Building another Spanish dictionary, this time with GPT-4

Miguel Ortega-Martín, Óscar García-Sierra, Alfonso Ardoiz, Juan Carlos Armenteros, Ignacio Garrido, Jorge Álvarez, Camilo Torrón, Iñigo Galdeano, Ignacio Arranz, Oleg Vorontsov, Adrián Alonso·June 17, 2024

Summary

The paper presents Spanish Built Factual Freecianary 2.0 (Spani-BFF-2), an updated Spanish dictionary generated using GPT-4-turbo, which improves upon its predecessor by addressing limitations, expanding coverage, and incorporating part-of-speech tags. The study evaluates GPT-4-turbo's performance in generating definitions, comparing it to the Diccionario de la Lengua Española (DLE) and analyzing its strengths and weaknesses, particularly in handling monosemy and polysemy. While GPT-4-turbo generally provides accurate definitions, it struggles with polysemy and occasionally hallucinates. The dictionary has a higher precision for monosemy but lower recall for polysemous words, indicating room for improvement in capturing multiple meanings. The study also identifies issues with subword tokenization and error analysis, suggesting future work on refining the model's handling of rare and unconventional words, as well as responsible use of large language models in lexicography.

Key findings

7

Paper digest

What problem does the paper attempt to solve? Is this a new problem?

The paper aims to address the issue of undefined terms in GPT-4-turbo within the framework of Spanish-BFF-2, where roughly 15% of the vocabulary in DLE requires a corresponding definition in GPT-4-turbo . This problem is not entirely new, as it has been identified in previous versions like Spanish-BFF-1 . The paper focuses on improving the quality of definitions generated by GPT models, particularly in handling infrequent and unconventional words to enhance the lexicographic quality of the Spanish dictionary .


What scientific hypothesis does this paper seek to validate?

This paper aims to validate the scientific hypothesis related to the construction of a Spanish dictionary using GPT models, specifically focusing on the improvement from GPT-3 to GPT-4-turbo in generating definitions and examples for Spanish lemmas . The study evaluates the performance of the GPT-4-turbo model in creating a more intricate and comprehensive Spanish dictionary compared to its predecessor, GPT-3, by analyzing the quality and accuracy of the definitions produced . The research explores the advancements made in computational lexicography by utilizing Large Language Models (LLMs) like GPT-4-turbo to enhance the linguistic information provided in the dictionary, aiming to contribute to the field of natural language processing .


What new ideas, methods, or models does the paper propose? What are the characteristics and advantages compared to previous methods?

The paper "Building another Spanish dictionary, this time with GPT-4" introduces several new ideas, methods, and models in the realm of lexicography and Natural Language Processing (NLP) . Here are some key points from the paper:

  1. Introduction of Spanish-BFF-2: The paper presents the "Spanish Built Factual Freectianary 2.0" (Spanish-BFF-2) as the second iteration of an AI-generated Spanish dictionary, utilizing GPT-4-turbo . This version aims to improve upon the previous version created with GPT-3 by enhancing the dictionary's linguistic information and performance .

  2. Use of Large Language Models (LLMs): The study highlights the significance of Large Language Models, particularly GPT models, in NLP . These models, such as GPT-4-turbo, with 1.76 trillion parameters, demonstrate advanced generative capabilities . They play a crucial role in text-to-text tasks and have evolved to incorporate user intents for improved functionality .

  3. Enhancements in Definitions: The paper discusses improvements in the quality of definitions generated by GPT-4-turbo compared to GPT-3, such as a significant reduction in errors like definitions starting with the defined lemma . GPT-4-turbo shows enhanced lexicographic quality by addressing issues like definitions composed in English and nouns defined as verbs .

  4. Experimental Setup: The experimental setup involves curating a list of Spanish lemmas and submitting prompt-based queries to GPT-4-turbo for definitions and example sentences . The methodology includes handling batches of lemmas per query and utilizing a few-shot approach to guide the model's output .

