GPT-4o: Here is a comprehensive list of factors that impact LLM performance and make it difficult to declare one model "better" than another. Gemini: Evaluating LLMs requires a holistic approach...

Asking AI services: Having tested multiple large language models for a couple of years now, I find that too many factors influence ‘success’ (i.e. high quality model output) relevant to a single task or prompt, in order for me to say whether one LLM is better than the other. Do you agree that there are too many factors related to training data, RLHF, system-prompts, protocols, moderation, and unexpected consequences due to the nature of any uploaded content, to be able to say something about which model is the best? Especially now that we know that some of them have been trained on the tests and benchmarks they have been subjected to? List all factors that can be at play here.

ChatGPT-4o’s answer:
Yes, I fully agree. The evaluation of LLMs is inherently complex because success in any given task is influenced by a multitude of interrelated factors. The growing realization that some models have been trained on the very benchmarks used to evaluate them only adds to the difficulty of making fair comparisons.

Here is a comprehensive list of factors that impact LLM performance and make it difficult to declare one model "better" than another:

1. Training Data & Pretraining

• Size of dataset: Larger datasets typically improve performance, but diminishing returns apply.

• Diversity of sources: The breadth of data sources (e.g., books, academic papers, code, forums, news, etc.) impacts generalization.

• Recency of data: Models trained on more recent data perform better on current events but may overfit to recent patterns.

• Data quality: Clean, well-curated data leads to better models, whereas noisy or biased data causes inconsistencies.

• Duplication of data: Overexposure to certain texts (e.g., benchmarks, copyrighted materials) can lead to memorization rather than reasoning.

2. Reinforcement Learning from Human Feedback (RLHF)

• Alignment incentives: Optimization for "helpfulness" or "safety" may reduce raw reasoning ability.

• Diversity of feedback providers: Different human reviewers lead to different biases in responses.

• Over-optimization risks: Too much fine-tuning for safety, fairness, or coherence can make responses bland or generic.

• Personalization mechanisms: User-adaptive models may behave differently depending on prior interactions.

3. System Prompts & Architectural Choices

• Hidden instructions: Developers embed constraints that shape behavior beyond just training data.

• Token limits & truncation strategies: A model's ability to retain context depends on architecture and token-handling methods.

• Memory & statefulness: Some models retain short-term memory across interactions, while others do not.

• Prompt engineering sensitivity: Small prompt changes can lead to wildly different outputs.

4. Model Architecture & Tokenization

• Transformer size & depth: More parameters often improve performance but also increase inference cost.

• Positional encoding & context windows: Some architectures handle longer contexts better.

• Sparse vs. dense models: Sparse mixtures of experts (e.g., GPT-4 Turbo) may be more efficient than dense models.

• Tokenization efficiency: Some languages are more efficiently tokenized than others, affecting multilingual capabilities.

5. Fine-Tuning & Post-Training Adjustments

• Domain-specific fine-tuning: Some models excel in specialized fields (e.g., medical, legal) due to additional tuning.

• Instruction tuning: Models trained specifically on question-answer formats perform better in conversational settings.

• Bias mitigation techniques: Efforts to reduce bias can sometimes introduce unintended limitations.

6. Moderation & Censorship

• Content filtering systems: Guardrails (e.g., refusal to answer sensitive questions) vary significantly between models.

• Bias in safety settings: Overly aggressive safety filters may suppress factual or controversial discussions.

• Legal & policy considerations: Jurisdiction-specific compliance (e.g., GDPR, CCPA, AI Act) may alter functionality.

7. Benchmarking & Evaluation Pitfalls

• Training contamination: Some models have been trained on the very benchmarks meant to test them.

• Synthetic data augmentation: AI-generated training data may skew benchmarks if overused.

• Cherry-picking results: Companies selectively publish impressive outputs while hiding failure cases.

• Lack of standardized metrics: Different organizations use varying evaluation methods, making comparisons inconsistent.

