCA; Morcos et al., 2018) to study the layer-wise evolution of representations, while Saphra and Lopez (2019) explored how these representations evolve during training. Abnar et al. (2019) used Representational Similarity Analysis (RSA; Laakso and Cottrell, 2000; Kriegeskorte et al., 2008) to study the effect of context on encoder representations, while Chrupała and Alishahi (2019) correlated them with syntax. Abdou et al. (2019); Gauthier and Levy (2019) also compared these representations to fMRI and eye-tracking data.


In contrast to the pre-trained models, there have been comparatively few studies on understanding the fine-tuning process. Initial studies of fine-tuned encoders have shown state-of-the-art performance on benchmark suites such as GLUE Wang et al. (2019) and surprising sample efficiency. However, behavioral studies with challenge sets McCoy et al. (2019b); Poliak et al. (2018); Ettinger et al. (2018); Kim et al. (2018) have shown limited ability to generalize to out-of-domain data and across syntactic perturbations.

Looking more directly at representations, van Aken et al. (2019)

focused on question-answering models with task-specific probing models and clustering analysis. They found evidence of different stages of processing in a fine-tuned BERT model but showed only limited comparisons with the pre-trained encoder.

Hao et al. (2019) explored fine-tuning from an optimization perspective, finding that pre-training leads to more efficient and stable optimization than random initialization. Peters et al. (2019) also analyzed the effects of fine-tuning with respect to the performance of diagnostic classifiers at various layers. Gauthier and Levy (2019) is closest to our work: while focused on correlating representation to fMRI data, they also studied fine-tuning using RSA and the structural probe of Hewitt and Manning (2019), finding a significant divergence between the final representations of models fine-tuned on different tasks. By comparison, we seek a more general analysis of the internal representations of fine-tuned BERT models and also focus on how they change compared to the pre-trained Base model.

3 Experimental Setup


In this paper, we focus on the effects of fine-tuning for the popular BERT architecture Devlin et al. (2019). During pre-training, the model is initialized by training on masked language-modeling and next sentence prediction, with an unsupervised corpus from BooksCorpus Zhu et al. (2015) and English Wikipedia. This model uses WordPiece tokenization Wu et al. (2016) and prepends input sentences with a [CLS]

token, used for classification. We use the original TensorFlow

(Abadi et al., 2015) implementation of BERT222https://github.com/google-research/bert and focus on the 12-layer bert_base_uncased variant. We denote the pre-trained model as Base and refer to fine-tuned versions by the name of the task.


A common benchmark for natural language understanding, the MNLI dataset Williams et al. (2018) contains over 433K sentence pairs annotated with textual entailment information. BERT is adapted to this task by feeding the [CLS] token in the last layer through an additional output layer for predictions. We use the parameters and architecture of Devlin et al. (2019) for fine-tuning. Across three trials, the evaluation accuracy of our BERT Base model is , slightly lower but comparable to the published score of .


The SQuADv1.1 dataset Rajpurkar et al. (2016) contains over 100,000 crowd-sourced question-answer pairs, created from a set of Wikipedia articles. The answers are given by contiguous spans from the original question, so the task is integrated into the BERT framework with an additional output layer for predicting the start and end tokens of the answer. We use the parameters and architecture of Devlin et al. (2019) for fine-tuning. Our average F1 score is , slightly higher than the published .

Dependency Parsing

We also introduce a BERT model fine-tuned on dependency parsing (Dep). We include this task to present a contrasting perspective from the prior two datasets, in particular since prior research has suggested that much of the information needed to solve dependency parsing is already present in the pre-trained base Hewitt and Manning (2019); Goldberg (2019). Our model is trained on data from the CoNLL 2017 Shared Task Zeman et al. (2017) and uses the features of BERT as input to a biaffine classifier, similar to Dozat and Manning (2017). The model uses a learning rate of with a warm-up portion, uses an Adam optimizer Kingma and Ba (2014), and is trained for 20 epochs. The Labeled Attachment Score (LAS) on the development set for our models is .