  5. Qualitative and Quantitative Analysis: The paper conducts a qualitative and quantitative analysis of the generated dictionary, comparing it with previous versions and a trusted source like the "Diccionario de la Lengua Española" (DLE) . The analysis includes evaluating the definitions' quality, example sentences, and the coverage of monosemous and polysemous words .

In summary, the paper proposes advancements in computational lexicography by leveraging GPT-4-turbo to enhance the quality of definitions, improve the Spanish-BFF dictionary, and explore the potential of Large Language Models in generating comprehensive dictionaries . The paper "Building another Spanish dictionary, this time with GPT-4" introduces several characteristics and advantages compared to previous methods in computational lexicography and dictionary construction :

  1. Improved Lexicographic Quality: The study highlights a significant enhancement in the quality of definitions generated by GPT-4-turbo compared to its predecessor, GPT-3. Spanish-BFF-2, constructed with GPT-4-turbo, demonstrates a notable reduction in errors, such as definitions starting with the defined lemma, which was a recurring issue in GPT-3 but significantly improved in the new version .

  2. Enhanced Linguistic Information: The paper emphasizes that GPT-4-turbo delivers enhanced linguistic information, providing more accurate and refined definitions. The model showcases improved lexicographic quality by addressing errors like definitions composed in English and nouns defined as third-person forms of verbs, which have been resolved in Spanish-BFF-2 .

  3. Advanced Generative Capabilities: GPT-4-turbo, with its 1.76 trillion parameters, demonstrates advanced generative capabilities in producing definitions and example sentences for Spanish lemmas. The model's capacity to circumvent hallucinations, a common issue in contemporary NLP systems, is a significant advantage .

  4. Methodological Advancements: The experimental setup involves a curated list of Spanish lemmas, utilizing GPT-4-turbo to generate definitions and example sentences. The methodology includes a prompt-based query approach, handling batches of lemmas per query, and incorporating a few-shot strategy to guide the model's output, leading to the creation of a more robust and comprehensive Spanish dictionary .

  5. Qualitative and Quantitative Analysis: The paper conducts a thorough qualitative and quantitative analysis of the generated dictionary, comparing it with previous versions and a trusted source like the "Diccionario de la Lengua Española" (DLE). The analysis includes evaluating the quality of definitions, example sentences, and the coverage of monosemous and polysemous words, showcasing the advancements made in Spanish-BFF-2 .

In summary, the utilization of GPT-4-turbo in constructing the Spanish-BFF-2 dictionary results in improved lexicographic quality, enhanced linguistic information, advanced generative capabilities, methodological advancements, and a comprehensive qualitative and quantitative analysis, highlighting the progress made in computational lexicography and dictionary construction compared to previous methods.


Do any related researches exist? Who are the noteworthy researchers on this topic in this field?What is the key to the solution mentioned in the paper?

Several related research studies exist in the field of computational lexicography and natural language processing. Noteworthy researchers in this area include Miguel Ortega-Martin, Oscar García-Sierra, Alfonso Ardoiz, Juan Carlos Armenteros, Ignacio Garrido, Jorge Alvarez, Camilo Torrón, Iñigo Galdeano, Ignacio Arranz, Oleg Vorontsov, and Adrián Alonso . These researchers have contributed to the development of the "Spanish Built Factual Freectianary" (Spanish-BFF) dictionaries using advanced AI models like GPT-4-turbo.

The key solution mentioned in the paper involves the utilization of GPT-4-turbo, an advanced version of the GPT-4 model with 1.76 trillion parameters, to enhance the quality and comprehensiveness of the Spanish dictionary generated . The GPT-4-turbo model demonstrates improved performance in avoiding hallucinations and inaccuracies in defining words, addressing the challenge of undefined terms and infrequent nature of vocabulary . Additionally, the paper highlights the importance of leveraging Large Language Models (LLMs) for generating dictionaries and optimizing computational expenses in lexicographical work .