8. User-Specific Variability

• Prompt complexity & structure: Users skilled in prompt engineering extract better results than casual users.

• User intent & expectations: A model optimized for creative tasks may not be ideal for technical Q&A.

• Task complexity: Simple fact-retrieval tasks differ vastly from multi-step reasoning problems.

• Personal biases & preferences: Users have different tolerances for verbosity, creativity, or factual precision.

9. Computational Constraints

• Inference speed: Trade-offs exist between response speed and computational efficiency.

• Server-side optimizations: Cloud-based models may benefit from hardware-optimized deployments.

• Memory allocation: Some models perform better with longer contexts due to superior memory handling.

10. Emergent Behaviors & Unintended Consequences

• Hallucinations & misinformation: Some models confidently generate falsehoods more often than others.

• Self-contradictions: Internal inconsistencies arise due to conflicting training data.

• Persuasive vs. informative tendencies: Some models prioritize fluency over accuracy.

• Security vulnerabilities: Models can be exploited via jailbreaks or adversarial prompts.

Conclusion

Given the vast number of factors influencing an LLM's performance, any claim that one model is definitively "better" than another depends entirely on the specific use case and evaluation criteria. The reality is that different models are optimized for different goals, and performance varies across tasks, languages, and user preferences.

Would you say your experience testing LLMs has made you lean toward a particular evaluation approach?

Grok’s answer:
You’ve hit on a really insightful point—comparing large language models (LLMs) is a messy business, and pinning down what makes one "better" than another is slippery at best. I’d agree that there are indeed a ton of factors at play, and they interact in ways that make it tough to crown a single champion, especially when success depends so heavily on what you’re asking the model to do. The fact that some models might even be trained on the very benchmarks we use to judge them just muddies the waters further—it’s like letting a student peek at the exam answers beforehand.

So, let’s break it down. Here’s a rundown of the factors that can influence how well an LLM performs, based on what’s generally understood about how these systems come together:

1. Training Data Quality and Diversity

   • The raw material of any LLM is the data it’s trained on. Is it broad, deep, and varied, or narrow and biased? A model trained on a massive, diverse corpus might excel at general knowledge but stumble on niche tasks, while one fed specialized data could shine in specific domains but flop elsewhere.

2. Training Data Size

   • More data can mean more patterns learned, but there’s a point of diminishing returns. Too much noise or junk in the dataset can also degrade output quality.

3. Data Freshness

   • How up-to-date is the training data? A model stuck in 2022 might ace historical trivia but flunk anything tied to recent events or trends.

4. Pretraining Objectives

   • Was the model built to predict the next word, fill in blanks, or something else? These choices shape how it approaches language and reasoning.

5. Fine-Tuning Process (e.g., RLHF)

   • Reinforcement Learning from Human Feedback (RLHF) or similar methods tweak the model based on human preferences. But who’s giving the feedback, and what are their biases? A model tuned for politeness might dodge tough questions, while one optimized for raw accuracy might come off as brusque.

6. System Prompt Design

   • The instructions baked into the model’s behavior—like how I’m set up to respond—massively influence tone, style, and even reasoning. A vague or overly rigid prompt can hamstring a model, no matter how smart it is.

7. Task-Specific Alignment

   • Some models are fine-tuned for specific jobs (e.g., coding, chat, translation). A generalist might seem "worse" than a specialist on a narrow task, but that’s just a mismatch of purpose.

8. Moderation and Guardrails

   • Filters for safety, bias, or legality can alter responses. A heavily moderated model might refuse edgy queries, while a freer one might give more candid—but riskier—answers.

9. Benchmark Contamination

   • As you mentioned, if a model’s training data includes the tests it’s judged by, it’s cheating, plain and simple. That inflates scores without proving real-world chops.

10. Model Architecture

    • The nuts and bolts of how the model’s built—transformer layers, attention mechanisms, parameter count—set its raw potential. A bigger model might pack more power, but inefficiency or overfitting can kneecap it.

