Consideration of prompts as a neglected factor in research on evaluating ChatGPT’s translation performance

2.1 Studies on prompts

With the rapid rise of ChatGPT, studies on prompts have become an emerging research area (Muktadir 2023). Researchers have been exploring prompting strategies to unlock the full potential of ChatGPT, both for general purposes and specific tasks such as translation. These studies have demonstrated that well-designed prompts can significantly influence ChatGPT’s performance, especially in the context of machine translation, which is the primary focus of this study.

In terms of general prompting tactics, researchers have identified methods that can enhance ChatGPT’s performance across a wide range of tasks. These include few-shot prompting (Brown et al. 2020), which improves the model’s performance by providing examples that help establish context and direct the model to imitate desired styles and conventions; chain-of-thought prompting (Kojima et al. 2022; Wei et al. 2022), which boosts the model’s ability to handle complex tasks by enhancing its reasoning capabilities; and role-play prompting (Njifenjou et al. 2024), which guides the model’s behavior by assigning it specific roles or personas through tailored instructions. OpenAI (2023), the developer of ChatGPT, has further consolidated these findings in its Prompt Engineering Guide, which outlines six key strategies and detailed tactics for optimizing ChatGPT’s performance. In addition, several frameworks, such as the CAST model (Jacobs and Fisher 2023), the CLEAR model (Lo 2023), and the TRUST model (Trust 2023), have been proposed to combine different prompting techniques, offering structured approaches to prompt engineering, thus further advancing the field.

In the specific context of translation, a series of studies have explored and tested various prompting methods to enhance ChatGPT’s overall translation capabilities, or to address particular challenges in machine translation. Some researchers applied standard, general prompting strategies to translation tasks, while others designed more customized prompts specifically for translation, building upon foundational prompting strategies. For example, Yamada (2023) and He (2024) investigated the effectiveness of incorporating background information about translation tasks in the prompts, including the purpose, target audience, and the personas of “translator” and “author”. Gu (2023) as well as Prahallad and Mamidi (2024) successfully employed prompts with clearly articulated, step-by-step instructions to address long-standing challenges in Japanese-Chinese and English-Dravidian machine translation. In a similar vein, Jiao et al. (2023) introduced pivot prompting, a method for translating distant languages, which asks ChatGPT to translate the source sentence into a high-resource pivot language before into the target language, thereby breaking down the translation task into more manageable steps. Additionally, Jiao et al. (2024) combined various prompting strategies to propose a gradable prompting taxonomy using prompts containing expression type, translation style, part-of-speech information and explicit instructions, aiming to facilitate development of prompts tailored for various translation tasks.

These explorations not only have significantly enriched the toolkit for translation practitioners and researchers, but also have demonstrated the significant impact of prompting strategies on ChatGPT’s translation performance. However, while many researchers have pointed out that prompts are insufficiently utilized in the evaluation of ChatGPT’s translation capabilities (which is the premise of their own studies), they typically focused on proposing new prompting methods rather than analyzing how prompts were used in existing evaluations of ChatGPT’s translation performance.

2.2 ChatGPT-related translation studies

ChatGPT, though not specifically designed for translation, has demonstrated extraordinary translation abilities and in essence has become an emerging translation tool. As a result, ChatGPT-related translation studies have gained momentum. Current research primarily falls into two categories: theoretical explorations and empirical studies.

Theoretical explorations usually take a more abstract or higher-level perspective, focusing on assessing the potential impact and challenges raised by ChatGPT or, more broadly, by generative AI and large language models on various aspects of translation, including translation studies (Hu and Li 2023; Yu and Liu 2024), translation education (Deng and Liu 2024), translation practice (Zhong 2024), translation systems (Gao and Ren 2023), translation ethics (Wu and Chen 2023; Yu and Guo 2024), and the translation industry (Cui 2025). These studies also provide recommendations to address these challenges.

Empirical studies, on the other hand, apply ChatGPT to specific translation tasks and evaluate its performance and features, and they are the focus of this study’s analysis. Some researchers examined the effectiveness of generative AI in translating different types of text, such as Chinese discourse (Wen and Tian 2024) and subtitles (Calvo-Ferrer 2023), revealing both its strengths and weaknesses. Others have explored the capabilities of ChatGPT in translating specific languages, including low-resource languages of the Belt and Road Initiative countries (Hou et al. 2024), Classical Latin, Ancient Greek, and Classical Sanskrit (Ross 2023), and Middle Polish (Klamra et al. 2023). Meanwhile, Yu (2024) analyzed the lexical diversity and syntactic complexity of ChatGPT translations using news texts, providing new insights into its translation capabilities.