How were the experiments in the paper designed?

The experiments in the paper were designed as follows:

  • The paper is organized into different sections, with Section 4 outlining the experimental setup, followed by a detailed examination of the generated dictionary in Section 5 .
  • The curated list used for the experiments comprised 94,472 Spanish lemmas, considering polysemy and distinct parts of speech. The methodology involved running GPT-4-turbo and requesting example sentences for each lemma and category, with batches of 32 lemmas per query .
  • The prompt specified instructions for generating definitions and examples for each lemma and category, incorporating a few-shot approach by providing the model with examples of the expected output. The entire process of building the dictionary took approximately 90 hours .
  • The results and contrasts of the experiments were evaluated qualitatively and quantitatively. The qualitative analysis involved comparing the definitions and example sentences generated by GPT-4-turbo with the previous version, Spanish-BFF-1, to assess improvements. The quantitative analysis included parsing the output of GPT-4-turbo to compare definitions with a trusted source like "Diccionario de la Lengua Española" (DLE) .

What is the dataset used for quantitative evaluation? Is the code open source?

The dataset used for quantitative evaluation in the study of the Spanish dictionary built with GPT-4 is a curated list comprising 94,472 Spanish lemmas . The code for the dictionary, including the second iteration "Spanish Built Factual Freectianary 2.0" (Spanish-BFF-2), is open source and accessible on the Hugging Face hub and their GitHub repository .


Do the experiments and results in the paper provide good support for the scientific hypotheses that need to be verified? Please analyze.

The experiments and results presented in the paper provide substantial support for the scientific hypotheses that needed verification. The study focused on constructing a Spanish dictionary using GPT-4-turbo, aiming to enhance the dictionary's quality compared to its predecessor, GPT-3 . The analysis involved comparing the performance of both models, demonstrating the improved lexicographic quality of GPT-4-turbo definitions . Additionally, the study explored the limitations and future work of using GPT models in generating dictionaries, highlighting the need for further exploration of polysemous words .

The experimental setup involved manipulating a curated list of Spanish lemmas and running GPT-4-turbo queries to generate definitions and example sentences . The results indicated a qualitative and quantitative analysis of the generated dictionary, showcasing improvements in the succeeding GPT models . The study also evaluated the cosine similarity between Spanish-BFF-2 and DLE, providing insights into the precision, recall, and F1 scores for monosemy and polysemy definitions .

Overall, the detailed experimental methodology, results, and analysis presented in the paper offer strong empirical evidence to support the scientific hypotheses related to the construction and enhancement of a Spanish dictionary using GPT-4-turbo. The study's findings contribute to the advancement of computational lexicography and the utilization of Large Language Models in linguistic research .


What are the contributions of this paper?

The contributions of the paper "Building another Spanish dictionary, this time with GPT-4" are as follows:

  • The paper presents the construction of the second freely accessible AI-generated Spanish dictionary, which is the first of its kind in Spanish, utilizing GPT-4-turbo .
  • It evaluates the role of GPT-4 and GPT-3 models by conducting a comparative analysis of the two Spanish-BFF versions, showcasing the advancements made in the newer version .
  • The study aims to improve the dictionary by using GPT-4-turbo and explores enhancements made to the initial version, comparing the performance of both models .
  • The paper contributes to the field of computational lexicography by enhancing the quality and comprehensiveness of the Spanish dictionary generated with GPT-4-turbo, demonstrating superior performance and linguistic information delivery compared to its predecessor .
  • It highlights the improved lexicographic quality of GPT-4-turbo definitions compared to GPT-3, showcasing a reduction in errors and enhanced accuracy in defining words .

What work can be continued in depth?

Further work that can be continued in depth includes exploring the nature of words whose definitions are not known by GPT models. These words, although rare, warrant a comprehensive examination to enhance the completeness of the dictionary . Additionally, there is a need to address the limited generation of polysemous words by GPT models, ensuring a more thorough coverage of words with multiple meanings . This continued exploration can contribute to a better understanding of Natural Language Processing (NLP) and promote responsible use of language models .

