Title: BERTs are Generative In-Context Learners
Venue: NeurIPS 2024
Accepted: yes
Keywords: in-context learning

masked language modeling

bert

language modeling

evaluation

inference
Abstract: While in-context learning is commonly associated with causal language models, such as GPT, we demonstrate that this capability also 'emerges' in masked language models. Through an embarrassingly simple inference technique, we enable an existing masked model, DeBERTa, to perform generative tasks without additional training or architectural changes. Our evaluation reveals that the masked and causal language models behave very differently, as they clearly outperform each other on different categories of tasks. These complementary strengths suggest that the field's focus on causal models for in-context learning may be limiting – both architectures can develop these capabilities, but with distinct advantages; pointing toward promising hybrid approaches that combine the strengths of both objectives.

BERTs are Generative In-Context Learners

Abstract

While in-context learning is commonly associated with causal language models, such as GPT, we demonstrate that this capability also 'emerges' in masked language models. Through an embarrassingly simple inference technique, we enable an existing masked model, DeBERTa, to perform generative tasks without additional training or architectural changes. Our evaluation reveals that the masked and causal language models behave very differently, as they clearly outperform each other on different categories of tasks. These complementary strengths suggest that the field's focus on causal models for in-context learning may be limiting - both architectures can develop these capabilities, but with distinct advantages; pointing toward promising hybrid approaches that combine the strengths of both objectives.

1 Introduction

Masked language models used to dominate the field of natural language processing due to their adaptability across diverse tasks and their superior performance compared to causal language models (Radford et al., 2018; Devlin et al., 2019). Between 2018 and 2020, the field witnessed a surge in the development of these models (Devlin et al., 2019; Liu et al., 2019; Lan et al., 2020, inter alia). However, the field dramatically shifted with GPT-3 and its introduction of in-context learning - the ability to infer and perform tasks from prompts and examples without any finetuning (Brown et al., 2020). This capability eliminated the need for task-specific training data and deep-learning expertise, making such models far more practical for real-world applications. This perceived advantage led many researchers and practitioners to abandon masked language models in favor of GPT-style architectures.

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Previous studies of 'emergent' in-context learning abilities have focused almost exclusively on causal language models, creating a widespread assumption that this capability is unique to them (Saunshi et al., 2021; Olsson et al., 2022; Wei et al., 2022; Wang et al., 2023, inter alia). In this paper, we challenge this assumption by demonstrating that in-context learning can emerge in masked language models as well. In-context learning is a more general phenomenon and should not be studied with a singular pretraining objective in mind. Moreover, the assumed inability of masked language models to perform (generative) in-context learning has rendered them outdated - as explicitly noted by Tay et al. (2023): 'BERT-style models are very restricted in their generative capabilities. Because of the cumbersomeness of task-specific classification heads, we strongly do not recommend using this class of autoencoding models moving forward and consider them somewhat deprecated.'

In this paper, we challenge these prevailing assumptions about masked language models (MLMs). We present empirical evidence showing that DeBERTa, an MLM released just one month after GPT-3, is equally adept at in-context learning. Our findings suggest that the capacity for in-context learning is not tied to the training objective, but can be achieved across different types of language models. To our surprise, we found that DeBERTa does not simply mimic the performance of GPT-3 - the two model behave very differently - DeBERTa is clearly much better on tasks such as language understanding, and, on the other hand, much worse on tasks such as closed-book question answering. This suggests that masked and causal language modeling are two complementary training objectives and that there is a great potential for a training method that combines the strengths of both objectives. Finally, scaling (performance improvement with increased size of pretrained language models) is a crucial feature of modern language models; we demonstrate that MLMs do scale on in-context learning (Figure 1).

We introduce a simple inference technique that transforms an MLM into a generative model without any further training. Using publicly available DeBERTa checkpoints, we show that the MLM training objective not only provides a versatile way of encoding text, but is also competitive in text generation and text completion ranking. This claim is tested by following the same evaluation suite as GPT-3, speculating on an 'alternative reality' in which a masked language model is the first model reported to achieve the so-called 'emergent' in-context learning abilities. While other masked language models could potentially demonstrate similar capabilities, we deliberately target DeBERTa because of its large size and its length-generalization abilities. Ultimately, our goal is to demonstrate that MLMs can perform in-context learning and that they can be surprisingly good at doing so.

Outline First, Section 2 (Method) describes the inference methods used to evaluate the in-context learning abilities of an off-the-shelf masked language model. Then Section 3 (DeBERTa) describes the details of the particular model used in this study. Section 4 (Evaluation) details the evaluation setup and compares DeBERTa with GPT-3. Finally, Section 5 (Related work) talks about other relevant work within this topic, and the paper concludes with Section 6 (Conclusion).

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2 Method: text generation and ranking with masked language models

The goal of this article is to reuse an existing pretrained masked language model for (generative) in-context learning. We achieve this without any additional training or finetuning, our method only slightly changes the sequence of input tokens, as illustrated in Figure 2. There are two methods used to solve tasks with in-context learning: text generation where the model completes a given prompt (e.g. for translation) and ranking where the model chooses an answer from several options (e.g. for multiple choice questions).

2.1 Text generation

Masked language models are trained on semi-supervised fill-in-the-blanks tasks and so they cannot be used to generate straight out of the box. One possibility is to interpret these models as Markov random fields and produce text by Gibbs sampling (Wang and Cho, 2019). However, a simpler and more consistent way to produce text is to do the familiar left-to-right autoregressive generation - we could place a [MASK] token next to a text prompt and let the model generate next token by unmasking the appended token - then, when we repeat this process in a loop, we can generate text in the same way as causal language models (and apply the same advanced generation techniques).

This straightforward inference scheme would be enough if the pretraining process were designed with this use case in mind. However, since our goal is to repurpose an existing masked language model, we have to complicate the method with two modifications that are also illustrated in Figure 2:

Masked language models are typically trained with a special end-of-sequence [SEP] token. This token is always present during pretraining and so we also have to include it as the last token during inference.

