请帮我把这句话改的不像ai生成的，要点请保留：This research contributes s...

请帮我把这句话改的不像ai生成的，要点请保留：This research contributes several innovations to the intersection of translation studies and natural language processing:

1. It marks the first systematic application of communicative translation theory to the optimization of hallucination reduction in LLM-based translation. By aligning machine outputs with functional equivalence and reader-centered pragmatics, the study extends Newmark's framework into the realm of generative AI.

2. The work bridges a critical gap between translation distortion theory and LLM hallucination research. Previous studies have largely addressed hallucination as a computational error or factual inconsistency, whereas this paper frames it within a translational fidelity context—revealing new explanatory dimensions and intervention possibilities.

3. The study proposes a hybrid scoring and classification system, combining: A hallucination typology (semantic drift, fictional information, misleading metaphor, structural pseudo-fidelity);A human evaluation rubric rooted in communicative translation principles (semantic consistency, logical coherence, information integrity, and communicative effect); And traditional automatic metrics (BLEU, cosine similarity), thereby creating a theory-informed and operationally robust framework for evaluating LLM translations.

将上面的内容翻译为英文

Replace all transition words and conjunctions in the sentences with the most basic and commonly used ones.Use simple expressions，avoiding complex vocabulary.Ensure the logical connections between sentences are clear.Deletes the conclusion part in the end of the text.
：This study introduces several novel insights at the intersection of translation studies and natural language processing:

It presents the first systematic application of communicative translation theory to the optimization of hallucination reduction in large language model (LLM) translation. By emphasizing functional equivalence and reader-oriented pragmatic strategies, the research extends Newmark’s translation theory into the context of generative AI.

The paper addresses a significant gap between the theory of translation distortion and current research on hallucinations in LLMs. Whereas prior studies often treat hallucinations as computational errors or factual inaccuracies, this study frames them within the paradigm of translational fidelity, offering new explanatory perspectives and potential interventions.

A comprehensive scoring and classification system is proposed, consisting of three components: a typology of hallucinations—including semantic drift, fabricated information, misleading metaphors, and structural pseudo-fidelity; a human evaluation rubric based on communicative translation principles, focusing on semantic consistency, logical coherence, information integrity, and communicative effect; and traditional automatic evaluation metrics such as BLEU scores and cosine similarity. Together, these elements form a theoretically grounded and practically robust framework for assessing LLM-generated translations.

Replace all transition words and conjunctions in the sentences with the most basic and commonly used ones.Use simple expressions，avoiding complex vocabulary.Ensure the logical connections between sentences are clear.Deletes the conclusion part in the end of the text：
Despite the contributions of this study to the field of AI-assisted translation and hallucination mitigation, several limitations must be acknowledged:

(1) Limited Corpus Scope
This research was conducted using selected excerpts from a single source text—The Psychology of Money by Morgan Housel. Although this book is rich in metaphor, financial reasoning, and rhetorical diversity, its stylistic and thematic range is still confined to the popular financial non-fiction genre. The generalizability of the findings to other domains such as legal, medical, or technical translation remains untested.

(2) Restricted Language Pair and Monolingual Direction
All experiments were conducted in the English-to-Chinese translation direction, focusing solely on hallucinations arising in Chinese target texts. The hallucination behavior of LLMs may differ across language pairs and linguistic systems, especially in morphologically rich or low-resource languages. Therefore, the current results do not account for multilingual or bidirectional translation scenarios.

(3) Strategy Evaluation in Controlled Prompts Only
The three proposed fidelity optimization strategies were implemented using carefully engineered prompts, and their effectiveness was measured under these fixed conditions. In real-world deployment, prompts may vary significantly in length, structure, and user intent. As such, the strategies' robustness across diverse prompting environments is still uncertain.

(4) Subjectivity of Human Scoring
Although manual evaluation was conducted by qualified graduate-level translators, it remains inherently subjective. While inter-rater agreement was substantial and scoring rubrics were standardized, biases related to personal judgment, stylistic preference, or interpretive latitude may have influenced the results. The reliance on qualitative dimensions (e.g., “communicative effect”) poses challenges for replicability.

Replace all transition words and conjunctions in the sentences with the most basic and commonly used ones.Use simple expressions，avoiding complex vocabulary.Ensure the logical connections between sentences are clear.Deletes the conclusion part in the end of the text：7.2 Future Research Directions

Building on the foundations laid in this study, several promising avenues for future investigation are proposed:

(1) Expansion to Larger, Multidomain Corpora
Future research should extend the current framework to include multiple genres and domains, such as legal documents, scientific abstracts, healthcare manuals, and literary works. A broader corpus would allow for more robust typological mapping of hallucinations and enable comparison across stylistic registers.

(2) Construction of a Hallucination-Annotated Benchmark Dataset
There is a clear need to create a standardized, large-scale hallucination dataset, annotated by professional translators across different types and degrees of distortion (e.g., semantic drift, fictional content). Such a resource could serve both as a training corpus for fine-tuning LLMs and as an evaluation benchmark for future research.