Tables

1

Introduction
Background
Evolution of built factual dictionaries
GPT-4-turbo as a language model innovation
Objective
Improve upon Spani-BFF-1
Evaluate GPT-4-turbo's performance in lexicography
Address limitations and enhance coverage
Method
Data Collection
GPT-4-turbo generation process
Comparison dataset: Diccionario de la Lengua Española (DLE)
Data Preprocessing
Definition generation from GPT-4-turbo
Part-of-speech tagging and analysis
Performance Evaluation
Monosemy and polysemy analysis
Accuracy, precision, and recall metrics
Error Analysis
Subword tokenization issues
Hallucinations and unconventional word handling
Responsible Use in Lexicography
Large language model ethics
Future directions for refining the model
Results and Discussion
Strengths and weaknesses of GPT-4-turbo in Spanish definitions
Comparison with DLE: GPT-4-turbo's performance
Recommendations for model improvements
Conclusion
Summary of findings
Significance of Spani-BFF-2 for Spanish language resources
Implications for future language model applications in lexicography
Basic info
papers
computation and language
artificial intelligence
Advanced features
Insights
What are the specific challenges GPT-4-turbo faces in handling word meanings, as mentioned in the study?
How does Spani-BFF-2 differ from its predecessor in terms of improvements?
What is the primary focus of the paper about the Spanish dictionary?
What is the main evaluation method used to compare GPT-4-turbo with Diccionario de la Lengua Española (DLE)?

Building another Spanish dictionary, this time with GPT-4

Miguel Ortega-Martín, Óscar García-Sierra, Alfonso Ardoiz, Juan Carlos Armenteros, Ignacio Garrido, Jorge Álvarez, Camilo Torrón, Iñigo Galdeano, Ignacio Arranz, Oleg Vorontsov, Adrián Alonso·June 17, 2024

Summary

The paper presents Spanish Built Factual Freecianary 2.0 (Spani-BFF-2), an updated Spanish dictionary generated using GPT-4-turbo, which improves upon its predecessor by addressing limitations, expanding coverage, and incorporating part-of-speech tags. The study evaluates GPT-4-turbo's performance in generating definitions, comparing it to the Diccionario de la Lengua Española (DLE) and analyzing its strengths and weaknesses, particularly in handling monosemy and polysemy. While GPT-4-turbo generally provides accurate definitions, it struggles with polysemy and occasionally hallucinates. The dictionary has a higher precision for monosemy but lower recall for polysemous words, indicating room for improvement in capturing multiple meanings. The study also identifies issues with subword tokenization and error analysis, suggesting future work on refining the model's handling of rare and unconventional words, as well as responsible use of large language models in lexicography.
Mind map
Future directions for refining the model
Large language model ethics
Hallucinations and unconventional word handling
Subword tokenization issues
Accuracy, precision, and recall metrics
Monosemy and polysemy analysis
Part-of-speech tagging and analysis
Definition generation from GPT-4-turbo
Comparison dataset: Diccionario de la Lengua Española (DLE)
GPT-4-turbo generation process
Address limitations and enhance coverage
Evaluate GPT-4-turbo's performance in lexicography
Improve upon Spani-BFF-1
GPT-4-turbo as a language model innovation
Evolution of built factual dictionaries
Implications for future language model applications in lexicography
Significance of Spani-BFF-2 for Spanish language resources
Summary of findings
Recommendations for model improvements
Comparison with DLE: GPT-4-turbo's performance
Strengths and weaknesses of GPT-4-turbo in Spanish definitions
Responsible Use in Lexicography
Error Analysis
Performance Evaluation
Data Preprocessing
Data Collection
Objective
Background
Conclusion
Results and Discussion
Method
Introduction
Outline
Introduction
Background
Evolution of built factual dictionaries
GPT-4-turbo as a language model innovation
Objective
Improve upon Spani-BFF-1
Evaluate GPT-4-turbo's performance in lexicography
Address limitations and enhance coverage
Method
Data Collection
GPT-4-turbo generation process
Comparison dataset: Diccionario de la Lengua Española (DLE)
Data Preprocessing
Definition generation from GPT-4-turbo
Part-of-speech tagging and analysis
Performance Evaluation
Monosemy and polysemy analysis
Accuracy, precision, and recall metrics
Error Analysis
Subword tokenization issues
Hallucinations and unconventional word handling
Responsible Use in Lexicography
Large language model ethics
Future directions for refining the model
Results and Discussion
Strengths and weaknesses of GPT-4-turbo in Spanish definitions
Comparison with DLE: GPT-4-turbo's performance
Recommendations for model improvements
Conclusion
Summary of findings
Significance of Spani-BFF-2 for Spanish language resources
Implications for future language model applications in lexicography
Key findings
7

