# Grandes modelos de lenguaje: de la predicci'on de palabras a la comprensi'on?

arXiv: https://arxiv.org/abs/2502.18205 · HTML: https://mlsanity.com/paper/2502.18205/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 32 of its 57 references that are in the map.
- First posted on arXiv 25 Feb 2025.

## Builds on (most cited first, 30 of 32)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [Mamba: Linear-Time Sequence Modeling with Selective State Spaces](https://mlsanity.com/paper/2312.00752/) (2023)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [On the Opportunities and Risks of Foundation Models](https://mlsanity.com/paper/2108.07258/) (2021)
- [Emergent Abilities of Large Language Models](https://mlsanity.com/paper/2206.07682/) (2022)
- [Survey of Hallucination in Natural Language Generation](https://mlsanity.com/paper/2202.03629/) (2022)
- [BLOOM: A 176B-Parameter Open-Access Multilingual Language Model](https://mlsanity.com/paper/2211.05100/) (2022)
- [Towards Reasoning in Large Language Models: A Survey](https://mlsanity.com/paper/2212.10403/) (2022)
- [Are Emergent Abilities of Large Language Models a Mirage?](https://mlsanity.com/paper/2304.15004/) (2023)
- [Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task](https://mlsanity.com/paper/2210.13382/) (2022)
- [Hallucination is Inevitable: An Innate Limitation of Large Language Models](https://mlsanity.com/paper/2401.11817/) (2024)
- [Implicit Representations of Meaning in Neural Language Models](https://mlsanity.com/paper/2106.00737/) (2021)
- [Art or Artifice? Large Language Models and the False Promise of Creativity](https://mlsanity.com/paper/2309.14556/) (2023)
- [Towards Making the Most of ChatGPT for Machine Translation](https://mlsanity.com/paper/2303.13780/) (2023)
- [A Confederacy of Models: a Comprehensive Evaluation of LLMs on Creative Writing](https://mlsanity.com/paper/2310.08433/) (2023)
- [Is ChatGPT a Highly Fluent Grammatical Error Correction System? A Comprehensive Evaluation](https://mlsanity.com/paper/2304.01746/) (2023)
- [Understanding Emergent Abilities of Language Models from the Loss Perspective](https://mlsanity.com/paper/2403.15796/) (2024)
- [Is Temperature the Creativity Parameter of Large Language Models?](https://mlsanity.com/paper/2405.00492/) (2024)
- [LLMs Will Always Hallucinate, and We Need to Live With This](https://mlsanity.com/paper/2409.05746/) (2024)
- [Could a Large Language Model be Conscious?](https://mlsanity.com/paper/2303.07103/) (2023)
- [Datasets for Large Language Models: A Comprehensive Survey](https://mlsanity.com/paper/2402.18041/) (2024)
- [Summarization is (Almost) Dead](https://mlsanity.com/paper/2309.09558/) (2023)
- [Pron vs Prompt: Can Large Language Models already Challenge a World-Class Fiction Author at Creative Text Writing?](https://mlsanity.com/paper/2407.01119/) (2024)
- [Language Models Do Hard Arithmetic Tasks Easily and Hardly Do Easy Arithmetic Tasks](https://mlsanity.com/paper/2406.02356/) (2024)

## Abstract (arXiv)

Large language models, such as the well-known ChatGPT, have brought about an unexpected revolution in the field of artificial intelligence. On the one hand, they have numerous practical applications and enormous potential still to be explored. On the other hand, they are also the subject of debate from scientific, philosophical, and social perspectives: there are doubts about the exact mechanisms of their functioning and their actual capacity for language comprehension, and their applications raise ethical dilemmas. In this chapter, we describe how this technology has been developed and the fundamentals of its operation, allowing us to better understand its capabilities and limitations and to introduce some of the main debates surrounding its development and use. -- Los grandes modelos de lenguaje, como el conocido ChatGPT, han supuesto una inesperada revoluci'on en el 'ambito de la inteligencia artificial. Por un lado, cuentan con multitud de aplicaciones pr'acticas y un enorme potencial todav'ia por explorar. Por otro lado, son tambi'en objeto de debate, tanto desde el punto de vista cient'ifico y filos'ofico como social: hay dudas sobre los mecanismos exactos de su funcionamiento y su capacidad real de comprensi'on del lenguaje, y sus aplicaciones plantean dilemas 'eticos. En este cap'itulo describimos c'omo se ha llegado a esta tecnolog'ia y los fundamentos de su funcionamiento, permiti'endonos as'i comprender mejor sus capacidades y limitaciones e introducir algunos de los principales debates que rodean su desarrollo y uso.