The Bottom-up Evolution of Representations in the Transformer: A Study with Machine Translation and Language Modeling Objectives (1909.01380v1)

Abstract: We seek to understand how the representations of individual tokens and the structure of the learned feature space evolve between layers in deep neural networks under different learning objectives. We focus on the Transformers for our analysis as they have been shown effective on various tasks, including machine translation (MT), standard left-to-right LLMs (LM) and masked LLMing (MLM). Previous work used black-box probing tasks to show that the representations learned by the Transformer differ significantly depending on the objective. In this work, we use canonical correlation analysis and mutual information estimators to study how information flows across Transformer layers and how this process depends on the choice of learning objective. For example, as you go from bottom to top layers, information about the past in left-to-right LLMs gets vanished and predictions about the future get formed. In contrast, for MLM, representations initially acquire information about the context around the token, partially forgetting the token identity and producing a more generalized token representation. The token identity then gets recreated at the top MLM layers.

• The paper reveals that Transformer layers refine token representations in a bottom-up manner, exhibiting distinct dynamics for MT, LM, and MLM objectives.
• The study employs Canonical Correlation Analysis and the Information Bottleneck approach to quantify information flow and representation changes across layers.
• Findings indicate that MLM objectives better balance context encoding and token identity preservation compared to LM, informing effective pretraining strategies.

Analysis of Token Representation Evolution in Transformers

The paper "The Bottom-up Evolution of Representations in the Transformer: A Study with Machine Translation and LLMing Objectives" presents an in-depth examination of how token representations evolve between layers in Transformer models, contingent upon the learning objective. The authors, Elena Voita, Rico Sennrich, and Ivan Titov, aim to elucidate the manner in which different training objectives—specifically, Machine Translation (MT), Left-to-Right LLMing (LM), and Masked LLMing (MLM)—affect the processing of representations across the layers of a Transformer.

Transformers have emerged as crucial architectures in NLP, producing state-of-the-art results across diverse tasks. Despite their success, understanding what representations these models learn internally is a challenge. Previous research often employed probing tasks to analyze model representations, but this paper takes a step further by employing Canonical Correlation Analysis (CCA) and mutual information estimations to inspect the flow of information across layers in a more nuanced manner.

Key Concepts and Findings

1. Information Flow and Representation Changes: The paper employs an information-theoretic approach rooted in the Information Bottleneck (IB) method, which postulates that neural networks progressively encode only the information necessary to make accurate predictions, discarding irrelevant details through the layers. This paper adapts IB to assess how token-level representations shift from layer to layer in Transformer models.
2. Task-Specific Representation Dynamics:
   • LLMing (LM): In LMs, the representations progressively lose information about the input token identity as they ascend the layers while forming predictions about subsequent tokens. This aligns with the natural characteristic of LM in emphasizing future token prediction.
   • Masked LLMing (MLM): MLM representations initially lose details about the individual token to generalize from context, but in deeper layers, the specific token identity is reconstructed. This two-step process of context encoding followed by token prediction offers insights into why MLM is advantageous in pretraining contexts.
   • Machine Translation (MT): Although MT representations also refine with context, they retain more information about the original token identity compared to LMs. This observation is intuitive given MT’s necessity to keep translation details accessible for the decoder’s subsequent stages.
3. Canonical Correlation Analysis (CCA): The paper applies Projection Weighted CCA to measure the correlations between layers across models with different objectives. Notably, it finds more considerable differences between models trained on dissimilar objectives (LM vs. MT or MLM) than between different random initializations of the same objective. Interestingly, MT and MLM objectives result in representations closer to each other than those produced by LM.
4. Insights into Pretraining Efficacy: The findings suggest why MLM pretraining may outperform LM: MLM’s capability to leverage contextual encoding and retain extensive contextual understanding before a precise token prediction process.
5. Understanding through Feature Analysis: By employing t-SNE visualization and CCA, the authors demonstrate that the internal dynamics within MLMs and LMs align closely with encoding qualitative features like syntactic context and maintaining crucial syntactical structures across layers. These analyses reveal that MLMs adeptly manage the dual objectives of detailed syntax encoding and token identity preservation.

Implications

The implications of this work are twofold: practically, it offers guidance on the choice between LM and MLM objectives for efficient pretraining strategies, particularly illustrating MLM’s superior property of context enhancement before prediction. Theoretically, it provides a robust framework to further dissect how different learning objectives sculpt neural representations and how these can be characterized or manipulated for better downstream task performance.

In future research, the methodologies and findings from this paper could propel advancements in model interpretability and robustness across diverse NLP tasks. Exploring similar dynamics in emerging architectures or unconventional objectives could further enhance our understanding of neural representation learning.

Conclusion

The paper by Voita, Sennrich, and Titov is a critical contribution toward comprehending how learning objectives impact representation dynamics in Transformer models. It elucidates the nuanced ways in which different tasks shape token representation evolution, providing valuable insights for NLP research and application development. This paper’s computational rigour and analytical depth make it a pertinent reference for ongoing and future explorations into the inner workings of Transformer-based architectures.