Paper digest

What problem does the paper attempt to solve? Is this a new problem?

The paper aims to address the issue of undefined terms in GPT-4-turbo within the framework of Spanish-BFF-2, where roughly 15% of the vocabulary in DLE requires a corresponding definition in GPT-4-turbo . This problem is not entirely new, as it has been identified in previous versions like Spanish-BFF-1 . The paper focuses on improving the quality of definitions generated by GPT models, particularly in handling infrequent and unconventional words to enhance the lexicographic quality of the Spanish dictionary .


What scientific hypothesis does this paper seek to validate?

This paper aims to validate the scientific hypothesis related to the construction of a Spanish dictionary using GPT models, specifically focusing on the improvement from GPT-3 to GPT-4-turbo in generating definitions and examples for Spanish lemmas . The study evaluates the performance of the GPT-4-turbo model in creating a more intricate and comprehensive Spanish dictionary compared to its predecessor, GPT-3, by analyzing the quality and accuracy of the definitions produced . The research explores the advancements made in computational lexicography by utilizing Large Language Models (LLMs) like GPT-4-turbo to enhance the linguistic information provided in the dictionary, aiming to contribute to the field of natural language processing .


What new ideas, methods, or models does the paper propose? What are the characteristics and advantages compared to previous methods?

The paper "Building another Spanish dictionary, this time with GPT-4" introduces several new ideas, methods, and models in the realm of lexicography and Natural Language Processing (NLP) . Here are some key points from the paper:

  1. Introduction of Spanish-BFF-2: The paper presents the "Spanish Built Factual Freectianary 2.0" (Spanish-BFF-2) as the second iteration of an AI-generated Spanish dictionary, utilizing GPT-4-turbo . This version aims to improve upon the previous version created with GPT-3 by enhancing the dictionary's linguistic information and performance .

  2. Use of Large Language Models (LLMs): The study highlights the significance of Large Language Models, particularly GPT models, in NLP . These models, such as GPT-4-turbo, with 1.76 trillion parameters, demonstrate advanced generative capabilities . They play a crucial role in text-to-text tasks and have evolved to incorporate user intents for improved functionality .

  3. Enhancements in Definitions: The paper discusses improvements in the quality of definitions generated by GPT-4-turbo compared to GPT-3, such as a significant reduction in errors like definitions starting with the defined lemma . GPT-4-turbo shows enhanced lexicographic quality by addressing issues like definitions composed in English and nouns defined as verbs .

  4. Experimental Setup: The experimental setup involves curating a list of Spanish lemmas and submitting prompt-based queries to GPT-4-turbo for definitions and example sentences . The methodology includes handling batches of lemmas per query and utilizing a few-shot approach to guide the model's output .

  5. Qualitative and Quantitative Analysis: The paper conducts a qualitative and quantitative analysis of the generated dictionary, comparing it with previous versions and a trusted source like the "Diccionario de la Lengua Española" (DLE) . The analysis includes evaluating the definitions' quality, example sentences, and the coverage of monosemous and polysemous words .

