Report on Current Developments in Natural Language Generation (NLG)
General Direction of the Field
The field of Natural Language Generation (NLG) is currently undergoing significant advancements, driven by a deeper understanding of language structures and the limitations of existing models, particularly in non-English languages. Recent research is increasingly focusing on addressing language-dependent disparities introduced by data-intensive methods, which predominantly use English as the main training data. This shift is evident in the exploration of alternative language generation strategies for languages with different grammatical structures, such as Spanish, which exhibit less rigid word ordering and subject omission.
A notable trend is the evaluation of causal versus non-causal language modeling, particularly in languages like Spanish and English. This approach aims to identify the most effective generation strategies by analyzing the predictability and entropy of grammatical categories in different contexts. The findings suggest that while causal models perform well for English, non-causal models may be more suitable for languages like Spanish, where bidirectional transformer language models could offer better performance.
Another significant development is the exploration of next-token prediction (NTP) and its implications on model representations. Researchers are delving into the implicit geometry of NTP, examining how it influences the mapping of linguistic patterns to geometric properties of model representations. This work highlights the importance of understanding the sparse and low-rank structures in large embedding spaces, which can lead to insights on how to improve the learning of linguistic patterns and regularities.
The concept of predictability maximization is also gaining traction, particularly in understanding the optimal placement of heads and dependents in linguistic sequences. This information-theoretic approach offers insights into the harmonic order of words, suggesting that different placements can maximize predictability depending on the focus—whether it is on the head or the dependents.
Lastly, there is a growing recognition of the limitations of the current NTP paradigm, particularly in terms of target narrowing and error propagation. Researchers are proposing extensions like Next Distribution Prediction (NDP), which aims to enhance learning by using more representative distributions, thereby improving performance across various tasks and domains.
Noteworthy Papers
Predictability and Causality in Spanish and English Natural Language Generation: This paper provides a novel metric for comparing causal and non-causal language modeling, suggesting that non-causal models may be more effective for Spanish.
Implicit Geometry of Next-token Prediction: From Language Sparsity Patterns to Model Representations: This work offers a framework for analyzing the geometry of word and context embeddings, highlighting the importance of understanding the sparse and low-rank structures in NTP.
NDP: Next Distribution Prediction as a More Broad Target: Introducing NDP, this paper demonstrates significant improvements in various tasks by addressing the limitations of the current NTP paradigm.
These papers collectively represent a significant step forward in understanding and improving NLG, particularly in non-English languages and underrepresented dialects.
