Research methods in practice

Two models are evaluated on identical tasks. What should uncertainty estimation preserve?

The task-level pairing, so comparisons use each task's score difference. Independently resampling the two score lists discards shared task difficulty and changes the variance estimate.

What different uncertainties do repeated training seeds and resampled test tasks measure?

Seeds expose variation in the training procedure; task resampling addresses variation in the evaluation population under its assumptions. Reporting only one can omit a major source of uncertainty.

Why is comparing the best of 30 new-method runs with one baseline run misleading?

The new method receives a selection advantage. Compare defined training-and-selection procedures under comparable budgets and report the distribution, not only the chosen maximum.

Why must divergent or OOM runs be accounted for in a method comparison?

Dropping failures changes the evaluated procedure to one conditioned on success. Report failure rates, causes, and the predefined handling rule alongside successful-run quality and cost.

Why can equal optimizer steps be an unfair comparison between two language-model methods?

Token counts, sequence lengths, model size, auxiliary passes, and communication differ. Match or report the relevant compute and data budgets, including tuning cost.

Why should a claim of faster training specify hardware and achieved quality?

Wall-clock speed depends on hardware, implementation, precision, and workload. Compare time or cost to a defined quality threshold with those conditions recorded.

Two components help only when used together. Why can one-at-a-time ablations misdescribe their contribution?

The effect depends on the other component's presence. A factorial comparison of both absent, each alone, and both present can reveal the interaction.

Why can removing a component without retuning produce an ambiguous ablation result?

The remaining system may use hyperparameters suited only to the full method. Distinguish the effect of removal under fixed settings from the best achievable reduced system.

What would a negative control test in an experiment claiming benefit from retrieved evidence?

Replace evidence with irrelevant or shuffled content while holding other factors stable. Unexpected gains can expose formatting, leakage, or evaluation artifacts rather than useful retrieval.

What can an oracle-context evaluation tell you when a RAG system performs poorly?

Whether the generator can solve tasks with sufficient evidence supplied. It helps localize limitations but may overestimate achievable performance if oracle context is unrealistically clean.

Before using an LLM judge to rank two methods, what should be checked on human-labeled examples?

Agreement by error type and subgroup, order and verbosity bias, rubric adherence, and stability. One aggregate agreement score can hide a systematic preference affecting the comparison.

Why can using the same model family as generator and judge bias a comparison?

Shared style preferences or correlated errors may favor familiar outputs. Validate against independent human judgments and alternative judges rather than assuming self-evaluation is neutral.

Why does repeatedly improving prompts against a public benchmark weaken its role as a final test?

The development process adapts to its cases, even without training on them directly. Use a separate held-out evaluation for the final claim and disclose benchmark-driven tuning.

Does finding no exact text overlap establish that a benchmark is uncontaminated?

No. Paraphrases, translated content, answer keys, or shared sources can leak information. Use multiple checks and state the limits of available training-data visibility.

A small study finds no statistically significant difference. What conclusion is unjustified?

That the methods are equivalent. The study may lack precision; report the interval and use a prespecified equivalence margin and appropriate design for an equivalence claim.

Why are striking gains found only after inspecting dozens of subgroups exploratory evidence?

The search selects unusually favorable noise as well as real effects. Disclose the search and confirm the subgroup hypothesis on new data or with an appropriate multiplicity procedure.

Why should a fitted scaling curve not be treated as a guaranteed forecast outside measured budgets?

The data regime, optimization, architecture, or bottleneck may change. Report the fitted range, uncertainty, and assumptions, then test intermediate points before relying on distant extrapolation.

What information makes a language-model result reproducible beyond a repository commit?

Model and tokenizer revisions, data versions and filters, preprocessing, prompts, evaluation code, seeds, decoding settings, dependencies, and hardware-relevant settings. Record unavailable or nondeterministic components explicitly.

A method wins on short English QA tasks. What additional evidence is needed for a broad long-context multilingual claim?

Evaluations that actually vary language, context length, evidence placement, and task type with adequate sample sizes. State the demonstrated scope until those conditions are tested.