NLP systems don't know how to say no

Pré-texto: N?o entendeu nada do que eu escrevi, basta ouvir: https://www.youtube.com/watch?v=-J5gTpiI3KU

Machine learning models, by their nature, lack "consciousness" or a subjective understanding of the world. This means they do not have the intrinsic ability to say "no" or make decisions based on ethics, morality, or personal judgment. These limitations arise from their design, which is grounded in mathematical and statistical correlations derived from large volumes of training data. In particular, large language models (LLMs) are designed to predict and generate text based on observed patterns, but their responses reflect a focus on coherence and plausibility rather than absolute truth or deep understanding.

The fabrication of responses in NLP (Natural Language Processing) systems, especially in LLMs, is a side effect of their focus on maximizing the probability of word sequences. Although impressive in tasks such as answering questions, drafting text, and conducting dialogue, these models often produce responses that seem convincing but may be incorrect, inaccurate, or entirely fabricated. This "invention" occurs because models lack inherent access to verifiable databases during text generation. Instead, they operate probabilistically, prioritizing fluency and coherence over factual accuracy.

Machine learning models process data by correlating inputs and outputs. They calculate the most probable response from previously provided examples, without understanding the meaning or intentionality of the information. This explains why they cannot autonomously or appropriately "say no." For example, a model trained to detect financial fraud merely returns a probability of fraud without the capacity to deny a request. Similarly, virtual assistants may appear to decline requests, but this happens because they are programmed to "simulate" refusals based on predefined response patterns.

Saying "no" often requires deeper contextual judgment beyond the patterns in training data. The lack of this ability can lead to inadequate or misleading decisions. In more complex scenarios, such as medical diagnoses or risk analyses, the absence of explicit denial increases the risk of severe errors. LLMs, in particular, are designed to respond to any input, even when the response should be a refusal or "I don't know." This behavior reflects a combination of design objectives, which prioritize utility and completeness, and inherent limitations in the absence of semantic understanding.

Response fabrication in these systems occurs due to several factors. First, models are trained on large volumes of data, learning co-occurrence patterns without factual validation during text generation. Additionally, their architecture prioritizes producing text that is fluent and grammatically correct, which can mask the fabricated nature of a response. Lastly, the lack of intrinsic connections to reliable databases during processing allows these models to create plausible but unverifiable information.

This limitation becomes particularly relevant in analyzing complex phenomena, such as the functioning of the human brain. The brain is an incredibly sophisticated system, composed of billions of interconnected neurons capable of dynamic interactions across multiple temporal and spatial scales. Simplistic models attempting to interpret cerebral complexity may make basic errors, such as assuming linearity in nonlinear processes, ignoring contextual variables, or reducing complex phenomena to one-dimensional metrics. These errors can hinder scientific understanding and negatively impact practical applications, such as the development of therapies or brain-machine interfaces.

Analyzing brain data is challenged by factors such as high data volumes, biological and mechanical noise, and the temporal complexity of neural processes. Even with advanced machine learning tools like deep neural networks, capturing the brain's functional and structural coherence remains a challenge. These models often lack interpretive clarity and generalization, making it difficult to extrapolate findings across individuals.

To address these limitations, it is essential to invest in robust data collection and integrate analyses across different temporal and spatial scales. Additionally, combining interdisciplinary expertise in neuroscience, statistics, and data science can help interpret data more holistically and reliably. Similarly, in NLP and LLM systems, incorporating human feedback, factual validation, and ethical training can reduce response fabrication and improve reliability.

In summary, while NLP and LLM systems are powerful tools, their inability to "say no" or refuse when they lack sufficient data highlights their fundamental limitations. Understanding these limitations is crucial to avoiding errors, enhancing accuracy, and developing safer and more responsible applications, whether in brain research or other fields of inquiry.