In summary, while existing ChatGPT-related translation studies have examined its influence and effectiveness in various tasks, there remains a noticeable gap in systematically assessing how prompts were employed and evaluated in current research. This gap calls for further investigation into the adequacy and effectiveness of prompt usage, which this study aims to address.

3.1 Paper collection and selection

Studies focusing on evaluating the translation performance of ChatGPT, or containing such evaluation as a major part were eligible, regardless of their discipline, evaluation method or outcome. Measures were taken during the selection process to ensure the inclusion of high-quality papers, which will be detailed below.

The papers were collected following the four phases of the PRISMA methodology: (1) identification, (2) screening, (3) eligibility, and (4) inclusion (Moher et al. 2009). The entire process is documented in Figure 1. The following paragraphs provide additional clarification for each step of the process.

1. Identification: Relevant literature was searched across two databases – WOS and CNKI – to include studies around the world. For WOS, the search scope was the Web of Science Core Collection (all editions), using the search string “Topic = ‘ChatGPT’ AND ‘translat*’”, with a wildcard character to capture different forms of the word “translate”. For CNKI, the search scope included CSSCI, Peking University Core Journals, and CSCD databases, which are considered high-quality sources. The search string used was “Topic = ‘ChatGPT’ AND ‘翻译^[1]’”. Additionally, a full-text search was conducted in CNKI,^[2] with the search string “Topic = ‘大语言模型^[3]’ + ‘AI’ AND ‘翻译’, Full text = ‘ChatGPT’” in case relevant papers used general terms like “large language model” or “AI” rather than “ChatGPT” in the topic but actually evaluated ChatGPT’s translation performance. The initial search, conducted on June 12, 2024, generated a total of 232 records (CNKI: 88; WOS: 144). 25 duplicate articles were removed before screening, leaving 207 to be screened.

2. Screening: Titles and abstracts of the literature were screened to assess their relevance, resulting in the removal of 170 records as shown in Figure 1. Meanwhile, an additional 13 records were identified through backward citation, as they were highly referenced by relevant and high-quality papers. This led to a total of 50 records for further analysis.

3. Eligibility: Full texts of the 50 records were retrieved and assessed to determine their eligibility. 18 records were excluded at this stage.

4. Inclusion: Ultimately, 32 studies were included for final analysis.

Figure 1:

PRISMA flow diagram.

3.2 Evaluation criteria

From the review and analysis in previous sections, it is evident that prompts are a critical variable influencing the performance of ChatGPT. Therefore, the use of prompts in evaluation of ChatGPT’s translation performance must adhere to well-established principles of experimental design to ensure meaningful, reproducible, and interpretable results. Drawing on foundational works in experimental methodology (Montgomery 2017; Seltman 2018), several criteria are proposed for assessing the consideration of prompts in existing studies:

1. Identification and articulation of variables. Researchers should clearly identify prompts as a key experimental variable and explicitly explain their potential influence on the research outcome. This transparency is essential for contextualizing findings and elucidating the role prompts play in shaping the results.

2. Explicit design and justification of prompts. Studies should provide detailed descriptions of the prompt design and justify the rationale behind their selection. This ensures the audience’s understanding of the experimental process, facilitates replication, and aligns with the principle of transparency in scientific research.

3. Alignment of prompt design with research objectives. The chosen prompts should be suitable for the specific research objectives, with appropriate prompting strategies applied. This alignment ensures that the experimental setup effectively addresses the research questions and minimizes confounding factors.

4. Integration of prompt influence in findings. The influence of prompts should be explicitly analyzed and reported as part of the study’s conclusions. This inclusion aligns with the principle of comprehensive reporting, which enhances the validity and reliability of the findings.

These criteria ensure the rigor and trustworthiness of an evaluation, and will serve as the foundation for the analysis in this study. To operationalize these principles, a set of assessment questions were formulated as listed in Table 1, drawing on the works of Hemkens et al. (2018) and Munkholm et al. (2020) who used standardized questions to assess whether a given factor (confounding bias) that was supposed to have impact on the research results had been adequately considered in existing studies. These questions were designed in accordance to the criteria above, addressing different aspects of prompt consideration and providing a structured framework for evaluating. Each question is accompanied by illustrative examples drawn from the studies under review.

