Semiparametric Token-Sequence Co-Supervision (2403.09024v1)

Abstract: In this work, we introduce a semiparametric token-sequence co-supervision training method. It trains a LLM by simultaneously leveraging supervision from the traditional next token prediction loss which is calculated over the parametric token embedding space and the next sequence prediction loss which is calculated over the nonparametric sequence embedding space. The nonparametric sequence embedding space is constructed by a separate LLM tasked to condense an input text into a single representative embedding. Our experiments demonstrate that a model trained via both supervisions consistently surpasses models trained via each supervision independently. Analysis suggests that this co-supervision encourages a broader generalization capability across the model. Especially, the robustness of parametric token space which is established during the pretraining step tends to effectively enhance the stability of nonparametric sequence embedding space, a new space established by another LLM.

• The paper demonstrates that integrating parametric token (NTP) and nonparametric sequence (NSP) supervision significantly enhances language model performance.
• The STSC method leverages dual training to improve model generalization across both in-domain and out-of-domain datasets with a 14.2% average boost.
• The approach exhibits a synergistic effect between embedding spaces, setting a new benchmark for robust and accurate language model training.

Semiparametric Token-Sequence Co-Supervision Enhances LLM Performance

Introduction to Semiparametric Token-Sequence Co-Supervision

The Semiparametric Token-Sequence Co-Supervision (STSC) method represents an innovative approach to training LLMs. By incorporating both parametric token embedding space supervision (Next Token Prediction, NTP) and nonparametric sequence embedding space supervision (Next Sequence Prediction, NSP), STSC aims to significantly augment the capabilities of LLMs. The nonparametric sequence embedding space is constructed by leveraging a separate LLM, specifically designated for compressing input text into a representative single embedding. Such a dual-supervision training paradigm has shown to outperform traditional methods that utilize either of the supervision techniques in isolation.

Methodological Insights

STSC integrates supervision from both a parametric and a nonparametric perspective, capitalizing on the strengths of each to enhance model performance. The underlying hypothesis suggests that LLMs can not only accommodate additional embedding spaces but can also benefit from the well-established robustness of the parametric token embedding space, especially when integrating new embedding spaces.

• Next Token Prediction (NTP): Follows traditional LLM training by predicting the next token in a sequence.
• Next Sequence Prediction (NSP): Extends the prediction to entire sequences, leveraging nonparametric sequence embeddings derived from another LLM.
• Co-Supervision: Combines NTP and NSP supervisions in a multi-task training framework. This approach encourages the model to exploit both token and sequence-level embeddings concurrently.

Experimental evidence supports the broader generalization capabilities facilitated by this co-supervision, with noticeable improvements across a range of information-seeking datasets. Particularly, the semiparametric token-sequence co-supervised models demonstrate enhanced performance attributes, notably in both in-domain and out-of-domain scenarios.

Empirical Validation and Analysis

Models trained using the STSC method were rigorously evaluated against those trained using traditional supervisory approaches. Across 10 diverse information-seeking benchmarks, the STSC approach consistently yielded superior performance, with an average improvement of 14.2%.

• Robustness of Nonparametric Space: The integration of NSP supervision alongside traditional NTP supervision contributes to the stability and robustness of the nonparametric sequence embedding space.
• Enhanced Generalization: The co-supervised models exhibit better generalization across varying input distributions, indicating a more versatile understanding of language.
• High Interaction Between Embedding Spaces: Evidence suggests a synergistic effect between parametric and nonparametric embedding spaces, with the co-supervised model utilizing knowledge from both to generate responses.

Theoretical and Practical Implications

The STSC method stands to revolutionize how LLMs are trained and how they interact with both parametric and nonparametric embeddings. The ability to effectively bridge these two spaces opens new avenues for constructing more powerful and nuanced AI systems capable of understanding and generating text with unprecedented accuracy and contextual awareness.

Future Outlook

The STSC framework presents a pioneering shift in LLM training paradigms. While the immediate advantages are clear, the full extent of its potential and adaptability remains open for exploration. Future research could explore the applicability of STSC across various parametric and nonparametric embeddings, extending beyond token-sequence co-supervision to include other forms of embeddings, potentially uncovering new pathways to AI advancements in natural language understanding and generation.

Conclusion

Semiparametric Token-Sequence Co-Supervision represents a significant advancement in LLM training, merging the strengths of both parametric and nonparametric approaches to push the boundaries of model performance. This method not only enhances the robustness and generalization capabilities of LLMs but also provides an insightful framework for future research in generative AI.