🔥LLM Interview Series(7): Data Quality, Scaling Laws, and Model Performance

1. (Interview Question 1) Why is data quality more important than dataset size when training modern LLMs?

Key Concept: Data Quality vs. Quantity Trade-off

Standard Answer:
While scaling up datasets can meaningfully boost LLM performance, high-quality data consistently delivers disproportionate gains compared with simply adding more tokens. The fundamental reason lies in how transformer models learn: LLMs rely on statistical correlations in data to infer patterns, reasoning structures, and world knowledge. If the data is noisy, contradictory, duplicated, or incorrectly labeled, the model’s internal representations become diluted. This degrades the model’s ability to generalize, coherently reason, and produce consistent answers.

Quality issues such as hallucination-prone text, spam, SEO garbage, incomplete code snippets, or low-information pages waste compute because the model spends capacity memorizing junk tokens rather than meaningful structure. In contrast, training on well-curated, diverse, deduplicated, and semantically rich data makes every token more valuable. High-quality tokens produce more “learning per FLOP.”

A growing body of scaling literature shows that data quality affects the scaling curve itself, not just the end result. When data is clean, models reach higher accuracy with fewer parameters and fewer training tokens. A model trained on 1T high-quality tokens may significantly outperform a model trained on 3T noisy tokens—even if FLOPs are equal.

Practically, organizations now invest heavily in filtering pipelines: near-duplicate removal, language detection, toxicity filtering, factuality scoring, perplexity filtering, and reinforcement learning from human feedback (RLHF) to enforce preference alignment. This is also why synthetic data (e.g., model-generated instruction datasets) is now common—synthetic datasets often have higher structure, clearer formatting, and more consistency than raw internet text.

So although scaling helps, the biggest modern performance jumps come from better data, not more data. In short:
Clean data scales. Noisy data saturates.

Possible 3 Follow-up Questions: 👉 (Want to test your skills? Try a Mock Interview — each question comes with real-time voice insights)

1. How would you design a data quality pipeline for a new LLM product?
2. What metrics would you use to quantify “data quality”?
3. How can synthetic data supplement limited real-world datasets?

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2. (Interview Question 2) What are scaling laws, and how did they change the way we build LLMs?

Key Concept: Neural Scaling Laws

Standard Answer:
Scaling laws refer to empirical relationships—first formalized by Kaplan et al.—showing that LLM performance improves predictably as we increase model parameters, dataset size, and compute. These laws revealed that loss decreases as a power law with respect to scale, meaning performance gains remain consistent and predictable across orders of magnitude.

This discovery fundamentally reshaped AI research. Before scaling laws, improvements were typically driven by new architectures or clever optimization tricks. After scaling laws, organizations realized that simply scaling up transformers reliably produces better generalization, even with minimal architectural changes. This shift created the era of “foundation models,” where training giant models on vast, diverse text corpora became the dominant approach.

Scaling laws also revealed optimal allocation of compute: given a compute budget, there is an optimal ratio of model size to training tokens. Oversized models trained on too few tokens underperform, while small models with too many tokens waste compute. This insight improved training efficiency and helped avoid expensive mistakes.

However, scaling laws have limits. Beyond certain scales, data quality bottlenecks emerge—loss curves flatten because the dataset simply does not contain sufficiently rich or diverse information. This is why modern research emphasizes data curation, augmentation, and filtering as much as scaling parameters.

In summary, scaling laws enabled companies to predict ahead of time how big a model must be to reach a target performance, and what compute budget is required. They also democratized AI research by providing a roadmap that any organization with enough compute can follow. Today, nearly all frontier LLMs—from GPT to Claude to Gemini—are direct descendants of scaling law optimization.

Possible 3 Follow-up Questions: 👉 (Want to test your skills? Try a Mock Interview — each question comes with real-time voice insights)

1. What are the practical limits of scaling laws?
2. How do scaling laws change when using high-quality filtered datasets?
3. What happens if model size is scaled without increasing dataset size?

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3. (Interview Question 3) How does deduplication impact model performance and training efficiency?

Key Concept: Deduplication, Token Efficiency, Overfitting Prevention

Standard Answer:
Text deduplication—removing repeated passages, documents, or near-duplicates—is one of the highest-ROI data-cleaning steps in LLM training. Internet-scale datasets contain enormous redundancy: boilerplate pages, scraped templates, repeated forum posts, duplicated code files, mirrored Wikipedia dumps, or identical PDF copies.

Training on duplicates wastes compute because repeated tokens provide diminishing marginal learning value. After a model has seen a text pattern several times, additional copies contribute little new information. Worse, duplicates can lead to overfitting, causing models to memorize exact text sequences, increasing hallucination risk and reducing generalization capacity.

