Emergent Communication and Learning Pressures in Language Models: A Language Evolution Perspective

Concetti Chiave

Language models can benefit from understanding emergent communication to improve language acquisition research.

Sintesi

The abstract highlights the importance of finding commonalities between language models and humans for breakthroughs in language evolution.
Neural language models have influenced linguistic theories, showcasing a thriving relationship between machine learning and linguistics.
Emergent communication literature excels at designing models to recover linguistic phenomena absent in natural languages.
Key pressures identified include communicative success, efficiency, learnability, and psycho-/sociolinguistic factors.
The content discusses mismatches between neural agents and humans regarding Zipf's law of abbreviation, compositional structure benefits, and group size effects.
Resolutions are provided for these mismatches by introducing inductive biases like laziness, impatience, and population heterogeneity.
Various cognitive and psycholinguistic factors affecting language evolution are explored along with pressures for successful communication, efficient communication, learnability, and memory constraints.

Statistiche

"The unprecedented success of language models in recent years provides many opportunities to further advance our understanding of human language learning." - Bahdanau et al., 2015; Brown et al., 2020; Devlin et al., 2019; Raffel et al., 2020; Vaswani et al., 2017

Citazioni

"The emergence of new communication systems is similarly studied using deep neural network models."
"Compositional structure is considered a hallmark feature of human language."
"In general, while emergent communication simulations are tuned for communicative success by design..."

Approfondimenti chiave tratti da

Emergent communication and learning pressures in language models

by Lukas Galke,... alle arxiv.org 03-22-2024

https://arxiv.org/pdf/2403.14427.pdf

Domande più approfondite

How do the cognitive biases present naturally differ from those that need to be artificially introduced?

In the context of language evolution and emergent communication in neural networks, cognitive biases that are present naturally refer to inherent learning pressures or constraints that drive language acquisition and evolution. These include pressures for successful communication, efficient communication, learnability, and other psycho-/sociolinguistic factors. For example, the pressure for successful communication is essential for distinguishing between various meanings during interaction. On the other hand, cognitive biases that need to be artificially introduced are additional constraints imposed on the learning system to promote desired behaviors. These artificial biases may include penalties for message length (to encourage efficiency) or resetting parameters periodically (to enhance compositional structure). The key difference lies in how natural cognitive biases align with human-like behavior while artificial biases are tailored specifically to mimic certain aspects of human language learning.

What implications do memory constraints have on the development of neural agents compared to humans?

Memory constraints play a crucial role in shaping language development and evolution by influencing processes such as transmission, learning dynamics, convergence on shared languages within communities, and linguistic diversity. In humans, limited memory capacity leads to preferences for more structured and less variable languages in larger populations due to challenges in maintaining unique communication protocols with different partners over time. However, neural agents typically lack memory constraints because they are often heavily over-parametrized models with no difficulty storing large amounts of information efficiently. This absence of memory limitations can impact how neural agents converge on shared languages or maintain idiolects across interactions compared to humans who face challenges related to memory retention during linguistic exchanges.

How can large language models be fine-tuned to optimize for communicative success rather than utterance completion?

To optimize large language models like GPT-3 for communicative success instead of just utterance completion during fine-tuning stages after pre-training:

Modify training objectives: Adjust optimization goals towards achieving successful interaction outcomes rather than solely predicting words from context.
Incorporate feedback mechanisms: Implement reward-based systems where models receive feedback based on human ratings or preferences regarding effective communication.
Introduce efficiency bias: Include penalties or incentives within training data that encourage efficient messaging strategies aligned with human-like behavior.
Explore iterative learning paradigms: Utilize approaches similar to iterated learning where models continually refine their understanding through repeated exposure and adaptation based on performance metrics related directly to successful communication outcomes.

By integrating these strategies into the fine-tuning process of large language models post-pre-training phases can help shift their focus towards optimizing for communicative success akin more closely resembling human-like linguistic interactions rather than mere utterance completion tasks alone.