Two key points warrant further clarification:

First, Question 1 serves as a threshold to determine whether a study meets the minimum level of prompt consideration. It might seem basic but is necessary as a starting point. Naturally, studies that address the aspects covered in Question 2 to 5 will also receive an affirmative answer to Question 1, though the reverse is not necessarily true. Notably, these questions are not meant to be mutually exclusive or strictly parallel. Taken together, they provide a whole picture of prompt consideration in the studies reviewed, while each question individually serves as an important indicator in its own right.

Secondly, while Questions 1 to 5 collectively assess the overall degree of prompt consideration, Question 4 differs from the others. It specifically examines how prompts were utilized, which is a key aspect of this study, and includes 2 sub-questions: one addressing how researchers justified their selection of prompts, and the other exploring how prompting techniques were applied to meet research objectives. The analysis in the following sections will use the five main questions as indicators of overall prompt consideration, and focus on Question 4 to examine prompt usage in greater detail.

3.3 Data analysis

First, to examine how existing studies have considered prompts, each paper was systematically analyzed using the set of questions outlined in Table 1. The results were analyzed from two perspectives: (1) the overall level of prompt consideration, measured by the coverage of the five primary questions in Table 1; and (2) the specification and use of prompts, which were analyzed by focusing specifically on how Question 4, along with its two sub-questions, was addressed.

The second part of the analysis focuses on disciplinary differences in prompt consideration. For the purpose of this analysis, disciplines refer to the categories under which journals, preprint platforms, or conferences classify articles. This part starts by summarizing the distribution of examined papers across different disciplines, then compares the overall level of prompt consideration within the identified disciplines, and finally investigates how the specification and use of prompts differ across them.

Based on the data analysis, Section 5 will summarize the key findings from the analysis of the existing research and offer recommendations to improve research rigor, in response to the third research question.

4.1 Overall patterns of prompt consideration

4.1.1 Overall level of prompt consideration

Figure 2 presents the overall level of prompt consideration in the 32 articles examined through the five evaluation questions outlined in Table 1.

Figure 2:

Coverage of aspects of prompt consideration.

The result indicates that the overall prompt consideration in the examined articles is relatively low and unevenly distributed across different aspects. The most frequently addressed aspect – mention of prompts – appears in only 68.75 % of the studies, meaning that 31.25 % of the articles show no indication of prompt awareness at all. Among the five aspects, only two – mention of prompts and specification of prompts used – are addressed in more than half of the studies, while the remaining three aspects are notably underrepresented, with coverage rates around 20 %–30 %. Notably, introducing or reviewing the role of prompts, assessing their impact on ChatGPT’s translation performance, and integrating their influence into findings likely require more intentional effort and specialized knowledge than merely mentioning and specifying the prompts. This pattern suggests that researchers who acknowledge prompts in their studies tend to focus on more explicit and surface-level aspects, whereas deeper exploration of prompt usage remains limited, leaving a substantial gap in ChatGPT-related research.

It is also worth noting that even when an aspect is addressed, its consideration often lacks depth. For example, in Lexical Diversity and Syntactic Complexity in ChatGPT Translation (Yu 2024), the author mentioned in the introduction that although ChatGPT was not specifically designed for translation, its translation abilities can be brought out with accurate prompts – an observation that is very accurate, insightful and aligned with the central idea of this study. However, this point was not further elaborated upon in the subsequent sections. Instead, the study relied on a fixed prompt, “translate from Chinese to English”, for all tests, without examining how different prompts might affect ChatGPT’s translation performance. This trend is particularly evident in the analysis of prompt specification and usage, as discussed in Section 4.1.2.

Figure 3 provides a more detailed view of how the examined studies addressed different aspects of prompt consideration.

Figure 3:

Distribution and overlap of articles by prompt consideration aspects.

The bar chart on the left displays the number of articles covering 0 to 5 aspects of prompt consideration, while the Venn diagram on the right illustrates the overlaps among these aspects. Each oval in the Venn diagram represents one aspect outlined by Questions 2 to 5. Question 1 is excluded as it serves as the minimal threshold for consideration and encompasses the other four aspects. The numbers in the diagram indicate how many studies share the characteristics represented by each overlapping area. Different shades and font colors are used to indicate varying levels of intersection.