Deduplication improves performance in several ways:

1. Better generalization
   Removing duplicates encourages models to learn underlying patterns rather than memorizing verbatim text.

2. Improved sample efficiency
   Every training token becomes more unique and informative. Models trained on deduplicated corpora learn faster and reach lower loss.

3. Reduced memorization risk
   This is critical for privacy concerns (e.g., personal information in repeated data dumps). Deduplication reduces the probability of the model regurgitating sensitive text.

4. Smoother scaling behavior
   Scaling laws become more robust when data is unique and diverse—loss curves track more predictably.

Deduplication is typically implemented using techniques like MinHash, SimHash, LSH (locality-sensitive hashing), or embedding-based similarity search. Pseudocode example:

for doc in corpus:
    hash = compute_lsh_hash(doc)
    if hash not in seen_hashes:
        keep(doc)
    else:
        discard(doc)

Deep deduplication—sentence-level or paragraph-level—goes further, removing small repeats inside large documents. This has shown significant performance improvements, especially for code models.

In short, deduplication is a quiet superpower. It improves quality, reduces compute cost, and increases factual reliability without adding a single new token.

Possible 3 Follow-up Questions: 👉 (Want to test your skills? Try a Mock Interview — each question comes with real-time voice insights)

1. How would you evaluate whether your deduplication threshold is too aggressive?
2. What’s the difference between exact and near-duplicate detection?
3. Why is deduplication even more important for code models?

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4. (Interview Question 4) How does model performance scale with training compute, and why is “effective compute” more important than raw FLOPs?

Key Concept: Effective Compute, Training Efficiency

Standard Answer:
Raw compute—measured in FLOPs—captures the total mathematical operations used during training. But what truly predicts model performance is effective compute: the amount of compute actually contributing to useful learning. Two models may use identical FLOPs, but the one trained with better data, better batching, and better hyperparameters can outperform the other dramatically.

Effective compute depends on factors like:

• Data quality (high-quality tokens yield more gradient signal)
• Data diversity (preventing saturation)
• Curriculum ordering (tokens shown earlier influence representation space)
• Learning rate schedules
• Optimizer choice (AdamW vs. Adafactor)
• Deduplication (eliminating waste)
• Tokenization strategies

For example, training on a low-quality dataset may require 10× more tokens to reach the same loss achieved with a curated dataset. Here, raw compute is identical, but effective compute differs massively. This is why companies like OpenAI, Anthropic, and Google invest so heavily in data pipelines—they directly improve how efficiently compute is translated into model intelligence.

Scaling laws themselves implicitly assume effective compute. If a dataset is noisy or duplicated, the empirical scaling curve shifts downward—meaning more compute is required to achieve the same performance.

Recent research also suggests that effective compute is becoming the real competitive moat. While hardware progress is slowing, improvements in token efficiency, deduplication, synthetic data, model distillation, and curriculum strategies can produce equivalent performance at a fraction of the FLOPs.

So while raw compute is a baseline input, effective compute determines real-world model quality and is now the dominant driver of LLM competitiveness.

Possible 3 Follow-up Questions:

1. How do you measure effective compute in practice?
2. Which hyperparameters most influence training efficiency?
3. How can curriculum learning improve effective compute?

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5. (Interview Question 5) What are the trade-offs when scaling model parameters versus scaling dataset size?

Key Concept: Parameter Scaling vs. Data Scaling

Standard Answer:
Model scaling requires balancing two axes: the number of parameters and the size of the training dataset. Scaling one without the other leads to suboptimal outcomes.

Oversized models, undersized data:
When a large model (e.g., 100B+ parameters) trains on too few tokens, it becomes under-trained. The model underfits because its massive capacity doesn't receive enough examples to shape its representations. This produces poor generalization and high validation loss, despite enormous compute.

Undersized models, oversized data:
Conversely, using a small model with trillions of tokens causes saturation. The model lacks the capacity to absorb the dataset’s richness; beyond a certain point, additional tokens provide little benefit. Compute is wasted.

Scaling laws prescribe an optimal ratio: for a given compute budget, the dataset should be roughly proportional to model size. This ratio changes across domains—code, math, multilingual corpora—but the principle holds: parameters and data must scale together.

Trade-off considerations:

• Larger models capture deeper abstractions but require more training tokens.
• Larger datasets help generalization but require a large-enough model to benefit.
• High-quality datasets reduce the need for extreme scaling.

In practice, organizations increasingly prioritize data quality improvements over extreme parameter scaling. For example, small models trained on highly curated data often outperform larger models trained on raw web text.

