Large Language Models as Scientific Models of Public Languages

Основні поняття

Large language models can serve as scientific models of public languages, providing insight into the nature of language as an external, social entity, rather than just as a cognitive phenomenon.

Анотація

The author argues that large language models (LLMs) can provide valuable scientific insight into the nature of language, but not by serving as theories of linguistic cognition. Instead, the author proposes that LLMs can be fruitfully understood as models of public languages, or "E-languages" - languages understood as external, social entities rather than as internal mental representations.
The author first responds to arguments that LLMs cannot inform linguistic inquiry, showing that these arguments rely on the assumption that linguistics should only be concerned with the cognitive processes behind linguistic competence (I-languages). By adopting a pluralist view that recognizes language as having mental, social, and abstract dimensions, the author argues that the study of E-languages is a legitimate and important linguistic project.
The author then outlines how LLMs can serve as models of E-languages. While LLMs are opaque and their construction is not directly controlled by the modeler, the author argues that techniques from explainable AI and the use of evaluation tasks can help reduce the uncertainty about how LLMs are linked to the target phenomenon of E-languages. The author provides an example of how syntactic dependencies could be modeled using LLMs and their internal representations.
Finally, the author considers and rejects the objection that LLMs are merely models of their training corpus, rather than models of public languages. The author argues that features of LLM training, such as fine-tuning and evaluation on tasks beyond just language prediction, show that LLMs go beyond simply reproducing their training data.

Статистика

"LLMs learn to track linguistic features in an autonomous fashion, without prior specification as to what should be captured, we have the opportunity to construct models that capture linguistic features that we wouldn't have otherwise known how to capture."
"Efforts are usually made that the tasks that models are evaluated against do not rely on data that was included in the pre-training set."

Цитати

"LLMs can shed light on the nature of language. But contrary to those who have previously defended the positive position, I will argue that LLMs do not do so by providing theories of a language. Instead, I will argue that LLMs can fruitfully be thought of as models in the scientific sense: as a structure that serves as a proxy for a real world phenomenon."
"Recognition of the role of the construal is important as it reveals a number of ways in which the intentions of the modeller are relevant for deciding whether the model in question does successfully capture the target."

Ключові висновки, отримані з

Modelling Language

by Jumbly Grind... о arxiv.org 04-16-2024

https://arxiv.org/pdf/2404.09579.pdf

Глибші Запити

How can the construal of LLMs as models of E-languages be further refined and validated through empirical investigation?

The construal of LLMs as models of E-languages can be refined and validated through empirical investigation in several ways. One approach is to conduct in-depth analyses of the internal workings of LLMs using eXplainable AI (XAI) techniques. By employing XAI methods such as probing classifiers and visualization tools, researchers can gain insights into how LLMs process linguistic data and identify specific patterns related to linguistic features like syntactic relations. This empirical investigation can help establish a clearer understanding of how LLMs capture and represent linguistic conventions.
Furthermore, empirical validation can be achieved by comparing the outputs of LLMs with human linguistic judgments. By designing experiments where human participants evaluate the linguistic quality of text generated by LLMs, researchers can assess the model's ability to capture linguistic nuances and conventions accurately. This comparative analysis can provide valuable insights into the strengths and limitations of LLMs as models of E-languages.
Additionally, conducting studies that involve fine-tuning LLMs on specific linguistic tasks or datasets that are distinct from the original training corpus can help validate the model's adaptability and generalizability. By evaluating the performance of fine-tuned LLMs on a variety of language comprehension tasks, researchers can assess the model's capacity to learn and apply linguistic knowledge beyond its initial training data.
Overall, refining and validating the construal of LLMs as models of E-languages through empirical investigation involves a combination of XAI techniques, human evaluations, and task-specific fine-tuning experiments to gain a comprehensive understanding of the model's linguistic capabilities and limitations.

What are the limitations of using LLMs as models of E-languages, and how might these limitations be addressed?

One limitation of using LLMs as models of E-languages is the inherent opacity of deep neural networks, which can make it challenging to interpret how the model processes linguistic data and captures linguistic features. To address this limitation, researchers can leverage XAI techniques to enhance the interpretability of LLMs. By employing methods such as attention mapping, feature visualization, and probing classifiers, researchers can gain insights into the internal mechanisms of LLMs and better understand how they encode linguistic information.
Another limitation is the potential bias or lack of diversity in the training data used to pre-train LLMs, which can impact the model's performance on languages or dialects that are underrepresented in the training corpus. To mitigate this limitation, researchers can explore techniques like data augmentation, multi-lingual training, and domain adaptation to improve the model's robustness and generalizability across diverse linguistic contexts.
Furthermore, the reliance of LLMs on large-scale training data poses a challenge in terms of computational resources and data privacy concerns. To address this limitation, researchers can explore methods for efficient training, such as knowledge distillation, model compression, and federated learning, to reduce the computational burden and enhance the scalability of LLMs.
Overall, addressing the limitations of using LLMs as models of E-languages requires a multi-faceted approach that combines XAI techniques, data augmentation strategies, and efficient training methods to improve the interpretability, generalizability, and scalability of the models.

What insights about the nature of language as a social phenomenon might be gained by studying LLMs as models of E-languages that could not be obtained through other approaches?

Studying LLMs as models of E-languages can provide unique insights into the nature of language as a social phenomenon that may not be easily obtained through other approaches. One key insight is the emergent properties of language conventions and norms that arise from the collective behavior of linguistic communities. By analyzing how LLMs learn and encode linguistic patterns from large-scale corpora, researchers can gain a deeper understanding of how language conventions evolve and spread within social groups.
Additionally, studying LLMs as models of E-languages can shed light on the dynamics of language change and variation within communities. By examining how LLMs adapt to different linguistic contexts and dialectal variations, researchers can explore the factors that influence language evolution and the mechanisms through which linguistic diversity is maintained or diminished over time.
Furthermore, LLMs can offer insights into the role of context and social interaction in shaping language use and interpretation. By analyzing how LLMs process contextual information and generate language output in response to different stimuli, researchers can investigate the influence of social factors on language production and comprehension.
Overall, studying LLMs as models of E-languages provides a novel perspective on language as a social phenomenon, offering valuable insights into the complex interplay between individual cognition, social interaction, and cultural norms in shaping linguistic behavior and communication patterns within communities.