4 What happens to linguistic features?

Equipped with the models trained on these downstream tasks, we ask how the representation of linguistic features in the fine-tuned models compare to those in the pre-trained model? Recent studies have shown that these robust features do not inform predictions on downstream tasks, with models appearing to use dataset heuristics such as lexical overlap McCoy et al. (2019b) or word priors (Poliak et al., 2018), but it is an open question whether this is because these features are forgotten entirely or simply are not always used. We approach this with supervised probing models, using two complementary techniques: edge probes (Tenney et al., 2019b) which test for labeling information across several formalisms, and structural probes (Hewitt and Manning, 2019) which measure the representation of syntactic structure.

for Baselines for Fine-tuned Models
Task BERT Base Lexical Randomized MNLI SQuAD Dep
POS 97.53 -8.99 -13.61 -0.17 -1.52 -0.22
Constituents 84.35 -24.13 -12.88 -2.18 0.08 4.35
Dependencies 95.45 -15.57 -18.19 -0.51 -2.49 0.18
Entities 96.19 -6.56 -9.97 -0.34 -0.96 -0.62
SRL 92.87 -13.57 -15.04 -0.36 -2.90 -0.50
Coreference 95.72 -5.82 -6.15 -0.49 -0.84 -1.22
SPR 84.61 -6.56 -12.21 -0.67 -0.40 -1.17
Relations 79.50 -20.68 -40.53 -0.75 -0.37 -2.53
Table 1: Comparison of F1 performance on the edge probing tasks before and after fine-tuning. The BERT Base performance is consistent with Tenney et al. (2019b), and the results show that the fine-tuned models retain most of the linguistic concepts discovered during unsupervised pre-training. We report single numbers for clarity, but note that variation across runs is between probing runs, between fine-tuning runs from the same checkpoint, and point between different pretraining runs.


Let be the hidden size of the attention layers of a Transformer. Then, we define

to be the hidden representation of the

-th word at the -th attention layer. Note that BERT uses subword tokenization, so word representations are aggregated using mean pooling over all subword components.

Edge Probing

The edge probing tasks of Tenney et al. (2019b) aim to measure how well a contextual encoder captures linguistic phenomena, ranging from syntactic concepts such as part-of-speech to more semantic abstractions including entity typing and relation classification. To perform this task, the trained encoder is frozen, and the relevant hidden states

are fed into an auxiliary shallow neural network to predict labeling information. We use the tasks, architecture, and procedure of

Tenney et al. (2019b).333The dependency labeling task is from the English Web Treebank Silveira et al. (2014), SPR corresponds to SPR1 from Teichert et al. (2017), and relations is Task 8 from SemEval 2010 Hendrickx et al. (2010). All of the other tasks are from OntoNotes 5.0 Weischedel et al. (2013). After training, we report the micro-averaged F1 scores on a held-out test set.

Structural Probe

The structural probes of Hewitt and Manning (2019)

also analyze the token representations but are designed to evaluate how well this space encodes syntactic structure. Specifically, the probe identifies whether the squared L2 distance of the representations under some linear transformation encodes the tree distances between words in the dependency parse. The initial paper’s results showed that the deep models (ELMo and BERT) discover this syntax information during pre-training, which is not present in simple word embedding baselines.

The first structural probe predicts the tree depth (tree distance from the root node) by:

where is a learned matrix.444For our experiments, we use a rank to match the projection dimension from the edge probes. The corresponding metrics are the prediction accuracy of the root node and the Spearman correlation between the predicted and true tree depths.

The second structural probe measures the pairwise distances for all words in the parse tree. For any two words (, ), the distance is given by:

Following Hewitt and Manning (2019), we evaluate Spearman correlation between each row of the predicted and true distance matricies, and also compute the minimum spanning tree and compare to the true parse using the Undirected Unlabeled Attachment Score (UUAS).

Figure 1: Comparison of the structural probe performance on BERT models before and after fine-tuning. The stability of the Spearman correlations between both the depths and distance probes suggest that the embeddings still retain significant information about the syntax of inputted sentences.