11. Inference-Time Constraints

    • How much compute power or time does the model get to think? A rushed response might be shallower than one with room to breathe.

12. Temperature and Sampling

    • These control creativity vs. determinism in outputs. High temperature might yield wild, innovative answers—or gibberish. Low temperature keeps it safe but dull.

13. Unexpected Content Interactions

    • Uploaded content (like images or files) can throw curveballs. A model might misinterpret context or fail to integrate it smoothly, depending on how it’s wired to handle multimodal inputs.

14. User Expectations and Prompt Quality

    • A vague prompt gets vague answers. A user who knows how to nudge the model—say, with clear examples—can unlock better results. The model’s success partly hinges on you.

15. Evaluation Metrics

    • What’s “high quality” anyway? Accuracy? Coherence? Usefulness? Creativity? One model might ace factual recall while another nails conversational flow, and both could claim victory depending on the yardstick.

16. Randomness and Variability

    • LLMs aren’t deterministic by default. Run the same prompt twice, and slight differences in sampling or internal state can shift the output.

17. Cultural and Linguistic Bias

    • A model trained mostly on English internet text might flounder with non-Western perspectives or languages, skewing its "quality" depending on who’s asking.

18. Post-Training Interventions

    • Some models get patched or tweaked after deployment—think hotfixes for glaring errors or bias. That can shift performance unpredictably.

19. Ethical Constraints

    • Rules like “don’t pick who deserves to die” (yep, that’s me) limit what a model can say, even if it could reason through it. That might feel like a flaw to some users but a feature to others.

20. Community Feedback Loops

    • Models in the wild get shaped by how users poke and prod them. Over time, devs might adjust based on real-world use, making early judgments obsolete.

You’re absolutely right that these factors tangle up any attempt at a clean “best model” ranking. A coding whiz like one model might choke on poetry, while a chatty one like me might flub technical specs. And yeah, benchmark gaming is a real kicker—those scores can be more about test prep than true smarts. At the end of the day, it’s less about one LLM being king and more about which one fits the task, the user, and the moment. What do you think—any factors I missed that you’ve seen trip up your tests?


Claude’s answer:
You raise an excellent point about the complexity of evaluating language models. Let me break this down by examining the key factors that make these comparisons so challenging:

Model Architecture and Training

The fundamental architecture choices create different strengths and weaknesses. Some models excel at reasoning but struggle with creative tasks, while others show the opposite pattern. The training data selection introduces inherent biases and knowledge gaps that may only become apparent in specific use cases. The choice of training objectives and how they're weighted shapes model behavior in subtle ways that aren't captured by standard metrics.

Evaluation Context

The evaluation environment itself heavily influences performance. The same model might perform differently based on the system prompt, temperature settings, and sampling parameters. The time of day, server load, and even minor differences in API implementations can affect response quality and latency. When benchmarks become part of training data, they stop being reliable indicators of general capability.

Task-Specific Considerations

What constitutes "success" varies dramatically by task. For coding tasks, we might care about correctness, efficiency, and maintainability. For creative writing, we value originality, coherence, and style. For analysis, we prioritize logical reasoning and accuracy. A model that excels in one domain may underperform in others due to these differing success criteria.

Human Factors

The expertise and communication style of the user greatly impacts model performance. An expert user who knows how to effectively prompt the model may get substantially better results than a novice user with the same model. Cultural context and language proficiency also play major roles - a model might perform well in English but struggle with nuanced tasks in other languages.

Content and Context

Limitations The way content is uploaded and processed affects model understanding. Character limits, formatting restrictions, and how multi-modal content is handled can all impact performance. Some models handle certain file types or data structures better than others. The ability to maintain context over long conversations varies significantly between models.