However, the addition of this end-of-sequence token creates a problem - it raises the probability that the masked token should end the sequence (for example with a full stop). Thus, in order to obtain a less restricted continuation, we include additional [MASK] tokens to pad the space in front of the end-of-sequence token. Specifically, we use two additional masks for the DeBERTa models. 1 This decision is later ablated in Appendix B.

In the end, this approach gives a probability distribution over the next token prediction, thus we can use any existing method for searching or sampling an output sequence. We follow GPT-3 and use beam search with four candidate beams for all generative tasks.

Limitations While this method works with the same quadratic time complexity (in sequence length), it is slower in practice because it is not possible to cache the intermediate self-attention key and value vectors. Instead, these have to be recomputed every step due to the bidirectional nature of the model. While our current implementation prioritizes demonstrating the core capability over optimization, several promising approaches could address these computational limitations in future work. For example, using prefix language modeling or selectively updating hidden vectors could significantly improve efficiency. We leave these optimizations for future work to maintain focus on establishing the fundamental ability of MLMs to generate text.

2.2 Ranking

Many of the existing tasks for evaluating LLMs can be formulated as classification tasks where models have to select the correct answer from a number of different options. Brown et al. (2020) rank the candidate completions based on their estimated conditional log-likelihood, which can be computed exactly by the chain rule (where w 0 ⊕ w 1 . . . w k is a completion of a prompt c ):

While this equation matches the training objective of causal language models, it is not suitable for masked language models because they are not trained to estimate P ( w i | c ⊕ w 0 . . . w i -1 ) . Instead,

Wang and Cho (2019) proposed to modify Equation (1) to make it more appropriate for BERT-like models. Salazar et al. (2020) then empirically showed that the resulting pseudo-log-likelihood (PLL) score can be used to accurately rank text sequences by their likelihood. Specifically, the PLL score is approximately proportional to the conditional probability of a text sequence and is computed as:

However, this approximation gets very inaccurate when there are strong local dependencies between tokens. As a counterexample, the estimated likelihood of the multi-token word 'supercalifragilisticexpialidocious' is seven orders of magnitude higher than that of the single-token word 'super' , which is clearly an incorrect estimation of the relative frequencies of these words. 2

We improve on this behavior by interpolating between the mathematically correct unidirectional derivation in Equation (1) and the bidirectional approximation in Equation (2). Our approach is to simply mask two additional tokens in the right context to reduce the effect of local dependencies while still taking into account the global bidirectional context. This process is illustrated in Figure 2. We conduct an ablation study of this approach in Appendix C.

Limitations Even though Equations (1) and (2) look similar, the later sum is substantially more compute intensive when calculated with a transformer architecture - for a token sequence of length k , the first equation can be computed with passing a single sequence through a language model, while the second equation needs k sequences to be processed. However, Salazar et al. (2020) showed that the PLL score can be accurately estimated in a single pass after a short self-supervised finetuning.

2.3 Length generalization

A potentially limiting factor of using BERT-like models is that they are typically pretrained on shorter sequences than causal language models - arguably because the training of modern causal models is already optimized for in-context learning, which requires processing of long few-shot prompts. DeBERTa is not an exception to such pretraining; it was only trained with a relatively short maximum sequence length of 512 tokens (He et al., 2021). Fortunately, the architecture of DeBERTa can easily process much longer sequences than seen during training due to its use of relative positional embeddings with logarithmic buckets (Raffel et al., 2020).

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We measure the extent to which DeBERTa generalizes to longer sequences with the 'needle in a haystack' test from RULER (Hsieh et al., 2024). Specifically, in our formulation of this task, a random 6-digit number (needle) is hidden in a long collection of essays (haystack). We then measure the exact-match accuracy of retrieving the hidden number given two variables: the total sequence length and the position of the needle in the haystack (more details about the evaluation setup are given in Appendix E.1).

The results in Figure 3 demonstrate that DeBERTa generalizes to sequences well beyond its training length, which is enabled by its relative positional encoding. For comparison, we also show results from OPT (Zhang et al., 2022), which uses absolute positional encoding like GPT-3. As expected from models using absolute positional encoding, performance drops sharply beyond the training length. This comparison highlights the importance of positional encoding choice for length generalization, independent of whether the model is masked or causal. In practice, this observation means that DeBERTa should be able to handle as many task demonstrations as models trained with longer sequences.

3 DeBERTa family of language models

This study uses the largest openly available English masked language model, DeBERTa with 1.5 billion parametrs, and its smaller configurations - 0.1B, 0.4B and 0.9B (He et al., 2021). DeBERTa is an improved version of a BERT language model (Devlin et al., 2019) that uses an advanced attention mechanism with relative positional embeddings - apart from being trained on a larger corpus and with a larger number of training steps.

Training corpus Compared to GPT-3 and modern large language models, DeBERTa was pretrained on a relatively small and clean text corpus - totalling 78GB of data after deduplication, the corpus is comprised of the English Wikipedia (12GB), BookCorpus (6GB; Zhu et al., 2015), OpenWebText (38GB; Gokaslan and Cohen, 2019), and STORIES (31GB; Trinh and Le, 2019). This is almost an order of magnitude less data than what was used to pretrain GPT-3. Notably, our strong results despite this data disparity - could suggest that masked language models are more data-efficient than causal models for developing in-context learning capabilities. This claim would however need to be evaluated with a comprehensive study. In comparison, GPT-3 uses 570GB of filtered CommonCrawl, WebText2 (roughly 26GB), two web-scraped book corpora (roughly 17GB and 76GB), and the English Wikipedia (roughly 4GB, estimated from Brown et al. (2020)).

Total training compute Interestingly, even though DeBERTa uses a substantially smaller training corpus, it is trained on more than three times more tokens than GPT-3 (1 trillion compared to 300 billion). 3 However, the loss is computed only on 15% of tokens (150 billion) and it is not clear what would be the effective number of tokens used for pretraining. Nevertheless, the total compute used for training depends on the number of input tokens and it is roughly 8 . 0 · 10 21 FLOPs for the 1.5B DeBERTa, and 2 . 4 · 10 21 FLOPs for the 1.3B GPT-3.

