In December 2025, Merriam-Webster announced its 2025 Word of the Year: “Slop.” Notably, besides Merriam-Webster, The Economist also selected “slop” as its 2025 Word of the Year.

Merriam-Webster defines “slop” as “digital content, often mass-produced by artificial intelligence, that is of low quality.” Greg Barlow, President of Merriam-Webster, stated, “This word is highly symbolic, representing both the transformative technology of AI and the complex emotions of fascination, frustration, and even absurdity that people feel toward it.”

01. What Constitutes High-Quality Datasets in the AI Era

If low-quality content is the “noise” of the digital age, what serves as the true “signal” that nourishes intelligence? This naturally leads the discussion to the foundation of artificial intelligence: data.

As the saying goes, “Even the cleverest housewife cannot cook a meal without rice.” Like humans, AI requires vast amounts of data as “nourishment” for model training and deep learning. Currently, much of the training data for language models (LLMs) comes from the internet, where quality varies widely. Generated content relies on “probabilistic matching” rather than “factual judgment,” leading to frequent “hallucinations.”

Thus, it can be said that without high-quality data, high-quality artificial intelligence cannot be “nurtured.” In this context, high-quality datasets play a crucial role in the training, reasoning, and validation of AI large models.

A high-quality dataset refers to a collection of data that has been collected, cleaned, classified, and labeled according to specific standards, with mechanisms for updates and maintenance.

02. Current State of Data in the AI Era: A Surge in Quantity, but a Rapid Decline in Quality

However, high-quality data does not come easily or readily. The more we recognize its decisive significance for AI development, the more we need to soberly examine the severe challenges facing data supply in reality—a vast gap exists between ideal standards and the current scarcity (The original Chinese term " scarcity " means scarcity or lack. Here, it is translated as "reality" to convey the sense of the actual situation being far from ideal.) reality.

In the past, computational power and algorithms were the primary drivers of AI breakthroughs. Today, with foundational model architectures gradually converging and technical approaches becoming increasingly similar, high-quality data has emerged as the new battleground for determining model performance differences and the core bottleneck for AI to reach higher levels of intelligence.

It must be pointed out that we are caught in a paradox of data “abundance and scarcity”: the global data volume is expanding at an unprecedented rate, with massive amounts of text, images, and voice content generated and stored daily, seemingly inexhaustible. However, high-quality, structured, and compliant data that can actually be used for AI model training is extremely scarce. This contradiction is becoming increasingly pronounced across three major dimensions.

The first is structural imbalance in supply. Taking linguistic data as an example, English content dominates training corpora due to historical internet accumulation, while high-quality texts in Chinese, Arabic, and minority languages are severely underrepresented. Especially in Chinese academic and professional domains, the volume of cleaned, labeled, and knowledge-aligned corpora falls far short of meeting the needs for model refinement. This directly leads to asymmetric capabilities in specific linguistic and cultural contexts.

The second is the uneven quality of data. Most internet-native data resembles unrefined “crude oil,” with chaotic formats, pervasive noise, and widespread social biases, misinformation, or low-quality repetitions. Even some collected public data often suffers from inconsistent labeling standards, missing key information, and narrow domain coverage, making it difficult to directly support industry applications and cutting-edge research that require high reliability.

The third is systemic inefficiency in data utilization. Despite the vast amount of data, most remains “dormant”: constrained by privacy regulations, commercial barriers, and technological limitations, data lacks effective linking and secure circulation mechanisms, with extremely low reuse across scenarios and domains. Many enterprises and research institutions often repeat data collection and cleaning efforts without building sustainable data ecosystems, resulting in significant resource waste.

03. Four Key Characteristics: Accuracy, Completeness, Consistency, and Timeliness

Given the critical importance of high-quality data, how should we define and identify it? This requires a clear and measurable set of standards. Among them, accuracy, completeness, consistency, and timeliness are regarded as the four core pillars for assessing data quality, collectively forming a solid foundation for trustworthy data.

Specifically, accuracy is the soul of data quality, ensuring that each data point truly and accurately reflects objective facts. Erroneous data, like cracks in a foundation, can lead to misleading conclusions and even severe decision-making failures, regardless of how sophisticated subsequent analyses may be.

Completeness focuses on whether data is fully intact. Missing data fields or records, like lost puzzle pieces, create information gaps, distorting the overall picture and rendering it unable to support comprehensive analysis. Especially in correlation analysis or trend forecasting, incomplete data directly undermines the persuasiveness of conclusions.

Consistency emphasizes internal harmony and logical unity within data. It means that within the same dataset or across different datasets, data definitions, formats, and logical relationships should remain stable and free of contradictions. For example, information about the same customer in different systems should align, and statistical standards across time points should be comparable. Inconsistent data creates confusion, increases the difficulty of integration and cleaning, and undermines the effectiveness of cross-departmental or cross-temporal comparisons.

Finally, timeliness endows data with real-world relevance. In a rapidly changing world, outdated data, like yesterday’s weather forecast, quickly loses its value. Especially in fields such as finance, logistics, and public health, the ability to promptly access and process the latest information often directly determines the success or failure of actions.

These four characteristics do not exist in isolation but are interdependent and mutually constraining. Accurate but incomplete data offers a narrow perspective, while complete but outdated data may point in the wrong direction. Only by simultaneously addressing all four aspects can data transcend its raw form of characters and numbers, evolving into a truly trustworthy asset that provides a solid and dynamic basis for rational decision-making.

04. Conclusion

We stand at a crossroads where technology and content are deeply intertwined in competition. On one side is the “Slop” representing the increasingly flood (The Chinese term " flood " means overabundance or flood. Here, it is translated as "prevalent" to convey the widespread nature of low-quality AI content.) prevalent low-quality AI content, reflecting the crude and impetuous nature of the early stages of technological proliferation. On the other side are high-quality datasets supported by the pillars of “accuracy, completeness, consistency, and timeliness,” representing the inevitable path for AI to mature, become trustworthy, and achieve deep intelligence. The outcome of this competition will determine whether the internet descends into a “sinking age” of information entropy or advances toward a new era of continuously rising knowledge density and value.

The focus of future AI competition has clearly shifted from computational power and algorithms to data itself: how to extract high-value, highly usable “refined grains” from vast “raw ores” will become the core capability shaping the next generation of intelligence. Only by prioritizing quality and constructing a solid, vibrant, and professional data foundation can we harness the potential of AI and ensure that technology truly serves the advancement and deepening of human knowledge.

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