Predicting Fine-Tuning Performance with Probing (2022)

Abstract: Large NLP models have recently shown impressive performance in language understanding tasks, typically evaluated by their fine-tuned performance. Alternatively, probing has received increasing attention as being a lightweight method for interpreting the intrinsic mechanisms of large NLP models. In probing, post-hoc classifiers are trained on “out-of-domain” datasets that diagnose specific abilities. While probing the language models has led to insightful findings, they appear disjointed from the development of models. This paper explores the utility of probing deep NLP models to extract a proxy signal widely used in model development – the fine-tuning performance. We find that it is possible to use the accuracies of only three probing tests to predict the fine-tuning performance with errors 40% - 80% smaller than baselines. We further discuss possible avenues where probing can empower the development of deep NLP models.

## introduction large-scale neural models have recently demonstrated state-of-the-art performance in a wide variety of tasks, including sentiment detection, paraphrase detection, linguistic acceptability, and entailment detection @xcite @xcite . developing systems for these tasks usually involves two stages: a pre-training stage, where the large neural models gain linguistic knowledge from weak supervision signals in massive corpora, and a fine-tuning stage, where the models acquire task-specific knowledge from labeled data. the fine-tuning results are widely used to benchmark the performances of neural models and refine the models' development procedures. however, these fine-tuning results are summary statistics and do not paint the full picture of deep neural models @xcite . as researchers are increas-ingly concerned about interpreting the intrinsic mechanisms of deep neural models, many datadriven assessment methods have been developed. these assessments usually follow the route of compiling a targeted dataset and running post-hoc analyses. until now, one of the most popular interpretation methods is referred to as probing. to probe a neural model, one uses a predictor to obtain the labels from the representations that are embedded using the neural model. probing analyses on deep neural models revealed some low-dimensional syntactic structures @xcite , common-sense knowledge @xcite and (to some extent) human-like abilities, including being surprised upon witnessing linguistic irregularity @xcite ) and reasoning about space and time @xcite . from the viewpoint of data-driven assessments, both fine-tuning and probing can reveal the abilities of deep neural networks, but they appear to steer towards different directions: in-domain vs. out-of-domain. fine-tuning uses in-domain data -we evaluate the models on the same distributions as those in deployment. probing, however, uses out-domain data: instead of simulating the deployment environment, the targeted datasets focus on diagnosing specific abilities. inclusive vs. specific. in fine-tuning, edge cases should be included, so the unexpected behavior after deployment can be minimized @xcite and the fine-tuning results can be stable @xcite . on the contrary, the probing datasets are more specialized, so smaller datasets suffice. 1 high performances vs. faithful interpretations. while fine-tuning methods are mainly studied from an algorithmic perspective to enhance the perfor-mance of language models, probing methods aim at assessing the faithfulness of language models. to fulfill the former objective, fine-tuning is accompanied by efforts in pre-training, collecting more data, building better representations, and exploring novel model architectures @xcite @xcite . conversely, the latter goal is pursued by borrowing inspirations from a variety of other sources, including psycholinguistic assessment protocols @xcite , information theory @xcite @xcite , and causal analysis @xcite . in short, probing assessments are more specialized (therefore more flexible) and less computationally expensive. in contrast, the performance scores of fine-tuning assessments are more relevant to the design and training of deep neural models. can probing be used in the development of deep neural models? this question involves two aspects: this paper attempts to answer both. for feasibility, we show that a crucial feedback signal in model development, the fine-tuning performance, can be predicted via probing results, indicating a positive answer to the feasibility question. for operation, we run extensive ablation studies to simplify the probing configurations, leading to some heuristics to set up probing analyses. we start with a battery of probing tasks and evaluate the utilities both task-wise and layer-wise ( §5.2 - §5.3). we then reduce the number of probing configurations, showing that as few as 3 configurations can predict fine-tuning results with rmses between 40% and 80% smaller than the control baseline ( §5.5). to further answer the operation question, we run ablation studies on different probing configurations, including probing methods ( §5.6) and the number of data samples ( §5.7). we also analyze the uncertainty of the results ( §5.8). our analysis shows the possibility of using probing in developing high-performance deep neural models. all codes are open-sourced at https://github.com/spoclab-ca/ performance_prediction . ## methods we present the overall analysis method and evaluation metric in this section. §5 elaborates the detailed experiment settings. predicting fine-tuning performance a deep neural model m can be fine-tuned on task t to achieve performance a t . let @xmath0, using n configurations. for example, a deep neural model @xmath1.85 on a @xmath2. with post-hoc classifiers applied to the 12 layers of m , we can probe for 12 test accuracies on a probing task (e.g., detecting the past vs. present tense), which constitute of s. 2 to find the pattern across a diverse category of models, we regress over k models (we will describe in §4.3). the collected probing results {s (k) } k @xmath3. formally, this procedure optimizes for n + 1 parameters, θ ∈ r n +1 so that: this procedure has closed-form solutions that are implemented in various scientific computation toolkits (e.g., r and scipy). the minimum reachable rmse is therefore: rmse-reduction while rmse can evaluate the quality of this regression, it is insufficient for measuring the informativeness of s due to the discrepancy among the fine-tuning tasks t. suppose we have two tasks, t 1 and t 2 , where the probing results s can support high-precision regressions to @xmath4.01 on both tasks. however, on t 1 , even features drawn from random distributions foot_2 might be sufficient to reach @xmath5.02, while on the more difficult task, t 2 , random features could only reach @xmath6.10 maximum. the probing results s is more useful for t 2 than t 1 , but rmse itself does not capture this difference. considering this, we should further adjust against a baseline, the minimum reachable rmse using random features. in the experiments, all rmse and rmse c values follow 5-fold cross validation. we report the rmse_reduction as the score that measures the utility of s. ## discussion can probing results generalize to nonclassification tasks? all fine-tuning tasks and probing tasks in this paper are text-based classification problems. in the interpretable nlp literature, the probing analyses can also apply to other categories of deep neural networks including translation @xcite probing is computational-friendly compared to fine-tuning, probing evaluations require less computation. fine-tuning the 6 glue tasks takes around 30 gpu hours in total, while probing the 7 tasks (all 12 layers) takes 0.7 gpu hours to cache and 1.3 cpu hours to probe. we elaborate the computational budgets in appendix a. probing is far more efficient because it does not need to change the parameters in the neural model, and we only need one pass through the neural model and cache the representations. fine-tuning needs the gradients to update the parameters in the neural models. there are some methods to reduce the computation costs, 13 and we note that probing is competitive as well, in terms of computational time. fine-tuning tasks need more specifications currently, the most popular leaderboards for natural language understanding constitute fine-tuning tasks. a criticism towards these leaderboards is underspecification (d'amour et al., 2020) -the short descriptions of the tasks can hardly be inclusive enough to specify the precise abilities required to complete the tasks. to further understand the underspecification problem, researchers recently developed probing datasets @xcite . probing results on these datasets have been (indirectly) used in developing deep neural models -performance prediction is a more direct application. ## conclusion this paper shows that analyzing probing results can be relevant to developing deep nlp models via predicting a proxy signal, i.e., fine-tuning performance. we show that as few as three probing accuracy scores can be useful to predict finetuning results with rmses 40% -80% smaller than baselines. this can dramatically improve the efficiency of deep learning pipelines. given several ablation studies, we recommend mlp-20 and randomforest-100 over other probing methods and show that the probing results from as few as 400 per class may still contain predictability. probing analysis contain rich resources, and we show their results are closely related to fine-tuning performances. we call for further applications of probing into the developments of deep nlp models.