(3) Incorporation of Multimodal and Multisource Context
Future strategies should test whether multimodal input—such as image captions, financial charts, or tables—can improve LLMs’ grounding and reduce hallucination in data-rich texts. Likewise, incorporating intertextual or metadata-based context may enhance discourse coherence and pragmatic fidelity.

(4) Development of Adaptive Prompt Generation Systems
One practical extension is to develop an automated strategy selector or prompt generator, which dynamically assigns the appropriate strategy (e.g., context retrospection or cultural mapping) based on the content features of the source paragraph. This would enable more intelligent interaction with LLMs, especially in real-time translation platforms.

(5) Integration of Hallucination Recognition into Translation Pipelines
Future systems could incorporate hallucination detection and correction modules directly into the translation workflow. These could work as post-editing alerts or preemptive alignment checkers, helping users identify mistranslations that deviate from source logic or factual content before publication.

请帮我把这段话改的不像ai生成的，要变成学术性的一段叙述文字：
8.1 Limitations of the Study
Even though this study helps AI-assisted translation and reducing hallucinations, there are some limits:
(1) Small Text Range
This study used parts from only one book — The Psychology of Money by Morgan Housel. This book has many metaphors, ideas about money, and different writing styles, but it still belongs to the same type — popular financial non-fiction. So, we don’t know if the results would be the same for other types of writing, like legal, medical, or technical texts.
(2) One Language Pair and One Direction Only
All tests were done from English to Chinese. They only looked at problems in the Chinese translations. But LLMs may act differently with other language pairs, especially with languages that are very different or not well-studied. So, these results do not show what happens in other language directions or in more than two languages.
(3) Strategies Tested in Fixed Prompts Only
The three methods to improve translation were tested using planned and fixed prompts. These prompts stayed the same during testing. But in real use, prompts can be longer, shorter, or written in many ways. Because of this, we still don’t know if these methods work well in all kinds of prompts.
(4) Human Scoring Is Still Personal
Even though trained translators did the scoring, their judgments are still personal. The scoring system was the same for all, and the agreement between scorers was good, but things like personal style or different ways of understanding meaning may have affected the scores. Also, some parts of the scoring (like how well the message is shared) are hard to measure in the same way every time.

请帮我把这段话改的不像ai生成的，要变成学术性的一段叙述文字：(1) Use of Bigger and More Varied Texts
Later research can try this method on more kinds of texts, like legal writing, science papers, health guides, and stories. Using more types of texts can help find more types of hallucinations.
(2) Make Smart Prompt Tools
One good next step is to build a tool that picks the right method or prompt for each case. For example, if a text has culture-based terms, the tool can pick a prompt that helps with that. This can make working with models easier and faster.
(3) Put Hallucination Checks into the System
Future systems can add ways to find and fix hallucinations before the user sees the result. These checks can show warnings or catch mistakes early. This helps stop wrong or unclear translations from being shared.

把这段内容浓缩：This study demonstrates that all three proposed fidelity optimization strategies—Pragmatic Role Alignment, Cultural Equivalence Mapping, and Contextual Retrospection—are capable of reducing hallucination in AI-assisted translation to varying degrees. Among them, the Contextual Retrospection Strategy proved the most effective, with the lowest total number of confabulations (4) and the highest average human evaluation score (183.96/200). Its strength lies in maintaining discourse-level coherence and ensuring referential consistency across paragraphs, which minimizes semantic drift and structural hallucinations.
The Cultural Equivalence Mapping Strategy, while slightly more prone to introducing poetic or metaphorical additions, excelled in reducing misleading metaphors and enhancing rhetorical resonance—particularly in culturally embedded expressions. This strategy was most effective when the source text contained financial idioms or cultural metaphors not directly translatable into Chinese. Its performance in the "communicative effect match" dimension reached an average of 4.6, the highest among the three.
The findings also suggest that different hallucination types respond differently to specific strategies. For instance: Fictional information hallucinations were best handled by Contextual Retrospection;Metaphorical distortion was mitigated most effectively by Cultural Equivalence Mapping;Pragmatic Role Alignment improved consistency in speech act realization but had a moderate effect on structural hallucinations.

将这段内容浓缩：The comparative results revealed the following key findings:

All three strategies led to a reduction in hallucination frequency and an improvement in translation fidelity scores.
Among them, the Contextual Retrospection Strategy was the most effective overall, with the lowest hallucination rate (4 confabulations) and highest human evaluation score (183.96/200). It preserved discourse continuity, referential alignment, and logical progression better than other strategies.
The Cultural Equivalence Mapping Strategy was more effective in translating metaphorical and idiomatic content, improving the communicative effect score while introducing occasional poetic embellishments.
The Pragmatic Role Alignment Strategy performed best in preserving intentional and functional meaning, particularly in persuasive or explanatory sentences.
These results indicate that semantic hallucinations are not monolithic and that targeted strategies are required for different hallucination types. Fidelity optimization is thus both a linguistic and functional design challenge.