Figure 3 shows that comprehensive coverage of aspects of prompt consideration is rare: only five articles (15.6 %) addressed all five aspects. Most studies only covered a subset of these aspects: three addressed four aspects, four addressed three, nine addressed two, one addressed only a single aspect, and ten did not cover any aspect at all.

Moreover, the overlaps among various aspects are limited and highly uneven. The largest area of intersection involves eight articles that both mentioned and specified prompts (some of which may have only specified prompts, as specifying is a subset of mentioning). In contrast, overlaps across other combinations are scattered.

Notably, among the five papers that addressed all five aspects of prompt consideration, three are from the field of computer science and information technology, authored by researchers from institutions such as Microsoft (Hendy et al. 2023) and Tencent (Jiao et al. 2023), rather than scholars in translation studies, indicating a disciplinary disparity which will be further explored in Section 4.2.

4.1.2 Specification and use of prompts

Figures 4 and 5 visualize the specification and use of prompts in the reviewed articles. Figure 4 is a bar chart showing the number of articles that specified the prompts used, explained the rationales behind prompt selection, and leveraged prompting tactics, respectively. Figure 5 uses a Venn diagram to illustrate the overlaps among these aspects, accompanied by a summary table that explains key counts in selected intersecting areas.

Figure 4:

Number of articles by prompt specification and usage aspects.

Figure 5:

Overlap among aspects of prompt specification and usage.

Among the 19 articles that specified prompts, prompt usage appears limited and falls into two contrasting patterns. 8 out of the 19 articles neither justified their prompt selection nor employed any prompting tactics, relying solely on plain, straightforward prompts such as “translate from language A to language B”. In contrast, the remaining 11 articles demonstrated greater attention to prompt usage: most of them (9 out of 11) both provided a rationale for their prompt selection and applied prompting techniques, while the other two addressed only one of these two aspects.

Sections 4.1.2.1 and 4.1.2.2 present how the selected papers justified their selection of prompts and applied prompting tactics to meet research objectives. These are the two key aspects that reflect how prompts were utilized, corresponding to the two sub-questions of Question 4 listed in Table 1.

4.1.2.1 Rationale for prompt selection

Figures 6 and 7 indicate how examined studies used rationales for prompt selection. As shown in Figure 6, the 10 articles that gave justifications for their prompt selection drew on four types of rationale, each used with similar frequency. Figure 7 illustrates the overlaps among these rationale types. It suggests that prompt choices were often based on limited reasoning: of the 10 studies, six relied on a single justification approach, four combined two rationales, and none addressed more than two. The combinations of different rationale types were scattered, with no prevailing justification pattern.

Figure 6:

Number of articles by prompt selection rationale types.

Figure 7:

Distribution and overlap of articles by rationale types.

Each rationale type provides a reasonable basis for prompt selection by offering methodological rigor, empirical support, or alignment with prior research. However, many justifications remain relatively simplistic and lack a strong connection to the specific characteristics of the research.

1. Conformity to research requirements. Four papers justified their prompt selection based on specific methodological or technical needs, ensuring alignment with research objectives. However, these connections were generally weak or indirect. For instance, Klamra et al. (2023) selected few-shot examples in their prompt to avoid format problem, as they required ChatGPT to generate responses in JSON format for easier parsing. While this rationale was valid, it was not directly related to the core evaluation task. Moreover, their justification only addressed the selection of few-shot examples, overlooking the prompt template design.

2. Reference to other studies or ChatGPT. Four papers relied on prompts derived from prior research or recommendations, or directly asked ChatGPT to provide prompts. This approach has the advantage of building on existing knowledge or leveraging ChatGPT’s own capabilities to generate task-relevant prompts, potentially reducing bias in manual selection. However, in most cases, the researchers only briefly mentioned the source without analyzing the source itself. For example, Jiao et al. (2023) instructed ChatGPT to generate “ten concise prompts or templates that can make you translate” without specifying the task or translation requirements. Consequently, ChatGPT produced a set of generic prompts, such as “Translate this sentence from English to French”, with some even being repetitive. These prompts lacked any advanced prompting techniques and were unlikely to yield significantly different results.

3. Empirical comparison of prompts. Three papers compared multiple prompts and selected the best-performing one. While this approach added some empirical support to their rationale, the initial selection of prompts was often questionable. For instance, Hou et al. (2024) tested three prompt templates before selecting the most effective one for a few-shot evaluation. However, the three prompts – “Translate X into Chinese”, “Translate Lao sentence X into Chinese” and “Lao: X; Chinese:” – were simplistic and highly similar, with no clear explanation for why these specific variations were chosen.