Finally, inference considerations matter. Bigger models cost more to serve. For many applications, a smaller well-trained model is preferable to a massive model with mediocre training.

Possible 3 Follow-up Questions:

1. How do you determine the optimal dataset size for a given model architecture?
2. What happens if you intentionally over-train a smaller model on huge corpora?
3. Why is the compute-optimal frontier shifting toward better data, not bigger models?

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6. (Interview Question 6) How does tokenization affect model performance and scaling efficiency?

Key Concept: Tokenization Strategy, Vocabulary Design

Standard Answer:
Tokenization defines how raw text is broken into units that the model consumes. Because training cost scales with token count, tokenization efficiency directly influences compute, memory, and final model quality.

Bad tokenization wastes compute. For example, byte-level BPE tokenizers may split simple words into many sub-tokens, increasing sequence length and reducing training efficiency. Language-specific tokenizers (e.g., for Chinese) may segment text poorly, losing semantic structure.

Good tokenization improves representation learning by:

• Reducing average tokens per sentence
• Encoding meaningful semantic units
• Supporting multilingual and cross-domain text
• Improving handling of rare words and technical vocabulary

Better tokenization effectively gives a model more information per token, improving effective compute. This is why modern tokenizers use techniques like SentencePiece, Unigram LM tokenization, and large vocabulary sizes (e.g., 200k tokens) to reduce fragmentation.

Tokenization also affects scaling laws: if tokenization is inefficient, the model must process many more tokens to learn the same pattern, shifting the scaling curve downward.

Example comparison:

Text: "internationalization"
Bad tokenizer → ["inter", "nation", "al", "ization"]  
Efficient tokenizer → ["internationalization"]

Four tokens vs. one token may appear minor, but at web scale, this multiplies into trillions of unnecessary FLOPs.

Finally, tokenization affects downstream capabilities. Code models benefit from grammar-aware tokenization; multilingual models benefit from shared subword units; reasoning models benefit from compact representations of numbers and mathematical expressions.

Better tokenization = better scaling = better models.

Possible 3 Follow-up Questions:

1. How does vocabulary size impact model throughput?
2. Why do code models prefer different tokenization strategies?
3. What is the compute penalty of poor tokenization?

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7. (Interview Question 7) What are the sources of hallucination, and how does data quality influence hallucination frequency?

Key Concept: Hallucination Mechanisms

Standard Answer:
Hallucinations occur when a model generates text that is syntactically confident but factually incorrect. Many assume hallucinations arise primarily from model architecture limitations, but data quality is one of the strongest predictors of hallucination frequency.

Sources of hallucination include:

1. Low-quality and contradictory data
   If the training corpus contains conflicting facts, outdated information, or spam content, the model learns unstable representations.

2. Training on synthetic or templated content without grounding
   Some web pages mimic authoritative content but contain fabricated or SEO-inflated information.

3. Excessive memorization without true comprehension
   Models may learn to pattern-match text without understanding context, causing errors in unfamiliar situations.

4. Lack of negative examples
   If training data does not contain examples where the model must say “I don’t know,” it will default to generating plausible-sounding responses.

Data quality improvements reduce hallucinations significantly:

• Deduplication prevents pattern overfitting
• Factuality scoring removes unreliable pages
• RLHF teaches models when to decline a question
• Filtering outdated corpora (e.g., pre-2020 medical text) reduces contradictions
• Multimodal grounding improves stability

Additionally, high-quality data not only reduces hallucinations but also improves reasoning. Models trained on curated scientific literature or structured content—like textbooks and verified datasets—develop more stable internal representations.

Hallucination is not fully solvable, but it is highly controllable through better data engineering and post-training alignment.

Possible 3 Follow-up Questions:

1. What data-cleaning techniques most effectively reduce hallucination?
2. How does RLHF mitigate hallucinations?
3. Why do hallucinations increase for long context windows?

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8. (Interview Question 8) What role does synthetic data play in scaling LLMs, and what are the risks?

Key Concept: Synthetic Data, Self-Training, Bootstrapping

Standard Answer:
Synthetic data—model-generated text designed to supplement training—has become a critical component of modern LLM scaling strategies. Frontier labs now use synthetic data to generate instruction-tuning corpora, reasoning chains, code samples, and domain-specific datasets.

The advantages are substantial:

• High consistency: Generated data can follow precise formats and templates.
• Low noise: Unlike web data, synthetic data is usually clean and well structured.
• Cost-effective: Once a strong base model exists, producing high-quality synthetic data is cheaper than scraping or annotating human datasets.
• Infinite generation: Synthetic corpora can be scaled almost arbitrarily.