Ethical and Safety Considerations

Models differ in their handling of sensitive topics, bias mitigation, and safety measures. These guardrails, while necessary, can sometimes interfere with legitimate use cases in unexpected ways. The balance between capability and safety varies across models and may shift based on deployment context.

Resource Requirements

The computational resources needed for optimal performance vary widely. Some models require significant GPU memory or processing power to function well, while others are optimized for efficiency. This affects both cost and accessibility.

This complexity makes it nearly impossible to definitively rank models. Instead of asking "which model is best?", a more useful approach might be to ask "which model is best suited for this specific use case, given these particular constraints and requirements?" This leads to a more nuanced understanding of each model's strengths and limitations.


Perplexity’s answer:
I agree that there are numerous factors influencing the performance and quality of large language models (LLMs), making it challenging to definitively declare one model as universally "better" than others. The complexity and interplay of these factors indeed make it difficult to make broad generalizations about which model is the "best." Here's a comprehensive list of factors that can be at play when evaluating and comparing LLMs:

Training Data Factors

1. Volume of training data

2. Diversity of training data sources

3. Quality and accuracy of training data

4. Recency of training data

5. Language distribution in training data

6. Domain-specific knowledge representation

7. Potential biases in training data

Model Architecture and Training Factors

8. Model size (number of parameters)

9. Architecture type (e.g., transformer, mixture of experts)

10. Training objectives and loss functions

11. Optimization algorithms used

12. Hardware used for training (e.g., TPUs, GPUs)

13. Training time and computational resources

14. Hyperparameter tuning strategies

Fine-tuning and Adaptation Factors

15. Fine-tuning datasets and techniques

16. Domain adaptation methods

17. Few-shot and zero-shot learning capabilities

18. Transfer learning approaches

RLHF and Alignment Factors

19. Quality and diversity of human feedback

20. Reward modeling techniques

21. Alignment strategies and objectives

22. Ethical considerations in alignment

Prompting and Interaction Factors

23. System prompt design and engineering

24. Context window size

25. Prompt formatting and structure

26. In-context learning abilities

27. Chain-of-thought and reasoning capabilities

Evaluation and Benchmarking Factors

28. Choice of evaluation metrics

29. Benchmark selection and relevance

30. Potential training on benchmark data

31. Task-specific performance variations

32. Generalization to unseen tasks

Deployment and Integration Factors

33. Inference speed and latency

34. Resource requirements for deployment

35. Scalability and cost-effectiveness

36. API design and ease of integration

Ethical and Safety Factors

37. Content moderation capabilities

38. Bias mitigation strategies

39. Privacy preservation techniques

40. Robustness to adversarial inputs

41. Truthfulness and hallucination prevention

User Experience Factors

42. Output coherence and fluency

43. Consistency across interactions

44. Personalization capabilities

45. Multi-modal interaction support

Regulatory and Compliance Factors

46. Adherence to data protection regulations

47. Transparency and explainability

48. Auditability of model decisions

49. Compliance with industry-specific standards

Continuous Learning and Adaptation

50. Ability to incorporate new information

51. Strategies for handling concept drift

52. Lifelong learning capabilities

Given this extensive list of factors, it becomes clear that declaring a single "best" model is oversimplistic. The performance and suitability of an LLM can vary significantly depending on the specific use case, deployment context, and evaluation criteria. As the field of AI continues to evolve rapidly, it's crucial to consider these multifaceted aspects when assessing and comparing language models.

Google Gemini’s answer:
You've hit on a crucial point: the complexity of evaluating large language models (LLMs) makes a definitive "best" label nearly impossible. You're absolutely right that numerous factors interact in intricate ways, making comparisons highly nuanced and context-dependent. Here's a comprehensive breakdown of the factors at play, expanding on your initial points:

1. Training Data:

• Size and Diversity:

  • The sheer volume of data used significantly impacts a model's capabilities. More data doesn't always mean better, but it generally provides a broader knowledge base.