Causal conversion for HuggingFace We have converted the officially available DeBERTa checkpoint into a HuggingFace (Wolf et al., 2020) implementation of AutoModelForCausalLM (following the method in Section 2.1), and released it openly at https://hf.co/ltg/deberta-xxlarge-fixed . The weights of this model are exactly the same as the official release from microsoft/debertav2-xxlarge , but we have fixed some bugs found in the original modeling script in addition to implementing the text generation abilities. 4 Similarly, we have also converted the smaller DeBERTa models and released them as ltg/deberta-base-fixed , ltg/deberta-large-fixed , and ltg/deberta-xlarge-fixed .

4 Evaluation

As our goal is to compare two language models released around the same time in 2020 - GPT-3 and DeBERTa - we replicate the evaluation setup used for GPT-3 (Brown et al., 2020) and apply it to the latter model. This also means that we follow GPT-3 and divide the tasks into generative ones (such as machine translation) and into classification tasks (such as BoolQ) - the first group uses the method described in Section 2.1 and the second type of task uses the ranking described in Section 2.2. Generation is performed with beam search (4 candidate beams), and ranking uses the modified PLL scores (and the normalized unconditional probability of completions P ( completion | context ) P ( completion | answer context ) for ARC and OpenBookQA), again replicating the choices for GPT-3). We also use the exact same prompt templates, with the exception of the machine translation task - its template did not produce any meaningful output, and so we decided to use the simple prompt template from Garcia et al. (2023) instead. More details on the evaluation setup can be found in Appendix E. Note that using prompts optimized for GPT-3 is slightly unfair to all other models, as prompting has a strong influence on performance (Gonen et al., 2023), but we believe that it makes the results more convincing than if we were to do extensive prompt engineering.

To show the strengths and weaknesses of DeBERTa in (generative) in-context learning, we evaluate it on four groups of tasks and compare it to the results from Brown et al. (2020). The four groups are language understanding (SuperGLUE), language modeling (text completion and Winograd-like tasks), machine translation, and question answering (closed-book question answering and commonsense reasoning). We detail each of these groups of tasks below.

Before looking into the details of each group, we show the overall aggregated scores for each group in Figure 1 and Figure 4. The first figure shows how the performance of both models scales with their size, while the latter figure compares the in-context learning abilities of the two language models. We also provide a qualitative evaluation of text generation in Appendix A and full results in Appendix F.

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4.1 Language understanding (SuperGLUE)

We use SuperGLUE (Wang et al., 2019) as a popular collection of standard NLP tasks, allowing us to evaluate the performance on different aspects of natural language understanding.

In total, this benchmark consists of eight datasets, selected to be difficult for the contemporary (finetuned) language models. The Boolean Questions dataset is a yes/no reading comprehension dataset evaluated with accuracy (BoolQ; Clark et al., 2019); CommitmentBank is a three-class textual entailment dataset evaluated with accuracy and F1 score, where the multi-class F1 is computed as the unweighted average of the F1 per class (CB; de Marneffe et al., 2019); the Choice of Plausible Alternatives dataset is a causal reasoning task evaluated with accuracy (COPA; Roemmele et al.,

2011); Multi-Sentence Reading Comprehension is a multiple choice dataset, evaluated with exactmatch (of all answers per question) accuracy and F1 α score computed over all flattened answers (MultiRC; Khashabi et al., 2018); Reading Comprehension with Commonsense Reasoning Dataset is another reading comprehension dataset, it is evaluated with the exact-match accuracy and token-level F1 score (ReCoRD; Zhang et al., 2018); the collection of Recognizing Textual Entailment datasets is a textual entailment task evaluated with accuracy (RTE; Dagan et al., 2006; Bar-Haim et al., 2006; Giampiccolo et al., 2007); the Word-in-Context dataset is a word sense disambiguation dataset evaluated with accuracy (WiC; Pilehvar and Camacho-Collados, 2019); and finally, the Winograd Schema Challenge evaluates coreference resolution capabilities (WSC; Levesque et al., 2012).

Results We show the resulting scores of evaluation with the same prompts as GPT-3 in Table 1 and Appendix D. DeBERTa clearly outperforms its contemporary and scales much more favorably than the family of GPT models (Figure 1). Interestingly, the average performance of the 1.5B DeBERTa gets close to the reported performance of the largest 175B GPT-3 ( 68 . 4 vs. 68 . 9 , 1-shot). However, this average score is still far from the performance of a finetuned DeBERTa, which is more than 20 percentage points higher (He et al., 2021); the average few-shot performance of DeBERTa is slightly better than a finetuned BERT-large (Devlin et al., 2019; Wang et al., 2019).

4.2 Language modeling, Winograd-style and text completion tasks

The tasks in this category are defined in a familiar language-modeling form and focus on particularly difficult cases of language modeling, cases that involve commonsense reasoning, language understanding, and intricate coreference resolution.

In this group, we consider four NLP tasks: HellaSwag is a text completion task where a language model has to choose the most appropriate multi-word ending, the examples are adversarially filtered to be difficult for language models but easy for humans (Zellers et al., 2019). StoryCloze consists of five-sentence-long stories, the goal is to select the best final sentence based on commonsense knowledge (Mostafazadeh et al., 2016). Winograd is a language-modeling formulation of the WSC task from SuperGLUE (Levesque et al., 2012). WinoGrande is similar to Winograd in its form, but is adversarially mined to contain more difficult examples of coreference resolution (Sakaguchi et al., 2020). We do not include the LAMBADA benchmark (Paperno et al., 2016) here because Brown et al. (2020) used an unknown preprocessing step that disallows direct comparison with GPT-3.

Results We show the in-context-learning results from this group in Table 2, where we evaluate the 1.5B DeBERTa with a comparable GPT-3 model. The scores showcase consistently stronger performance of the masked language model, similarly to the language understanding tasks. One difference to those tasks is the rate of scaling, which appears to be similar between the two types of language models (Figure 1).