In summary, the paper proposes advancements in computational lexicography by leveraging GPT-4-turbo to enhance the quality of definitions, improve the Spanish-BFF dictionary, and explore the potential of Large Language Models in generating comprehensive dictionaries . The paper "Building another Spanish dictionary, this time with GPT-4" introduces several characteristics and advantages compared to previous methods in computational lexicography and dictionary construction :

  1. Improved Lexicographic Quality: The study highlights a significant enhancement in the quality of definitions generated by GPT-4-turbo compared to its predecessor, GPT-3. Spanish-BFF-2, constructed with GPT-4-turbo, demonstrates a notable reduction in errors, such as definitions starting with the defined lemma, which was a recurring issue in GPT-3 but significantly improved in the new version .

  2. Enhanced Linguistic Information: The paper emphasizes that GPT-4-turbo delivers enhanced linguistic information, providing more accurate and refined definitions. The model showcases improved lexicographic quality by addressing errors like definitions composed in English and nouns defined as third-person forms of verbs, which have been resolved in Spanish-BFF-2 .

  3. Advanced Generative Capabilities: GPT-4-turbo, with its 1.76 trillion parameters, demonstrates advanced generative capabilities in producing definitions and example sentences for Spanish lemmas. The model's capacity to circumvent hallucinations, a common issue in contemporary NLP systems, is a significant advantage .

  4. Methodological Advancements: The experimental setup involves a curated list of Spanish lemmas, utilizing GPT-4-turbo to generate definitions and example sentences. The methodology includes a prompt-based query approach, handling batches of lemmas per query, and incorporating a few-shot strategy to guide the model's output, leading to the creation of a more robust and comprehensive Spanish dictionary .

  5. Qualitative and Quantitative Analysis: The paper conducts a thorough qualitative and quantitative analysis of the generated dictionary, comparing it with previous versions and a trusted source like the "Diccionario de la Lengua Española" (DLE). The analysis includes evaluating the quality of definitions, example sentences, and the coverage of monosemous and polysemous words, showcasing the advancements made in Spanish-BFF-2 .

In summary, the utilization of GPT-4-turbo in constructing the Spanish-BFF-2 dictionary results in improved lexicographic quality, enhanced linguistic information, advanced generative capabilities, methodological advancements, and a comprehensive qualitative and quantitative analysis, highlighting the progress made in computational lexicography and dictionary construction compared to previous methods.


Do any related researches exist? Who are the noteworthy researchers on this topic in this field?What is the key to the solution mentioned in the paper?

Several related research studies exist in the field of computational lexicography and natural language processing. Noteworthy researchers in this area include Miguel Ortega-Martin, Oscar García-Sierra, Alfonso Ardoiz, Juan Carlos Armenteros, Ignacio Garrido, Jorge Alvarez, Camilo Torrón, Iñigo Galdeano, Ignacio Arranz, Oleg Vorontsov, and Adrián Alonso . These researchers have contributed to the development of the "Spanish Built Factual Freectianary" (Spanish-BFF) dictionaries using advanced AI models like GPT-4-turbo.

The key solution mentioned in the paper involves the utilization of GPT-4-turbo, an advanced version of the GPT-4 model with 1.76 trillion parameters, to enhance the quality and comprehensiveness of the Spanish dictionary generated . The GPT-4-turbo model demonstrates improved performance in avoiding hallucinations and inaccuracies in defining words, addressing the challenge of undefined terms and infrequent nature of vocabulary . Additionally, the paper highlights the importance of leveraging Large Language Models (LLMs) for generating dictionaries and optimizing computational expenses in lexicographical work .


How were the experiments in the paper designed?

