Are LLMs ready to compete in creative writing skills with a top (rather than average) novelist? To provide an initial answer for this question, we have carried out a contest between Patricio Pron (an awarded novelist, considered one of the best of his generation) and GPT-4 (one of the top performing LLMs), in the spirit of AI-human duels such as DeepBlue vs Kasparov and AlphaGo vs Lee Sidol. We asked Pron and GPT-4 to provide thirty titles each, and then to write short stories for both their titles and their opponent’s. Then, we prepared an evaluation rubric inspired by Boden’s definition of creativity, and we collected several detailed expert assessments of the texts, provided by literature critics and scholars. The results of our experimentation indicate that LLMs are still far from challenging a top human creative writer. We also observed that GPT-4 writes more creatively using Pron’s titles than its own titles (which is an indication of the potential for human-machine co-creation). Additionally, we found that GPT-4 has a more creative writing style in English than in Spanish.

This paper presents a new web portal with information about the state of the art of natural language processing tasks in Spanish. It provides information about forums, competitions, tasks and datasets in Spanish, that would otherwise be spread in multiple articles and web sites. The portal consists of overview pages where information can be searched for and filtered by several criteria and individual pages with detailed information and hyperlinks to facilitate navigation. Information has been manually curated from publications that describe competitions and NLP tasks from 2013 until 2023 and will be updated as new tasks appear. A total of 185 tasks and 128 datasets from 94 competitions have been introduced.

In Ordinal Classification tasks, items have to be assigned to classes that have a relative ordering, such as “positive”, “neutral”, “negative” in sentiment analysis. Remarkably, the most popular evaluation metrics for ordinal classification tasks either ignore relevant information (for instance, precision/recall on each of the classes ignores their relative ordering) or assume additional information (for instance, Mean Average Error assumes absolute distances between classes). In this paper we propose a new metric for Ordinal Classification, Closeness Evaluation Measure, that is rooted on Measurement Theory and Information Theory. Our theoretical analysis and experimental results over both synthetic data and data from NLP shared tasks indicate that the proposed metric captures quality aspects from different traditional tasks simultaneously. In addition, it generalizes some popular classification (nominal scale) and error minimization (interval scale) metrics, depending on the measurement scale in which it is instantiated.

The importance of evaluation in promoting research and development in the information retrieval and natural language processing domains has long been recognised but is this sufficient? In many areas there is still a considerable gap between the results achieved by the research community and their implementation in commercial applications. This is particularly true for the cross-language or multilingual retrieval areas. Despite the strong demand for and interest in multilingual IR functionality, there are still very few operational systems on offer. The Cross Language Evaluation Forum (CLEF) is now taking steps aimed at changing this situation. The paper provides a critical assessment of the main results achieved by CLEF so far and discusses plans now underway to extend its activities in order to have a more direct impact on the application sector.

The objective of the Cross-Language Evaluation Forum (CLEF) is to promote research in the multilingual information access domain. In this short paper, we list the achievements of CLEF during its first four years of activity and describe how the range of tasks has been considerably expanded during this period. The aim of the paper is to demonstrate the importance of evaluation initiatives with respect to system research and development and to show how essential it is for such initiatives to keep abreast of and even anticipate the emerging needs of both system developers and application communities if they are to have a future.

We present a metagrammatical formalism, generic rules, to give a default interpretation to grammar rules. Our formalism introduces a process of dynamic binding interfacing the level of pure grammatical knowledge representation and the parsing level. We present an approach to non-constituent coordination within categorial grammars, and reformulate it as a generic rule. This reformulation is context-free parsable and reduces drastically the search space associated to the parsing task for such phenomena.