4.3 Translation

Translation is a useful benchmark for language models as it evaluates their ability to understand text in one language and produce fluent text in another language. Even though the performance on the translation tasks is arguably very dependent on the composition of training data (especially when we are concerned with monolingual English models), we include translation to demonstrate the generative performance of masked language models.

To directly compare DeBERTa with GPT-3, we use the same SacreBLEU metric (Post, 2018) and the same bitexts. Thus, even though there are more recent (and arguably more thought-out) datasets, we use the French-English pair from the outdated 2014 shared task at the Workshop on Statistical Machine Translation (WMT14; Bojar et al., 2014), and also the Romanian-English and German-English pairs from the WMT16 workshop (Bojar et al., 2016). Our approach differs only in using a different prompt template, as we had to opt for the prompt from Garcia et al. (2023) to get consistent translations: "{$source_language}: {$source_text}\\n {$target_language}: {$target_text}" .

Results The SacreBLEU scores on each language pair are given in Table 3. Unlike in the previous two task groups, the tables have turned, and the causal language model clearly outperforms the masked model in all comparisons. We believe that the subpar performance of DeBERTa can be (at least) in part explained by its relatively small and clean monolingual training corpus (Section 3), because the performance on this task is highly dependent on the presence of multilingual data in the corpus (Lin et al., 2022). The rate of improved translation performance with larger scale appears to be similar between the two models (Figure 1).

4.4 Closed-book question answering and commonsense reasoning

An important quality of modern-day large language models is their ability to learn and retrieve world knowledge, and to have a degree of common sense. The final group of tasks attempts to evaluate these two qualities.

This category of tasks consists of seven datasets in total: Natural Questions (NQs; Kwiatkowski et al., 2019) and Web Questions (WebQs; Berant et al., 2013) are closed-book question-answering datasets sourced from natural web queries; while the original datasets are accompanied by relevant articles that contain the answer, we only ask models a question and then evaluate the exact-match accuracy of their answers. TriviaQA is a very similar dataset, but based on online quizzes (Joshi et al., 2017). The next four tasks fall more into a subcategory of commonsense reasoning datasets. The Physical Interaction: Question Answering dataset evaluates how well a language model is grounded in the real physical world (PIQA; Bisk et al., 2020). The AI2 Reasoning Challenge is a dataset sourced from grade-school science questions that evaluates knowledge and reasoning abilities; this task is divided into ARC-Easy and ARC-Challenge splits, based on their difficulty (Clark et al., 2018). Finally, OpenBookQA evaluates the understanding of common knowledge (Mihaylov et al., 2018).

Results The question-answering performance is given in Table 4. Apparently, the results of DeBERTa are substantially worse on closed-book question answering compared to GPT-3. We believe that this highlights a more general disadvantage of the MLM training objective - the model can often retrieve world knowledge from the rich bidirectional context during training, not needing to store it in its learned weights; similar effect has been shown in retrieval-augmented language models (Samuel et al., 2024). However, the commonsense reasoning abilities are comparable between the two models. The scaling behavior is again similar between the two models (Figure 1). The same is also true about the improvement when given more in-context examples, which are especially important for the tasks evaluated with exact-match accuracy, where the goal is not only to answer correctly but also to match the expected style and form of the gold answers (Figure 4).

5 Related work

Few-shot finetuning with masked language models While our work demonstrates the emergence of in-context learning in masked language models, prior research has explored different approaches to few-shot learning with these architectures. The dominant paradigm has been few-shot finetuning , where the model's weights are updated using a small number of examples. Studies by Schick and Schütze (2021), Gao et al. (2021), and Xia et al. (2022) showed promising results with this approach. However, these methods require additional training steps with a complicated training objective,

making them more complex to implement compared to the simple prompting-based in-context learning demonstrated in our work. Despite the generally lower performance of in-context learning compared to few-shot finetuning (Liu et al., 2022), its simplicity and immediacy have made it the preferred choice in many practical applications.

Other large masked language models Our choice of DeBERTa for this study was motivated by its unique combination of size and capability to handle extended context lengths. While larger masked language models exist, such as Megatron BERT with 3.9 billion parameters (unfortunately not publicly available; Shoeybi et al., 2019) and XLM-RoBERTa with 10.7 billion parameters (Goyal et al., 2021), they have limitations that make them less suitable for studying in-context learning. Megatron BERT lacks mechanisms for length generalization, which is crucial for processing long prompts with multiple examples, while XLM-RoBERTa's multilingual nature and restricted sequence length of 512 tokens would confound our analysis. DeBERTa's architecture, particularly its relative positional embeddings, makes it an ideal candidate for exploring how masked language models scale with in-context learning.

Hybrid masked-causal models Our empirical findings, particularly the complementary strengths of masked and causal models demonstrated in Section 4, suggest significant potential in combining these approaches. Several architectures have already explored this direction, even if inadvertently: T5 (Raffel et al., 2020), BART (Lewis et al., 2020) and GLM (Du et al., 2022) introduced autoregressive fill-in-the-blank objectives; CM3 developed a causal-mask approach (Aghajanyan et al., 2022); and PrefixLM implemented a partially bidirectional causal model (Dong et al., 2019; Raffel et al., 2020). These efforts align with our observations about the distinct advantages of masked and causal objectives. The recent work by Ding et al. (2024) provides theoretical support for this direction, demonstrating that prefix language models, which combine aspects of both architectures, are particularly well-suited for in-context learning.

6 Conclusion

This paper demonstrates that masked language models can be capable in-context learners. We show that these models - often considered deprecated and limited only to finetuning - can match and sometimes even exceed the performance of their causal counterparts in this domain. Our evaluation reveals that masked and causal models exhibit remarkably similar characteristics in terms of overall performance, scaling behavior, and improvements with additional in-context demonstrations. Most notably, we validate these capabilities using DeBERTa without any architectural modifications or additional training. We achieve this through carefully designed inference methods that unlock the model's latent generative abilities.