4.1 Results

The results from both probing tasks largely demonstrate that the linguistic structures from pre-training are preserved in the fine-tuned models. This is first seen in the edge probing metrics presented in Table 1. For the sake of comparison, we also provide baseline results on the output of the embedding layer (Lexical) and a randomly initialized BERT architecture (Randomized). These baselines are important because inspection-based analysis can often discover patterns that are not obviously present due to the high capacity of auxiliary classifiers. For example, Zhang and Bowman (2018); Hewitt and Liang (2019) found that expressive-enough probing methods can have decent performance even when trained on random noise.

Across the edge probing suite, for all three tasks we see only small changes in F1 score compared to BERT base. In most cases we observe a drop in performance of 0.5-2%, with some variation: MNLI and SQuAD lead to drops of 1.5-3% on syntactic tasks–constituents, and POS, dependencies, and SRL, respectively–while the dependency parsing model leads to significantly improved syntactic performance (+4% on constituent labeling) while dropping performance on the more semantically-oriented coreference, SPR, and relation classification tasks. Nonetheless, in all cases these effects are small: they are comparable to the variation between randomly-seeded fine-tuning runs (), and much smaller than the difference between the full model and the Lexical or Randomized baselines, suggesting that most linguistic information from BERT is still available after fine-tuning.

Next, we turn to the structural probe, with results seen in Figure 1. First, the dependency parsing fine-tuned model shows improvements in both the correlation and the absolute metrics of root accuracy and UUAS, as early as layer 5. Since the structural probes are designed and trained to look for syntax, this result suggests that the fine-tuning improves the model’s internal representation of such information. This makes intuitive sense as the fine-tuning task is aligned with the probing task.

On the MNLI and SQuAD fine-tuned models, we observe small drops in performance, particularly with the final layer. These changes are most pronounced on the root accuracy and UUAS metrics, which score against a discrete decoded solution (argmin for root accuracy or minimum spanning tree for UUAS), but are smaller in magnitude on Spearman correlations which consider all predictions. This suggests that while some information is lost, the actual magnitude of change within the “syntactic subspace” is quite small. This is consistent with observations by Gauthier and Levy (2019) and suggests that information about syntactic structure is well-preserved in end-task models.

Overall, the results from these two probing techniques suggest that there is no catastrophic forgetting. This is surprising as a number of prior error analyses have shown that the fine-tuned models often do not use syntax McCoy et al. (2019b) and rely on annotation artifacts Gururangan et al. (2018)

or simple pattern matching

(Jia and Liang, 2017) to solve downstream tasks. Our analysis suggests that the while this linguistic information may not be incorporated into the final predictions, it is still available in the model’s representations.

5 What changes in the representations?

Supervised probes are highly targeted: as trained models, they are sensitive to particular linguistic phenomena, but they also can learn to ignore everything else. If the supervised probe is closely related to the fine-tuning task–such as for syntactic probes and a dependency parsing model–we have observed significant changes in performance, but otherwise we see little effect. Nonetheless, we know that something must be changing when fine-tuning–as evidenced by prior work that shows that end-task performance degrades if the encoder is completely frozen Peters et al. (2019). To explore this change more broadly, we turn to an unsupervised technique, Representational Similarity Analysis, and corroborate our findings with layer-based ablations.

5.1 Representational Similarity Analysis

Representational Similarity Analysis (RSA; Laakso and Cottrell, 2000) is a technique for measuring the similarity between two different representation spaces for a given set of stimuli. Originally developed for neuroscience Kriegeskorte et al. (2008), it has become increasingly used to analyze similarity between neural network activations (Abnar et al., 2019; Chrupała and Alishahi, 2019). The method works by using a common set of examples, used to create two comparable sets of representations. Using two kernels (possibly different for each representation space) to measure the similarity of paired examples, the sets of representations are then converted into two pairwise similarity matrices in . The final similarity score between the two representation spaces is calculated as the Pearson correlation between the flattened upper triangulars of the two similarity matrices.