However, the benefits come with inherent risks:

1. Model collapse
   If synthetic data is generated from the same model being trained, errors compound over iterations. The model “learns its own mistakes,” causing representation drift.

2. Reduced diversity
   Models tend to generate repetitive structures. Training heavily on these patterns compresses diversity and harms generalization.

3. Over-optimization to synthetic formats
   If models overtrain on synthetic reasoning chains or rigid templates, they become brittle and less adaptable.

4. Safety risks
   Synthetic toxic or biased examples can amplify unwanted behaviors if not carefully filtered.

To mitigate these risks, labs use techniques such as:

• Mixing synthetic data with large amounts of real text
• Cross-model generation (e.g., Model A generates data for Model B)
• High-quality filtering, scoring, and deduplication
• Noise injection to preserve diversity
• Human review or RLHF to correct systemic errors

Synthetic data is powerful but must be treated carefully; otherwise it can degrade, rather than enhance, scaling behavior.

Possible 3 Follow-up Questions:

1. How can you detect synthetic data overfitting?
2. What scoring systems help filter synthetic instruction data?
3. Why is cross-model synthetic data safer?

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9. (Interview Question 9) Why do larger models generalize better, and how does data quality influence generalization?

Key Concept: Generalization Dynamics

Standard Answer:
Larger models exhibit better generalization due to increased representational capacity. With more parameters, transformers can encode deeper hierarchies of linguistic structure, semantics, world knowledge, and reasoning patterns. This enables them to learn both surface-level correlations and abstract concepts.

However, parameter count alone does not guarantee better generalization. The interaction between scale and data quality determines the true outcome.

High-quality data improves generalization by:

• Providing stable and consistent patterns
• Offering diverse and rich examples
• Reducing contradictory or misleading signals
• Giving models access to rare edge-case scenarios
• Supporting multilingual and cross-domain coverage

Larger models amplify these benefits because they can extract more complex relationships from high-quality input. Conversely, large models amplify data flaws as well. If the dataset is noisy, biased, or inconsistent, larger models may generalize incorrectly or hallucinate with greater confidence.

Scaling also influences generalization through implicit regularization. Larger models trained on huge corpora tend to converge toward smoother, more robust representations. Surprisingly, overparameterization can improve generalization—this phenomenon is related to the “double descent” curve seen in modern deep learning.

Finally, modern techniques like mixture-of-experts (MoE), larger context windows, rotary embeddings, and retrieval augmentation further increase generalization by giving models access to broader global information.

Generalization is therefore a synergy:
Large models + High-quality diverse data = Strong generalization
Large models + Low-quality data = Confident hallucinations

Possible 3 Follow-up Questions:

1. How does double descent relate to LLM generalization?
2. Why do larger models hallucinate more confidently than smaller models?
3. How does retrieval augmentation influence generalization?

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10. (Interview Question 10) What is the compute–data–model triad, and why does modern LLM performance depend on balancing all three?

Key Concept: Compute–Data–Model Balance

Standard Answer:
Modern LLM performance is determined by three interdependent components: compute, data, and model architecture/size. These three must be balanced to achieve optimal performance, cost, and generalization.

• Compute: FLOPs determine how much training the model can undergo. Too little compute results in underfitting; too much compute with low-quality data wastes resources.
• Data: High-quality, diverse, well-curated data provides the signals required for learning. Data bottlenecks are now a core limitation for frontier training.
• Model: The number of parameters influences representational capacity, reasoning ability, and abstraction depth.

The triad explains why simply scaling one dimension is not enough. Scaling models without scaling data leads to under-trained, unreliable models. Scaling data without sufficient compute fails to converge. Scaling compute without improving data quality wastes resources.

The triad also provides a framework for trade-offs:

• If compute is limited, improve data quality to raise effective compute.
• If data is limited, use synthetic augmentation or curriculum learning.
• If model size is limited, improve tokenizer efficiency or training strategies.
• If parameters are expensive at inference time, distill to smaller models while preserving quality.

Modern training strategies—MoE models, retrieval-augmented training, synthetic knowledge distillation, curated corpora—are all designed to rebalance the triad without incurring unreasonable cost.

In summary, scaling LLMs today is no longer about “bigger is better.” It is about balanced scaling, where compute, data, and model size reinforce one another to produce stable, reliable, and powerful AI systems.

Possible 3 Follow-up Questions:

1. How would you design a training run if compute is fixed but data is constrained?
2. When does scaling model size stop producing returns?
3. Why is the triad critical for cost-efficient inference?