Our findings point to several promising directions for future research. First, DeBERTa's performance could likely be enhanced through straightforward improvements such as training on larger and more diverse corpora, increasing model scale, and extending the pretraining context length. More fundamentally, the complementary strengths we observed between masked and causal models where each architecture excels in different tasks - suggest an exciting opportunity to develop hybrid approaches that combine the best of both paradigms. Rather than viewing these as competing architectures, future work might explore how to synthesize their distinct advantages into more capable and versatile language models.

These results argue for a broader reconsideration of how we approach language model architecture and training. The field's recent focus on causal models, while productive, may have prematurely discounted the potential of alternative approaches that are not limited to unidirectional text processing. Our work demonstrates that the path forward likely involves embracing architectural diversity rather than converging on a single dominant paradigm.

Acknowledgments and Disclosure of Funding

I am deeply grateful to Lilja Øvrelid, Andrey Kutuzov, and Erik Velldal for providing insightful feedback, for their never-ending encouragement and support, and for making Oslo a warm and welcoming place.

This work is fully funded by the University of Oslo. The computations were performed on resources provided through Sigma2 - the national research infrastructure provider for high-performance computing and large-scale data storage in Norway. We acknowledge Norway and Sigma2 for awarding this project access to the LUMI supercomputer, owned by the EuroHPC Joint Undertaking, hosted by CSC (Finland) and the LUMI consortium through project 5000144.

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A Examples of text generation

To also give a sense of the quality of the text produced by DeBERTa, we include some examples of text generation in this section. We use the exact same example prompts that were used in the GPT-3 paper (Brown et al., 2020), to provide a fair estimate of the generative qualities. All text completions were generated with nucleus sampling (Holtzman et al., 2020). 5 We compare the largest DeBERTa 1.5B with OPT 1.3B, an openly available replication of the GPT-3 1.3B (Zhang et al., 2022).

A.1 Learning and using novel words

Based on studies in developmental linguistics (Carey and Bartlett, 1978), this task tests the ability to understand and productively use new words; specifically using a word in a sentence after seeing it defined only once. We qualitatively test this ability in a generative one-shot setting, using the prompts provided below - there, the human-provided prompts are rendered as normal text while the generated completions are rendered in boldface. The prompts are taken from Brown et al. (2020, Section 3.9.5).

Results Overall, DeBERTa provides more appropriate example sentences than OPT. While the 'farduddle' and 'screeg' sentences from DeBERTa are not very descriptive, the rest of sentences are informative and fitting the word definitions. Note how the model tried to invent a plural inflection of 'yalubalu', the suffix '-a' is morphologically plausible, but the stem is fumbled, possibly because of subword tokenization. The examples generated by OPT are of lesser quality; it either repeats the definition (in 'farduddle'), repeats the one-shot example (in 'yalubalu') or provides an unfitting example (in 'screeg').

A.2 Correcting English grammar

English grammar correction is another task used for qualitative evaluation of GPT-3. Provided three demonstrations, the model is given an incorrect English sentence and is tasked to correct it. The prompts are taken from Brown et al. (2020, Section 3.9.6) and are shown below.

Results While the corrections are not perfect, DeBERTa outputs more sensible completions compared to OPT, similarly to the previous task. A notable correction is in the second example, where DeBERTa tried to improve the understandability, not only grammar.

B Ablation study of text generation

We empirically evaluate different approaches for text generation with DeBERTa language models. In particular, we study how many additional mask tokens should be used during autoregressive generation, and we also compare our approach with previously proposed methods based on Markovchain Monte Carlo sampling (Wang and Cho, 2019). For this analysis, we use German-to-English machine translation as a representative generative task. We evaluate different generation methods using one-shot prompts (following Section 2.1) and the largest 1.5B DeBERTa model. The translation quality is measured with SacreBLEU score, using the same signature as in the main experiments.

Results The results shown in Table 5 demonstrate that using additional mask tokens during generation substantially improves the performance of DeBERTa. This aligns with our hypothesis that additional masks help to reduce the effect of the end-of-sequence token on the generated text. The results also show that while adding a fourth mask token still marginally improves the performance, the gain is negligible compared to using three mask tokens.

We also compare our autoregressive approach with the Gibbs sampling method proposed by Wang and Cho (2019). There we relied on the same hyperparameters that are suggested in the official implementation: 500 total iterations, 250 burn-in iterations with top-100 sampling and temperature of 1 . 0 . 6 Their sampling-based approach performs substantially worse than autoregressive generation, regardless of the initialization strategy. We noticed that it often produces infinite token repetitions or locally-coherent but globally-disconnected pieces of text.

C Ablation study of ranking implementation

We mentioned some drawbacks of calculating the pseudo-log-likelihood score (PLL) as per Salazar et al. (2020), and how we mitigate these problems, in Section 2.2. This section supports our decision with a quantitative analysis of different ranking approaches. We test them on the ReCoRD task from SuperGLUE (Zhang et al., 2018; Wang et al., 2019), where the goal is to rank different named entities based on their appropriatness. Since the problem of the original PLL is in estimating likelihoods of long multi-loken expressions, we choose this task to highlight the differences. An example of a

prompt-formatted sample from ReCoRD is given below, the possible completions (all of which are named entities) are boldfaced:

Previous work After running the initial experiments, we were informed about the related work by Kauf and Ivanova (2023). Their study addresses a similar problem of the naive pseudo-log-likelihood scoring as this paper, but they do not target strongly correlated contiguous tokens in general, they focus on words that are split into multiple tokens - as they observed that PLL overestimates the likelihood of those sequences. We include their two proposed solutions in the comparison of different ranking methods.

Results In Table 6, we compare the original implementation of PLL by Salazar et al. (2020) that only masks the target subword; our approach that also masks next two subwords; other two alternatives that mask two or four subwords in total; the PLL-word-l2r and PLL-whole-word scoring functions by Kauf and Ivanova (2023); and the exact unidirectional computation of log-likelihood (using the same input formatting as for generation). The additional masks clearly help to make better estimates while the exact computation seems to not be appropriate for inherently bidirectional models.

D Detailed SuperGLUE results

This appendix provides a detailed analysis of DeBERTa's performance on the SuperGLUE benchmark, focusing on two aspects: the relationship between the number of demonstration examples and model performance (Figure 5), and the task-specific scaling behavior (Figure 6).