The experiments in the paper were designed as follows:

  • The paper is organized into different sections, with Section 4 outlining the experimental setup, followed by a detailed examination of the generated dictionary in Section 5 .
  • The curated list used for the experiments comprised 94,472 Spanish lemmas, considering polysemy and distinct parts of speech. The methodology involved running GPT-4-turbo and requesting example sentences for each lemma and category, with batches of 32 lemmas per query .
  • The prompt specified instructions for generating definitions and examples for each lemma and category, incorporating a few-shot approach by providing the model with examples of the expected output. The entire process of building the dictionary took approximately 90 hours .
  • The results and contrasts of the experiments were evaluated qualitatively and quantitatively. The qualitative analysis involved comparing the definitions and example sentences generated by GPT-4-turbo with the previous version, Spanish-BFF-1, to assess improvements. The quantitative analysis included parsing the output of GPT-4-turbo to compare definitions with a trusted source like "Diccionario de la Lengua Española" (DLE) .

What is the dataset used for quantitative evaluation? Is the code open source?

The dataset used for quantitative evaluation in the study of the Spanish dictionary built with GPT-4 is a curated list comprising 94,472 Spanish lemmas . The code for the dictionary, including the second iteration "Spanish Built Factual Freectianary 2.0" (Spanish-BFF-2), is open source and accessible on the Hugging Face hub and their GitHub repository .


Do the experiments and results in the paper provide good support for the scientific hypotheses that need to be verified? Please analyze.

The experiments and results presented in the paper provide substantial support for the scientific hypotheses that needed verification. The study focused on constructing a Spanish dictionary using GPT-4-turbo, aiming to enhance the dictionary's quality compared to its predecessor, GPT-3 . The analysis involved comparing the performance of both models, demonstrating the improved lexicographic quality of GPT-4-turbo definitions . Additionally, the study explored the limitations and future work of using GPT models in generating dictionaries, highlighting the need for further exploration of polysemous words .

The experimental setup involved manipulating a curated list of Spanish lemmas and running GPT-4-turbo queries to generate definitions and example sentences . The results indicated a qualitative and quantitative analysis of the generated dictionary, showcasing improvements in the succeeding GPT models . The study also evaluated the cosine similarity between Spanish-BFF-2 and DLE, providing insights into the precision, recall, and F1 scores for monosemy and polysemy definitions .

Overall, the detailed experimental methodology, results, and analysis presented in the paper offer strong empirical evidence to support the scientific hypotheses related to the construction and enhancement of a Spanish dictionary using GPT-4-turbo. The study's findings contribute to the advancement of computational lexicography and the utilization of Large Language Models in linguistic research .


What are the contributions of this paper?

The contributions of the paper "Building another Spanish dictionary, this time with GPT-4" are as follows:

  • The paper presents the construction of the second freely accessible AI-generated Spanish dictionary, which is the first of its kind in Spanish, utilizing GPT-4-turbo .
  • It evaluates the role of GPT-4 and GPT-3 models by conducting a comparative analysis of the two Spanish-BFF versions, showcasing the advancements made in the newer version .
  • The study aims to improve the dictionary by using GPT-4-turbo and explores enhancements made to the initial version, comparing the performance of both models .
  • The paper contributes to the field of computational lexicography by enhancing the quality and comprehensiveness of the Spanish dictionary generated with GPT-4-turbo, demonstrating superior performance and linguistic information delivery compared to its predecessor .
  • It highlights the improved lexicographic quality of GPT-4-turbo definitions compared to GPT-3, showcasing a reduction in errors and enhanced accuracy in defining words .

What work can be continued in depth?

Further work that can be continued in depth includes exploring the nature of words whose definitions are not known by GPT models. These words, although rare, warrant a comprehensive examination to enhance the completeness of the dictionary . Additionally, there is a need to address the limited generation of polysemous words by GPT models, ensuring a more thorough coverage of words with multiple meanings . This continued exploration can contribute to a better understanding of Natural Language Processing (NLP) and promote responsible use of language models .

Tables
1
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