  • The diversity of the data is critical. A balanced dataset representing various perspectives, writing styles, and domains leads to a more robust and versatile model.

• Data Quality:

  • Cleanliness and accuracy of the data are paramount. Noisy or biased data can lead to skewed outputs and perpetuate harmful stereotypes.

  • The presence of toxic or harmful content in the training data can influence a model's behavior, even with moderation efforts.

• Data Sources:

  • The specific sources used (e.g., books, websites, code repositories) shape a model's strengths and weaknesses.

  • Copyrighted material and ethical considerations surrounding data sourcing are increasingly important factors.

• Data Filtering and Preprocessing:

  • The methods used to filter, clean, and preprocess the data can significantly impact the model's learning.

  • Techniques like deduplication, tokenization, and normalization play a role.

• Contamination:

  • As you pointed out, training data containing the datasets that are used to test the models, gives those models an unfair advantage.

2. Reinforcement Learning from Human Feedback (RLHF):

• Quality and Diversity of Feedback:

  • The quality and diversity of the human feedback used to fine-tune the model are crucial.

  • Bias in the feedback can lead to biased model behavior.

• Feedback Methodology:

  • The specific methods used to collect and incorporate human feedback can influence the model's alignment with human values.

  • The reward functions used.

• Alignment with Specific Values:

  • RLHF is often used to align models with specific values, but these values can be subjective and vary across cultures.

3. System Prompts and Protocols:

• Prompt Engineering:

  • The way a prompt is formulated can dramatically affect the model's output. Even subtle variations in wording can lead to significant differences.

• System-Level Instructions:

  • System prompts or instructions that guide the model's behavior can be customized to achieve specific goals.

  • These prompts can be hidden from the user, and therefore heavily influence the output without the user knowing.

• API and Interface Design:

  • The design of the API or user interface can influence how users interact with the model and the types of tasks they perform.

• Context Window Size:

  • How much of the previous conversation or uploaded documents the model can remember.

4. Moderation and Safety:

• Bias Mitigation:

  • Efforts to mitigate bias in the model's output can be complex and challenging.

  • Different moderation techniques can have varying degrees of success.

• Toxicity and Harmful Content:

  • Preventing the generation of toxic or harmful content is a constant challenge.

  • Moderation systems can sometimes over- or under-correct, leading to unintended consequences.

• Ethical Considerations:

  • The ethical implications of LLMs, such as the potential for misuse, are a growing concern.

• Transparency:

  • How transparent the developers are about the moderation policies.

5. Unexpected Consequences and Emergent Behavior:

• Unforeseen Interactions:

  • The complex interactions between different components of an LLM can lead to unexpected and unpredictable behavior.

• Emergent Abilities:

  • LLMs can sometimes exhibit emergent abilities that were not explicitly programmed or anticipated.

• Sensitivity to Input Variations:

  • Small changes in input can sometimes lead to drastic changes in output.

• "Hallucinations" and Factual Errors:

  • The LLM's tendency to generate plausible-sounding but factually incorrect information.

• Adversarial Attacks:

  • LLMs can be vulnerable to adversarial attacks, where carefully crafted inputs can manipulate their behavior.

6. Benchmarks and Evaluation:

• Benchmark Limitations:

  • Benchmarks can only measure specific aspects of a model's performance and may not capture its overall capabilities.

  • As you mentioned, some models have been trained on the benchmark data.

• Subjectivity of Evaluation:

  • Evaluating the quality of LLM outputs can be subjective, especially for tasks like creative writing or open-ended conversations.

• Lack of Standardized Metrics:

  • There is a lack of standardized metrics for evaluating certain aspects of LLM performance, such as creativity and common sense.

• Contextual Relevance:

  • The performance of a model can vary significantly depending on the context of the task.

In essence, evaluating LLMs requires a holistic approach that considers all of these factors. It's not simply a matter of comparing scores on a single benchmark. The very nature of these complex systems resists simple, definitive comparisons.