Effect of shot count Figure 5 demonstrates how DeBERTa's performance changes as we increase the number of in-context examples from 0 to 32. Unlike the main results where we optimize the number of shots for each subtask independently, here we deliberately use the same number of shots across all tasks to provide a controlled comparison with GPT-3's results from Brown et al. (2020); there, SuperGLUE tasks are the only ones with such detailed few-shot evaluation.

The plot reveals several interesting patterns:

The steepest improvement occurs between 0 and 4 shots, suggesting that even a small number of examples provides substantial benefit.

The overall trend is very similar between DeBERTa and GPT-3, which again shows that masked language models can be just as good in-context learners as causal language models.

The performance of DeBERTa decreases after 8 or more shots. We believe that this is mainly caused by its imperfect processing of contexts longer than 512 tokens - as discussed in Section 2.3.

Average GLUE performance vs. number of shots

0

1

2

3

4

8

16

32

Number of Shots

55.0

57.5

60.0

62.5

65.0

67.5

70.0

72.5

75.0

Average Score

DeBERTa (1.4B)

GPT-3 (175B)

Task-specific scaling analysis Figure 6 breaks down the scaling behavior for each SuperGLUE task individually, revealing the varying impacts of model size across different language understanding challenges. The plots demonstrate that while DeBERTa and GPT-3 both exhibit generally positive scaling trends, their scaling patterns differ substantially across task types. Most notably, DeBERTa shows steeper scaling curves than GPT-3 on the majority of tasks. This suggests that bidirectional attention mechanisms may provide particular advantages for scaling some language understanding capabilities.

Average 1-shot SuperGLUE performance vs. paremeter count

Average Score

0.1

0.8 2.6

13

175

Parameters

50

60

70

80

90

BoolQ

0.1

0.8 2.6

13

175

Parameters

40

60

80

CB

0.1

0.8 2.6

13

175

Parameters

60

70

80

90

COPA

0.1

0.8 2.6

13

175

Parameters

0

10

20

30

MultiRC

0.1

0.8 2.6

13

175

Parameters

60

70

80

90

100

ReCoRD

0.1

0.8 2.6

13

175

Parameters

40

50

60

70

80

RTE

0.1

0.8 2.6

13

175

Parameters

30

40

50

60

70

WiC

0.1

0.8 2.6

13

175

Parameters

50

60

70

80

WSC

0.1

0.4

0.8

1.3

2.6

6.7

13

175

Number of parameters (in billions)

50.0

52.5

55.0

57.5

60.0

62.5

65.0

67.5

70.0

DeBERTa

GPT-3

E Evaluation details

This appendix provides more details about our evaluation setup to make it easier to reproduce our results; the source code is available at https://github.com/ltgoslo/bert-in-context . In general, we follow Brown et al. (2020) in all decisions about the implementation.

Newline separator Note we use newlines in the displayed prompts for better readability, but we do not actually use them as the DeBERTa tokenizers cannot encode a newline character (they convert it to the standard whitespace character instead). Instead, we convert all newline characters to double-escaped ' \\n ' string with a whitespace character, which then acts as paragraph/information separator.

Few-shot prompting For each evaluated sample, the example demonstrations are randomly selected (without replacement) from the training set of each task; if the training set is not available, we sample from the only available dataset split, making sure not to select the same sample as the evaluated

one. We format these examples using the respective prompt templates and concatenate them together, joined by two newline characters. The numbers of shots used for each task are given in Appendix F.

E.1 Needle in a haystack

The needle is a randomly generated 6-digit number (from 100 000 to 999 999 ). The prediction is produced via constrained generation that only allows sequences of tokens that form 6-digit numbers. We consider a prediction to be correct only if it exactly matches the needle, which means that a trivial baseline has the accuracy of 1 / 900 000 .

The evaluated models are prompted with the template shown below. Similarly to Hsieh et al. (2024), we use Paul Graham's essays to fill the context ( $prefix_lines and $suffix_lines ). 7 The essays are sentence-segmented and concatenated to fill the desired total sequence length.

E.2 Language understanding

This section provides the prompts used for the eight SuperGLUE tasks, all prompt templates are taken from the GPT-3 evaluation setup.

BoolQ This task is evaluated by ranking texts formatted as shown below with two possible $answer s, yes or no .

CB Evaluated by ranking texts formatted as shown below with three possible $answer s, true , false or neither .

COPA We rank two possible substitutions of $answer that follow the premise. $connector is formatted based on the question type: ' because ' if the type is ' cause ', otherwise ' therefore ' is used.

MultiRC The potential answer is substituted for $option and then we rank two possible substitutions for $answer : [True] or [False] .

ReCoRD Here we rank the possible entity names as substitutions for $answer . Note that $paragraph often includes summaries that, in a way, act as few-shot examples (see the formatted zero-shot example in Appendix C).

RTE Ranking of two possible completions in this binary classification task: True or False .

WiC Another binary task with two possible substitutions: yes or no . Note that this prompt template was not working for any GPT-3 models and it is also not working for the models evaluated in this paper, all models are just randomly guessing the answers (Table 1).

WSC We rank possible substitutions for $answer .

E.3 Language modeling

This group of tasks uses very straightforward prompt templates as all of these tasks are different variants of text completion.

HellaSwag Here, the task is to select the most likely completion ( $answer ) that follows after $context .

StoryCloze The goal is to select the most suitable $answer that completes a story laid out by four previous sentences.

Winograd $answer should be substituted by the correct entity (coreference resolution).

Winogrande Same as Winograd:

E.4 Translation

All language pairs and all evaluation setups (zero-shot, one-shot and few-shot) use the same prompt template given below. This is the only time when we decided to differ from the GPT-3 setup as its very simple (and non-informative) prompt was not working for one-shot evaluation of DeBERTa models. The models are then asked to complete the prompt - using beam search decoding with 4 beams and the default HuggingFace hyperparameters 8 - and the generation is stopped after producing the special newline character \\n .