In our application, we pass ordinary sentences (Wikipedia), sentence-pairs (MNLI), or question-answer pairs (SQuAD) as inputs to the BERT model, and select a random subset () of tokens as our stimuli. This choice of input is consistent with the masked language model pre-training and various fine-tuning tasks (such as SQuAD and dependency parsing) in analyzing the contextual representations for every token. We extract representations as the activations of corresponding layers from the two models to compare (e.g. Base vs. a fine-tuned model). Following previous applications of RSA to text representations (Abnar et al., 2019; Chrupała and Alishahi, 2019)

, we adopt cosine similarity as the kernel for all of our experiments.

While RSA does not require learning any parameters and is thus resistant to overfitting Abdou et al. (2019), the unsupervised technique is sensitive to spurious signals in the representations that may not be relevant to model behavior.555We note that probing techniques are more robust to this, since they learn to focus on relevant features. To mitigate this, we repeat the BERT pre-training procedure (as described in Section 3 of (Devlin et al., 2019)) from scratch three times. For each pre-trained checkpoints, we fine-tune on the three downstream task and report the average for these independent runs.

Figure 2: Comparison of the representations from BERT base and the various fine-tuned models, when tested on Wikipedia examples. The dependency probing model starts to diverge from BERT Base around layer 5, which matches the previous results from edge probing. For the MNLI and SQuAD fine-tuned models, the differences from the Base model mainly arise in the top layers of the network.


Figure 2 shows the results of our RSA analysis comparing the three task models, Dep., MNLI, and SQuAD, to BERT Base at each layer, using single-sentence inputs randomly selected from English Wikipedia. Across all tasks, we observe that changes generally arise in the top layers of the network, with very little change observed in the layers closest to the input. To first order, this may be a result of optimization: vanishing gradients result in the most change in the layers closest to the loss. Yet we do observe significant differences between tasks. Except for the output layer which is particularly sensitive to the form of the output (span-based for dependencies and SQuAD, or using the [CLS] token for MNLI), we see that MNLI involves the smallest changes to the model: the second-to-last attention layer still shows a very high similarity score of compared to the representations of the pretrained encoder. The SQuAD model shows a steeper change, behaving similarly to the Base model through layer 7 but dropping off steeply afterwards - suggesting that fine-tuning on this task involves a deeper, yet still relatively shallow reconfiguration of the encoder.

Finally, dependency parsing presents a dissimilar pattern: we observe the deepest changes, departing from the Base model as early as layers 4 and 5. This is consistent with our supervised probing observations (Section 4), in which we observe improved performance on syntactic features at a similar point in the model (Figure 1).

5.2 Layer Ablations

Figure 3: Effects of freezing an increasing number of layers during fine-tuning on performance, where different lines correspond to various tasks (we report the evaluation accuracy for MNLI, F1 score for SQuAD, and LAS for Dep). The point at -1 corresponds to no frozen components. The graph shows that only a few unfrozen layers are needed to improve task performance, supporting the shallow processing conclusion.
Figure 4: Effects of fine-tuning at earlier layers of BERT. We note that the MNLI evaluation accuracy and SQuAD F1 score approach the full model performance by layer 6, whereas the dependency parsing LAS seems to require more layers. This support our hypothesis that the processing of dataset-specific features is shallow.

As an unsupervised, metric-based technique, RSA tells us about broad changes in the representation space, but does not in itself say if these changes are important for the model’s behavior–i.e. for the processing necessary to solve the downstream task. To measure our observations in terms of task performance, we turn to two layer ablation studies.

Partial Freezing can be thought of as a test for how many layers need to change for a downstream task. We freeze the bottom layers (and the embeddings)–treating them as features–but allow the rest to adapt. Effectively, this clamps the first layers to have RSA similarity of 1 with the Base model. Also, we perform model truncation as a rough estimate of difficulty for each task, and as an attempt to de-couple the results of partial freezing from helpful features that may be available in top layers of BERT Base (Tenney et al., 2019a). Figure 3 (partial freezing) Figure 4 (truncation) show the effect on task performance.

