Exploring Linguistic Intentionality in Large Language Models

Keskeiset käsitteet

Large language models can be considered meaningful users of language by applying linguistic metasemantic theories, which focus on how linguistic expressions come to have meaning, rather than mental metasemantic theories that focus on intentional mental states.

Tiivistelmä

The content explores the question of whether the outputs produced by large language models (LLMs) like Chat-GPT can be considered meaningful language use, or whether they are merely mimicking language use without genuine understanding.
The author first provides an overview of how LLMs are constructed, focusing on the theoretical background of distributional semantics that motivates their development. This background suggests that LLMs may have access to certain semantic properties, contrary to some skeptical arguments.
The author then considers applying mental metasemantic theories, which focus on the conditions for intentional mental states, to LLMs. While some have argued that LLMs can meet these conditions, the author argues that LLMs trained only on pre-training tasks like next-word prediction do not plausibly satisfy the requirements for mental intentionality.
Finally, the author proposes that linguistic metasemantic theories, which focus on how linguistic expressions come to have meaning, provide a more promising approach for considering the meaningful usage of LLMs. The author examines two such theories - Gareth Evans' account of naming practices and Ruth Millikan's teleosemantics - and argues that they can plausibly attribute meaning to LLM outputs without requiring intentional mental states.
The key insight is that linguistic intentionality relies on a pre-existing meaningful system, which LLMs can employ in the same way as ordinary language users, even if they lack the mental states required by mental metasemantic theories.

Tilastot

"Reproducing the sounds or shapes of words is not sufficient for meaningful language use."
"Should we group large language models (LLMs) with the ant and the parrot?"
"Doing so will naturally require that we pay careful attention to the inner workings of LLMs."
"Externalism about meaning is true, then whether a given entity counts as meaningful partly depends on features external to that entity."

Lainaukset

"To the extent that word embeddings continue to be the state-of-the-art across all such tasks, the distributional hypothesis – which goes beyond a claim about the best way to predict plausible text – is made plausible."
"It is already widely-recognized that in performing the prediction task in pre-training, LLMs become sensitive to linguistic features that are not explicitly represented in the training data."
"The key insight is that linguistic intentionality relies on a pre-existing meaningful system, which LLMs can employ in the same way as ordinary language users, even if they lack the mental states required by mental metasemantic theories."

Tärkeimmät oivallukset

Large language models and linguistic intentionality

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

https://arxiv.org/pdf/2404.09576.pdf

Syvällisempiä Kysymyksiä

How might the meaningful usage of LLMs evolve as they are further developed and integrated into real-world applications?

As large language models (LLMs) continue to be developed and integrated into real-world applications, their meaningful usage is likely to evolve in several ways.

Improved Contextual Understanding: Future advancements in LLMs may lead to better contextual understanding, allowing them to generate more accurate and contextually appropriate responses. This would enhance their ability to engage in meaningful conversations and tasks.

Enhanced Multimodal Capabilities: LLMs may evolve to incorporate multimodal capabilities, such as understanding and generating text, images, and audio. This would enable them to interact more effectively in diverse communication settings.

Increased Personalization: With further development, LLMs could become more personalized, adapting their responses based on individual user preferences, history, and context. This personalization would contribute to more meaningful interactions.

Ethical and Bias Mitigation: Future advancements in LLMs may focus on mitigating ethical concerns and biases in language generation. By addressing these issues, LLMs can ensure that their outputs are more meaningful and inclusive.

Domain-Specific Expertise: LLMs could be tailored to specific domains or industries, acquiring specialized knowledge and expertise. This would enable them to provide more accurate and relevant information in specialized fields, enhancing their meaningful usage in those contexts.

Real-Time Learning and Adaptation: LLMs may evolve to incorporate real-time learning and adaptation capabilities, allowing them to continuously improve their language generation based on user feedback and changing contexts. This adaptive learning process would contribute to more meaningful and relevant outputs.

What are the potential risks or downsides of considering LLMs as meaningful language users, and how might these be mitigated?

While considering LLMs as meaningful language users offers numerous benefits, there are also potential risks and downsides that need to be addressed:

Bias and Misinformation: LLMs can perpetuate biases present in the training data and generate misinformation. Mitigation strategies include diverse and representative training data, bias detection algorithms, and human oversight.

Lack of Emotional Intelligence: LLMs may lack emotional intelligence and empathy, impacting the quality of interactions. This can be mitigated by incorporating sentiment analysis and emotional understanding into the models.

Privacy Concerns: LLMs may pose privacy risks by storing and processing sensitive information. Implementing robust data protection measures and encryption can help mitigate these concerns.

Security Vulnerabilities: LLMs can be vulnerable to adversarial attacks and manipulation. Robust security protocols, regular audits, and adversarial training can help enhance their resilience.

Overreliance on LLMs: Overreliance on LLMs for decision-making without human oversight can lead to errors and ethical dilemmas. Implementing clear guidelines for human intervention and verification can mitigate this risk.

Environmental Impact: The computational resources required to train and run LLMs can have a significant environmental impact. Developing energy-efficient models and utilizing sustainable computing practices can help reduce this impact.

In what ways could the insights from linguistic metasemantics inform the development of more advanced and capable language models in the future?

Insights from linguistic metasemantics can significantly inform the development of more advanced and capable language models in the future:

Semantic Understanding: By incorporating linguistic metasemantic theories, language models can better understand the semantic nuances and meanings of words, sentences, and utterances. This can lead to more accurate and contextually appropriate language generation.

Pragmatic Considerations: Understanding pragmatic aspects of language, such as implicatures and context-dependent meanings, can enhance the naturalness and effectiveness of language models in communication tasks.

Reference and Meaning: Insights from linguistic metasemantics can help language models better handle reference, ambiguity, and context-sensitivity in language processing. This can improve the accuracy and relevance of their outputs.

Language Evolution: By considering how linguistic conventions and practices evolve, language models can adapt to changes in language use over time. This adaptability can ensure that the models remain relevant and effective in diverse linguistic contexts.

Interpretation and Inference: Linguistic metasemantics can guide the development of language models in making accurate interpretations and inferences based on linguistic structures and meanings. This can enhance the models' ability to generate coherent and meaningful responses.

Ethical and Inclusive Language Generation: Incorporating insights from linguistic metasemantics can help language models generate more ethical, inclusive, and culturally sensitive language outputs. This can promote responsible and meaningful communication in diverse settings.

By leveraging the principles and frameworks of linguistic metasemantics, future language models can achieve higher levels of linguistic sophistication, accuracy, and meaningful language use in various applications and contexts.