For reference, here are the two prompt templates used for GPT-3 (for zero-shot and for one/few-shot, respectively):

E.5 Closed-book question answering

This group of tasks mixes two types of in-context evaluation: text generation (Natural Questions, TriviaQA and Web Questions) and text ranking (PIQA, ARC and OpenBookQA). The prompt setup exactly follows GPT-3.

Natural Questions Here, the goal is to generate an answer based on $question .

TriviaQA Same as Natural Questions:

Web Questions Same as Natural Questions:

PIQA Here, the goal is to select the most suitable text completion by substituting for $answer .

ARC (challenge) and ARC (easy) This a multiple choice test, the correct $answer has to be selected.

OpenBookQA Similarly, the goal is to select the correct $answer .

F All results

For reference, we list all results of the DeBERTa models evaluated throughout this paper in Table 7. The GPT-3 results that were used for comparison are published in Brown et al. (2020, Table H.1).

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Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes a finding, indicating that masked language models like BERT can be used as in-context learners. This work suggests that there is potential for a hybrid training, where masked/causal language models can be involved, to take advantage of both objectives. The authors show that masked and generative LMs can outperform each other on different categories of tasks.\n\nAfter reading the authors' response, I think they made some good points and I think I will give accept to this paper.\n\nSTRENGTHS:\nRethinking and attempting to discover the advantage of masked LM is very impressive, especially in the era of generative large language models, where most researchers put their efforts into improving the generative LLM performance. The figures are pretty clear.\n\nWEAKNESSES:\n1. In Figure 1, the authors demonstrate that DeBERTa outperforms GPT-3 in language understanding. But this is more about the natural advantage of the masked language model. The motivations for providing such experimental data are quite vague to me. Additionally, I don't see the advantages of masked models in the other three tasks (Figure 1). In logic, if one method only occasionally shows its advantage, then this method can lack robustness and generalizability. Furthermore, since the masked language models lack large versions (For example 175B), it's quite necessary to justify why use the smaller masked LM over the large and powerful generative LM.\n\n2. This paper mainly uses GPT-3 and DeBERTa to perform experiments. Several issues can weaken this paper. In the era of LLM, GPT-3 can be outdated. There are amount of generative LMs, which are redesigned and well-trained to make them much more lightweight and powerful than GPT-3. Even DeBERTa can demonstrate certain advantages over GPT-3, such experimental results cannot fully support the statement of this paper. In the same way, masked language models have many implementations. The authors need to prove that either DeBERTa is the best masked LM or provide more elements using other masked LMs.\n\n3. As a technical paper, either provide a wide and systemically empirical study or provide a solid mathematical proof.\n\nQUESTIONS:\nAs I explained the weaknesses, authors are encouraged to answer the questions I made in the weaknesses section. For details:\n\n1. Which direction do you plan to conduct your research: \"either provide a wide and systemically empirical study or provide a solid mathematical proof\"? Please specify that and provide the relevant content.\n2. At least add Llama-2, Llama-3, Phi-2, Phi-3, Gemma-2B/7B as the exemplar generative LMs. For masked LM, if you cannot prove DeBERTa is the best masked LM (or find literature that supports your model choice), you should at least add BERT, RoBRETa, DistillBERT, T5.\n\nLIMITATIONS:\n1. Lack of experiments to support the statement/conclusion of this paper.\n2. Unclear the research conduct path.", "TITLE": "Official Review", "RECOMMENDATION": 7, "REVIEWER_CONFIDENCE": 4, "SOUNDNESS_CORRECTNESS": 2, "DATE": "2024-06-29"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper argues that masked language models (MLMs) are just as capable as autoregressive language models at in-context learning. To demonstrate this, the authors propose a generative inference technique that allows DeBERTa to generate text, as well as a hybrid autoregressive/MLM pseudo-log-likelihood estimation technique that allows DeBERTa to rank text sequences by likelihood.\n\nDeBERTa and GPT-3 models are compared on a series of NLP benchmarking tasks in an in-context learning setup. While GPT-3 excels at translation and QA, DeBERTa excels at text completion and SuperGLUE tasks.\n\nSTRENGTHS:\n1. This paper argues against the recent exclusive focus on scaling autoregressive language models, showing instead that masked language models also have significant potential as generative in-context learners. Relatedly, it is demonstrated that these two architectures are better for very different types of tasks. This could inform future efforts in training large-scale systems.\n2. The limitations are acknowledged explicitly for each of the proposed techniques, and are all written in a constructive way that suggest clear future directions.\n3. The writing and visuals are clear and enjoyable to read.\n\nWEAKNESSES:\n1. The text ranking procedure seems unfair to autoregressive models: the MLMs are given access to the right context, except for the two tokens that directly follow the one being predicted. This would give them unfair advantages on certain tasks that require more long-term dependency resolution, or which would benefit from lookahead (e.g., syntactic evaluation tasks); thus, the comparison seems a bit unprincipled in these tasks. Would it be possible to devise a technique that does not give the model any of the right context (e.g., shortening the end of the sequence to just [MASK] [MASK] [MASK] [SEP] at each prediction step, instead of giving the rest of the tokens before [SEP])? This would remove any semantic hints.\n2. The proposed inference techniques are model-specific. Thus, future efforts using MLMs will need to take into account the quirks of the MLM objective that a particular model uses when writing pseudo-log-likelihood estimation functions. The authors explicitly acknowledge this limitation.\n3. Using OPT as a comparison to DeBERTa in the length generalization experiments seems unfair: this model is informally known to perform poorly on many tasks compared to more recent architecturally similar models like Pythia or Llama. Thus, presenting these results side-by-side could mislead readers into believing that MLMs are unilaterally capable of length generalization while autoregressive models are not. I know the stated point of this paper is to compare models released around the same time, but actually, the paper seems to be about comparing different types of LM objectives at performing generative in-context learning. Thus, I think using some of the models from the cited RULER paper would be an acceptable and more fair comparison---or even just a set of Llama or Alpaca models.