The patterns we observe corroborate the findings of our RSA analysis. On MNLI, we find that performance does not drop significantly unless the last two layers are frozen, while the truncated models are able to achieve comparable performance with only three attention layers. This suggests that while natural language inference Dagan et al. (2006) is known to be a complex task in the limit, most MNLI examples can be resolved with relatively shallow processing. SQuAD exhibits a similar trend: we see a significant performance drop when 3 or fewer layers are allowed to change (e.g. freezing through layer 8 or higher), consistent with where RSA finds the greatest change. From our truncation experiment, we similarly see that only five layers are needed to achieve comparable performance to the full model.

Figure 5: Comparison of the representations in the MNLI (left) and SQuAD (right) fine-tuned models and those of BERT Base, with the different lines corresponding to examples coming from various datasets. These graphs show that fine-tuning models only lead to shallow changes, consolidated to the last few layers. Also, we see that fine-tuning has a much greater impact on the token representations of in-domain data.

Dependency parsing performance drops more rapidly still–in both experiments–again consistent with the previous results from RSA. This is perhaps surprising, since probing analysis Goldberg (2019); Marvin and Linzen (2018) suggests that many syntactic phenomena are well-captured by the pre-trained model, and diagnostics for dependency parsing in particular (Tenney et al., 2019b, a; Hewitt and Manning, 2019; Clark et al., 2019) show strong performance from probes on frozen models. Yet as observed with the structural probes (Figure 1) there is much headroom available, and it appears that to capture it requires changing deeper parts of the model. We hypothesize that this effect may come from the hierarchical nature of parsing, which requires additional layers to determine the tree-like structure, and fully reconciling these observations would be a promising direction for future work.

6 Out-of-Domain Behavior

Finally, we ask whether the effects of fine-tuning are general: do they apply only to in-domain inputs, or do they lead to broader changes in behavior? This is usually explored by behavioral methods, in which a model is trained on one domain and evaluated on another–for example, the mismatched evaluation for MNLI (Williams et al., 2018)–but this analysis is limited by the availability of labeled data. By using Representational Similarity Analysis, we can test this in an unsupervised manner.

We use RSA to compare the fine-tuned model to Base and observe the degree of similarity when inputs are drawn from different corpora. We use random samples from the development sets for MNLI (as premise [SEP] hypothesis) and SQuAD (as question [SEP] passage) as in-domain for their respective models,666Note that these are unseen during fine-tuning, although RSA scores do not change significantly if the MNLI or SQuAD training sets are used. and as the out-of-domain control we use random Wikipedia sentences (which resemble the pre-training domain). As in Section 5.1, we use the representations of tokens as our stimuli for each comparison.777We also tested single-sentence examples from MNLI and SQuAD by only taking the premise and question respectively; the trends were similar to Figure 5. Results for the MNLI and SQuAD fine-tuned models are shown in Figure 5.

We note that in both cases, the trend follows Section 5.1 in that the models diverge from BERT Base in the top layers, but there is a significantly larger change in representations on the in-domain examples. When evaluated on Wikipedia sentences, which resemble the pre-training data, the similarity scores are much higher. This suggests that fine-tuning leads the model to change its representations for the fine-tuning domain but to continue to behave more like the Base model otherwise.

7 Conclusions

In this paper, we employ three complementary analysis methods to gain insight on the effects of fine-tuning on the representations produced by BERT. From supervised probing analyses, we find that the linguistic structures discovered during pre-training remain available after fine-tuning. Prior studies (McCoy et al., 2019b; Jia and Liang, 2017) have shown that end-task models often fall back on simple heuristics; taken together, our results suggest that while present, perhaps the linguistic features are not always incorporated into final predictions.

Next, our results using RSA and layer ablations show that the changes from fine-tuning alter a fraction of the model capacity, specifically within the top few layers (up to some variation across tasks). Also, although fine-tuning has a significant affect on the representations of in-domain sentences, the representations of out-of-domain examples remain much closer to those of the pre-trained model.

Overall, these conclusions suggest that fine-tuning–as currently practiced–is a conservative process: largely preserving linguistic features, affecting only a few layers, and specific to in-domain examples. While the standard fine-tuning recipe undeniably leads to strong performance on many tasks, there appears to be room for improvement: an opportunity to refine this transfer step–potentially by utilizing more of the model capacity–to better the generalization and transferability.