4. Selection of few-shot examples. Three papers focused on explaining the selection of examples used in few-shot prompting. While this is a highly relevant consideration, their discussions overlooked the design of the actual prompts themselves, leaving a critical aspect of prompt engineering unaddressed.

4.1.2.2 Leverage of prompting tactics

As noted earlier, among the 19 articles that specified prompts, only 10 employed any form of prompting tactics, while the remaining articles relied solely on plain and straightforward prompts. In terms of the use of prompting tactics, five distinct approaches with varied frequency were identified, as shown in Figure 8. Figure 9 illustrates the overlaps among these tactics, which are minimal, with non-zero counts appearing only along the peripheries.

Figure 8:

Number of articles by prompting tactics leveraged.

Figure 9:

Distribution and overlap of articles by prompting tactics. Note: For clarity, areas in the Venn diagram without numeric labels indicate zero values and have been left unmarked to enhance visual readability.

Few-shot prompting was the most commonly used approach, appearing in five studies. In this method, researchers provided ChatGPT with examples – ranging from 1 to 15 shots – to facilitate its learning for translation tasks. Four studies adopted context provision, where researchers either supplied ChatGPT with the text to be translated within a broader context or provided background information such as the text’s source and target audience, enhancing its context awareness in translation tasks. Four studies used detailed instruction, specifying requirements such as output format, style, and terminology use. Two studies applied chain-of-thought prompting, guiding ChatGPT to break down the translation process into sequential steps. Finally, iterative prompting was utilized in two studies, involving multiple rounds of interaction with ChatGPT that provided feedback to refine its performance.

While these studies explored key prompting tactics relevant to translation tasks, their implementation appears to be somewhat simplistic and not fully optimized. Two observations can be made:

1. Limited combination of tactics. While most of the articles (7 out of 10) used two tactics, the combination was mostly limited to pairing few-shot prompting with another tactic. Combinations involving more than two tactics were absent. Additionally, in some cases, articles that employed two tactics used them separately in different tests, rather than integrating them organically.

2. Rigid application of tactics. The way each tactic was employed tends to be basic and rigid. For instance, Calvo-Ferrer (2023) employed iterative prompting by responding to ChatGPT with a fixed prompt – “It sounds awful. Please make it sound more natural in Spanish and make sure the conversation makes sense” – regardless of ChatGPT’s actual output, rather than providing targeted feedback based on its responses.

4.2 Variations in prompt consideration across disciplines

4.2.2 Overall level of prompt consideration

It is evident from Figure 10 that articles in computer science and information technology exhibited the highest level of prompt consideration in most aspects, except for the specification of prompts used, where they surpass those in translation and linguistics but fall behind other disciplines by about 20 %. Furthermore, studies in computer science and information technology demonstrated a more evenly distributed consideration of prompts across different aspects, without major shortcomings, in contrast to the dramatic fluctuations observed in the other two groups. In most aspects, over half of the articles from computer science and information technology exhibited consideration.

Figure 10:

Comparison of overall level of prompt consideration across disciplines.

As mentioned preliminarily in Section 4.1.1, among the five papers that demonstrated the most comprehensive consideration of prompts, three are from the field of computer science and information technology, one is from translation and linguistics, and the remaining one is from the field of chemistry. Taking into account the overall distribution of articles across these disciplines, this further supports the advantage shown by articles from computer science and information technology.

When comparing articles from translation and linguistics and those from other disciplines, no definitive difference emerges, as each group excels in certain aspects. In fact, their distribution patterns closely resemble each other: apart from the similarly sharp fluctuations, they follow a comparable trend in addressing different aspects, characterized by relatively high levels of consideration in mention of prompts and specification of prompts used, and lower levels of consideration in other aspects (and very low levels of consideration in the examination of prompts’ impact). This pattern contributes to the overall trend observed in the analysis of all 32 articles discussed in Section 4.1.1.

4.2.3 Specification and use of prompts

As shown in Figure 11, though a smaller proportion of articles in computer science and information technology specified the prompts used, these articles exhibited higher levels of consideration in both explanation of rationale and leverage of prompting tactics. In fact, all articles in computer science and information technology that specified prompts also provided a rationale, and employed at least one prompting tactic, resulting in identical levels of consideration across these aspects. In contrast, quite a number of articles in translation and linguistics and other disciplines specified prompts but did not explain why and/or employ any prompting tactics.