\n4. It is repeatedly stated that hybrid architectures making use of both autoregressive and masked LM elements is a promising future direction. However, no real evidence is given for this. It is true that MLMs and autoregressive models excel at different things, but why would this mean that combining them would give us the best of both worlds, as opposed to the worst (or simply some mean interpolation between their performances, rather than an overall gain over both)? A few papers are cited as examples, but these seem less like hybrid models and more like entirely distinct ideas (e.g., T5 is cited, but encoder-decoder models like these have different pros and cons even relative to encoder-only or decoder-only models).\n\nQUESTIONS:\nQuestions:\n* When computing PLL for sequence ranking, would it be possible to simply remove all of the right context and give three MASK tokens followed by SEP at each probability estimation?\n* Could you describe some specific ways in which one could combine autoregressive and masked LMs to truly get the best of both worlds? My (admittedly naïve) ideas about how one would do this all involve some mixture of objectives during pre-training. This seems like it would implicitly end up just giving you the advantages of MLMs, since a lot of their behavior comes from the fact that they receive more information (from the right context) than autoregressive systems.\n\nSuggestions/Typos:\n* L15-16: inter alig -> inter alia\n* When discussing MLM scoring (Salazar et al., 2020), could you also cite the improved version introduced by Kauf & Ivanovna (2023) here: https://aclanthology.org/2023.acl-short.80/\n\nLIMITATIONS:\nThe limitations of directly comparing models that perform inference with right context versus without could be more directly discussed. Otherwise, I think the authors have done a good job of discussing limitations.", "TITLE": "Official Review", "RECOMMENDATION": 7, "REVIEWER_CONFIDENCE": 4, "SOUNDNESS_CORRECTNESS": 3, "DATE": "2024-07-01"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper investigates whether BERT style masked language models can perform in-context learning in multiple LM benchmarks used in the GPT-3 paper, and specifically compares the results of DeBERTa to the GPT-3 model. The paper shows that in multi-choice Q&A, winogrand style quizzes the DeBERTA model can match (sometimes outperforms) GPT-3 model, whereas DeBERTa down performs in machine translation benchmarks. To be able to evaluate these models on generative tasks, the paper also introduces a simple way to use BERT like models in auto-regressive generation, which is itself a significant challenge in the field.\n\nSTRENGTHS:\n- The paper presents extensive results for MLMs for in-context learning which were missing and highly required in the field. \n- The paper proposes simple (slightly hacky) procedures for (i) auto-regressive sampling from MLMs, (2) calculating logprobs of a sequence of tokens with MLMs.\n\nWEAKNESSES:\n- The comparison of sampling procedures missing. I would like to see what scores you would get if you calculate logprobs by just summing individual pseudo-likelihoods of the tokens without doing +2 additional mask tokens. Similarly, what happens if you use Wang and Cho, 2019 method for text generation?\n- The few-shot scaling curves are not complete: only provided 0-shot, 1-shot, and the best few-shot result.\n\nQUESTIONS:\n- In machine translation benchmarks, were there any examples above L>512, and what the reported results did for those examples for GPT-3?\n- In Figure 3, you write “[w]e use an open-source replication of GPT-3, OPT, which should perform similarly on this task”. How did you know this? I would delete this claim unless you discuss the evidence. I think it would be unnecessary to discuss it here. So, I suggest removing that claim.\n- Figure 4 is a little odd. Why don’t you have an actual scaling curve with the actual number of shots but instead have 0, 1, and few labels?\n\nLIMITATIONS:\nAs noted in the paper, The sampling procedure is too slow to use in practice since for each token the model needs to process the entire sequence again due to bi-directional attention.", "TITLE": "Official Review", "RECOMMENDATION": 7, "REVIEWER_CONFIDENCE": 4, "SOUNDNESS_CORRECTNESS": 3, "DATE": "2024-07-13"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis work proposes a simple modification on masked LMs such that they can be used as a generative way and conduct experiments as generative models. The claim of this work is that, through this modification, masked LMs can be used as in-context learners as GPT-3, adapting to new tasks with further fine-tuning. The experiments demonstrate the performance improvement with DeBERTa and a few categories of NLP tasks.\n\nSTRENGTHS:\nOverall, this work demonstrate the possibility of using masked LMs in a generative way. Although the modification is simple, the experiment results on some benchmark evaluation tasks are impressive. \n\nIn addition, the method description with limitations show the full picture of the proposed methods, which gives readers a comprehensive understanding about this work.\n\nWEAKNESSES:\nDespite of the impressive results, the work and the experiments are still in the preliminary stage, where more work can be done or needed to be done. For example\n\n- The proposed modification may be too simple, and more technical modification is needed to improve the efficiency. For example, in section 2.1, the opportunity of providing a more technically solid work is simplified as “We believe that this limitation can be fixed, …” Intuitively, I agree, although I do expect more technical novelty from this part.\n- Similar concern also applies to section 2.2, where it says “We improve on this behavior by interpolating between …”. Again, more technical discussion and novelty on this part will be deeply appreciated.\n\nWithout appropriate technical depth, I am wondering what the technical novelty of this work could be. \n\nIn addition, the experiment design is less convincing. The title and most of the discussion are about masked LMs, while the experiments are only about DeBERTa. \n\nThere are also some writing issues about this paper, for example, \n\n- Although this paper is about in-context learning, the major modification is about turning masked LMs to generative models. So, it’s not clear to me how this can be particularly tied to in-context learning\n- What the notation means in equations 1 and 2?\n- I think there may be some typos in equation 2\n\nAbout experiments\n\n- It would be great to list the inference costs of DeBERTa and GPT-3\n- What are the values of $k$ for few-shot learning?\n\nQUESTIONS:\nPlease refer to the previous section.\n\nLIMITATIONS:\nThe paper has listed the limitations of the proposed methods, for example, how to improve the proposed algorithms. However, given the lack of technical depth, some of the listed limitations should be addressed in this work, instead of future work.", "TITLE": "Official Review", "RECOMMENDATION": 3, "REVIEWER_CONFIDENCE": 4, "SOUNDNESS_CORRECTNESS": 2, "DATE": "2024-07-14"}