Creativity has become a core competence in the era of LLMs and human–AI collaboration, underpinning innovation in real-world problem solving. Crucially, the systematic improvement of creativity necessitates scientifically valid assessment instruments. Psychometric research recognizes context-based assessment as an effective way to measure creative thinking. However, high-quality expert-designed contexts remain scarce. Existing LLM-based generators often struggle with insufficient assessment cues, weak narrative coherence, limited stylistic diversity, and poor support for creative thinking. To address these challenges, we propose AlphaContext, an evolutionary tree-based psychometric context generator for creativity assessment. First, the HyperTree Outline Planner formalizes expert-designed outlining as a rule-guided hypertree and performs top-down hierarchical planning. The MCTS-based Context Generator fills the outline via MCTS to balance global structure and local quality. Then, the Evolutionary Context Optimizer evolves contexts with MAP-Elites by repeatedly updating niche elites to jointly improve diversity and quality. Finally, the Assessment-Guided Evolution Refiner simulates virtual participants with diverse styles and recycles weak contexts for further evolution. Experiments show that AlphaContext yields an average improvement of 8% over competitive methods across 6 quality metrics.

Post-editing (PE) refers to checking, proofreading, and revising the translation output of any automated translation (Gouadec, 2007, p. 25). It is needed because the meaning of a text can yet be accurately and fluently conveyed by machine translation (MT). The importance of PE and, accordingly, PE training has been widely acknowledged, and specialised courses have recently been introduced across universities and other organisations worldwide. However, scant consideration is given to when PE skills should be introduced in translation training. PE courses are usually offered to advanced translation learners, i.e., those at the postgraduate level or in the last year of an undergraduate program. Also, existing empirical studies most often investigate the impact of MT on postgraduate students or undergraduate students in the last year of their study. This paper reports on a study that aims to determine the possible effects of MT and PE on the translation quality of students at the early stage of translator training, i.e., undergraduate translation students with only basic translation knowledge. Methodologically, an experiment was conducted to compare students’ (n=10) PEMT-based translations and from-scratch translations without the assistance of machine translation. Second-year students of an undergraduate translation programme were invited to translate two English texts with similar difficulties into Chinese. One of the texts was translated directly, while the other one was done with reference to machine-generated translation. Translation quality can be dynamic. When examined from different perspectives using different methods, the quality of a translation can vary. Several methods of translation quality assessment were adopted in this project, including rubrics-based scoring, error analysis and fixed-point translation analysis. It was found that the quality of students’ PE translations was compromised compared with that of from-scratch translations. In addition, errors were more homogenised in the PEMT-based translations. It is hoped that this study can shed some light on the role of PEMT in translator training and contribute to the curricula and course design of post-editing for translator education. Reference: Gouadec, D. (2007). Translation as a Profession. John Benjamins Publishing. Keywords: machine translation, post-editing, translator training, translation quality assessment, error analysis, undergraduate students

Most downstream adaptation methods tune all or part of the parameters of pre-trained models (PTMs) through gradient descent, where the tuning cost increases linearly with the growth of the model size.By contrast, gradient-free methods only require the forward computation of the PTM to tune the prompt, retaining the benefits of efficient tuning and deployment.Though, past work on gradient-free tuning often introduces gradient descent to seek a good initialization of prompt and lacks versatility across tasks and PTMs.In this paper, we present BBTv2, an improved version of Black-Box Tuning, to drive PTMs for few-shot learning.We prepend continuous prompts to every layer of the PTM and propose a divide-and-conquer gradient-free algorithm to optimize the prompts at different layers alternately.Extensive experiments across various tasks and PTMs show that BBTv2 can achieve comparable performance to full model tuning and state-of-the-art parameter-efficient methods (e.g., Adapter, LoRA, BitFit, etc.) under few-shot settings while maintaining much fewer tunable parameters.