Figure 11:

Comparison of specification and use of prompts across disciplines.

A comparison between articles from translation and linguistics and those from other disciplines reveals similar patterns, with the latter exhibiting higher levels of consideration in both explanation of rationale and leverage of prompting tactics.

Furthermore, the articles from computer science and information technology not only present the most diverse rationales to justify the selection of prompts, but also cover the widest range of prompting tactics, suggesting a more intentional control over prompt use and greater familiarity with the available toolkit among researchers in this field.

5.1 General under-emphasis on prompts

The survey reveals a consistent under-emphasis on prompts in the reviewed articles, both in terms of the overall prompt consideration and the way prompts were used.

First, the overall level of prompt consideration in the examined articles is relatively low. More than 30 % of the surveyed articles did not mention the concept of prompts at all. Among those that did, most focused only on a narrow subset of aspects, with very few offering a comprehensive consideration of all relevant dimensions. Even when an aspect was addressed, the discussion often lacked sufficient depth.

Second, the way prompts were used is largely limited and often lacks justification. Among the 19 articles that specified the prompts used – which is already a rather small fraction – nearly half relied solely on plain and straightforward prompts such as “Translate from language A to language B”. For the remaining studies, the justification for prompt selection often lacked a strong connection to research objectives. Additionally, the prompt usage was characterized by limited combinations and rigid applications.

From many perspectives, ChatGPT was used in these articles in much the same way as Google Translate, where users select the source and target languages, input the text, and await the output – following a rigid, predefined workflow with limited variability. While prompts were mentioned, they often seemed to be included merely as a procedural step in the methodology rather than a focal point for critical examination. As a result, ChatGPT’s potential as a generative AI remained largely underexplored.

The ways in which prompts are considered and used can be seen as a reflection of users’ cognitive models, which reveals how they conceptualize generative AI and how they attempt to communicate intent to them (Shen and Yu 2025). With the rapid development of generative AI, prompt literacy – the ability to effectively craft and adopt prompts to achieve intended goals – has emerged as an important extension of digital literacy (Hwang 2023; Korzynski et al. 2023; Zhang and Jia 2024). While academic interest in prompt design and usage is rapidly growing, studies have highlighted that uses’ actual prompting behaviors in real-world contexts remain underexplored, and are often insufficiently strategic in practice (Zhao et al. 2025). This aligns with the findings of the present study, where even researchers – presumably more informed users – frequently failed to make full use of ChatGPT’s capabilities. These findings underscore the need for researchers to have more comprehensive consideration of prompts in ChatGPT studies, which is supported by strengthened prompt literacy, and more broadly, by a deeper understanding of AI and how to interact with them.

5.2 Disparity across research disciplines

The analysis reveals a significant imbalance in the consideration of prompts across various disciplines.

In terms of both overall prompt consideration and prompt usage, articles in computer science and information technology exhibited the highest, most balanced and most comprehensive consideration. This suggests that researchers in these fields may share a more established understanding of prompt’s impact on ChatGPT’s performance and how to effectively use it.

On the other hand, articles from translation and linguistics, as well as those from other disciplines – both lagging behind studies from computer science and information technology – showed no significant difference from each other. Both groups exhibited dramatic fluctuations across different aspects of prompt consideration. Specifically, they demonstrated relatively high attention to more explicit and surface-level aspects including mentioning and specifying prompts, but had considerably lower engagement in other aspects like examining prompts’ impact. This disciplinary pattern largely contributes to the overall uneven distribution of prompt consideration observed in the articles surveyed.

Such pattern unfolds against the broader backdrop of generative AI increasingly blurring the boundaries between the humanities and technological fields (Chen 2024). Scholars in the humanities – including those in translation and linguistics – are actively engaging with technological tools and expanding their academic horizons by exploring new research questions and adopting innovative methods (Hu and Li 2023; Yu and Liu 2024). While these efforts are both timely and commendable, the comparison reveals differences in both methodological approaches and underlying thinking paradigms between studies conducted by researchers in computer science and information technology and those in other disciplines, even when addressing very similar research questions. To foster more meaningful interdisciplinary outcomes, it is essential for researchers in translation and linguistics to develop a more foundational and up-to-date understanding of the underlying technologies. As will be further discussed in Section 5.3.2, such understanding can be effectively enhanced through deeper interdisciplinary collaboration.

5.3 Recommendations for future research