English for Machine Learning Researchers: Paper Reading and Presentation
ML paper vocabulary: ablation study, baseline, SOTA, experimental setup, and academic ML communication in English.
If you work in machine learning and want to engage with the global research community — reading papers, attending conferences, presenting your own work, or contributing to peer review — you need more than technical knowledge. You need the specific academic English that ML researchers use every day.
This vocabulary is highly specialised. Phrases like “we outperform the baseline” or “we evaluate on three benchmark datasets” appear constantly in papers, talks, and code reviews. If you encounter them for the first time during a conference Q&A, the result can be embarrassing. This guide covers the most important terms, with real-world conversation examples to help you use them naturally.
Core Terms: The Building Blocks of an ML Paper
baseline — the simplest or most established model you compare your new approach against. A baseline is your reference point: if your model cannot beat it, the contribution is questionable.
“Before we optimise anything, let’s make sure our baseline is solid. A weak baseline inflates how impressive our results look.”
“Their paper doesn’t specify which baseline they used, so it’s hard to evaluate whether the improvement is meaningful.”
state-of-the-art (SOTA) — the best-performing method currently known for a given task. Claiming SOTA means your results are better than everything previously published.
“We achieve state-of-the-art on ImageNet top-1 accuracy, outperforming the previous SOTA by 1.3 percentage points.”
“SOTA changes fast in NLP. A paper can be SOTA in January and beaten three times over by March.”
benchmark dataset — a standard, publicly available dataset used by the community to compare models fairly. Common examples include ImageNet, GLUE, and MS COCO.
“We evaluate on three benchmark datasets to demonstrate the generalisation of our approach.”
“The problem with relying on a single benchmark dataset is that you might be over-tuning to its quirks.”
ablation study — an experiment where you systematically remove or disable individual components of your model to measure how much each one contributes to performance. Think of it as reverse engineering your own system.
“The ablation study shows that removing the attention layer drops accuracy by 4.2%, which confirms it’s the most critical component.”
“Reviewers always ask for ablations. If you don’t have them, expect a rejection or at least a major revision.”
evaluation metric — the quantitative measure used to judge model performance. Common metrics include accuracy, F1 score, BLEU, ROUGE, mAP, and perplexity, depending on the task.
“We use F1 as our primary evaluation metric because the dataset is heavily imbalanced and accuracy would be misleading.”
“Make sure your evaluation metric actually captures what matters for the downstream task, not just what’s easy to compute.”
Experimental Setup and Training Language
experimental setup — the full description of how experiments were conducted: hardware, datasets, preprocessing steps, hyperparameters, and training procedure. Good experimental setup means others can reproduce your work.
“We detail the experimental setup in Appendix A, including the exact random seeds used for all runs.”
“Their experimental setup is vague. They mention ‘standard preprocessing’ without specifying what that means.”
hyperparameter search — the process of systematically trying different values for hyperparameters (learning rate, batch size, dropout rate, etc.) to find the combination that produces the best results.
“We performed a grid hyperparameter search over learning rates in {1e-3, 1e-4, 1e-5} and batch sizes in {16, 32, 64}.”
“Without a proper hyperparameter search, you can’t be sure your baseline is as strong as it could be.”
learning curve — a graph showing how model performance (or training loss) changes as a function of training steps or the amount of training data. A healthy learning curve descends smoothly and levels off.
“Looking at the learning curve, the model converges around epoch 15 and shows minimal improvement after that.”
“If your learning curve is still dropping sharply at the end of training, you probably need more epochs or more data.”
convergence — the point at which a model’s training loss (or validation metric) stabilises and stops improving significantly. A model that has converged is considered fully trained.
“We trained until convergence, defined as fewer than 0.01% improvement over five consecutive epochs.”
“The model failed to converge with a learning rate of 0.1 — we had to reduce it by an order of magnitude.”
overfitting / underfitting — two classic failure modes. Overfitting means the model memorises the training data but performs poorly on unseen data. Underfitting means the model is too simple to capture the underlying patterns.
“The gap between training and validation accuracy suggests overfitting. We added dropout and L2 regularisation to address this.”
“With only two layers, the model was clearly underfitting — it couldn’t even match the training set performance.”
Writing and Presenting Your Contribution
contribution statement — the explicit list of what your paper adds to the field. Usually found near the end of the introduction, often as a bulleted list starting with “Our contributions are as follows.”
“A clear contribution statement helps reviewers understand what you’re claiming. Don’t bury your novelty in the middle of a paragraph.”
“Our contributions are: (1) a new attention mechanism for long-document summarisation, (2) a curated benchmark dataset of 50,000 samples, and (3) a thorough ablation study validating each design choice.”
“we outperform” — the standard phrase used when your model beats competing methods. Always specify what you outperform and by how much.
“We outperform the previous state-of-the-art on three out of four benchmarks, with an average improvement of 2.7 F1 points.”
“Saying ‘we outperform existing methods’ without citing specific numbers is a red flag in peer review.”
“we evaluate on” — the standard phrase for stating which datasets or tasks you tested your model on. It signals rigour and scope.
“We evaluate on both in-domain and out-of-domain test sets to assess generalisation.”
limitations section — a section (increasingly required by major venues) where authors honestly describe what their method does not do well, where it may fail, and what assumptions it relies on.
“I actually respect papers more when the limitations section is detailed. It shows the authors understand their own work.”
“Our method has two key limitations: it requires labelled data for fine-tuning, and inference time scales quadratically with sequence length.”
reproducing results — repeating someone else’s experiments to verify their reported numbers. Reproducibility is a major concern in ML research.
“We were unable to reproduce their results using the code and hyperparameters provided in the supplementary material.”
“To facilitate reproducing results, we release all training code, model checkpoints, and the exact configuration files used in our experiments.”
How to Use These in Conversation
Academic ML English is more formal than day-to-day engineering talk, but it still has a natural rhythm. Here are some common situations and how to phrase things:
During a paper discussion:
“I’m not convinced the baseline is fair here — they’re comparing against a 2019 model when there are much stronger 2022 options available.”
“The ablation study is the strongest part of the paper. It really isolates the effect of each component.”
When presenting your own work:
“Our key contribution is a new training objective that improves convergence speed without any additional parameters.”
“We evaluate on four benchmark datasets. On three of them, we outperform the current state-of-the-art. The fourth — and I’ll be honest in the limitations section — is a domain where our approach struggles.”
During peer review:
“I’d ask the authors to clarify the experimental setup. Specifically, was hyperparameter search performed on the validation set or a held-out subset?”
“The learning curves suggest the model hasn’t fully converged. I’d recommend training for additional epochs and re-reporting results.”
When discussing reproducibility:
“Has anyone actually tried reproducing their results? The code is on GitHub but the hyperparameter search details are missing.”
Quick Reference: Key ML Research Terms
| Term | What it means | Typical context |
|---|---|---|
| baseline | Reference model to beat | ”Our model outperforms a strong baseline” |
| SOTA | Best current published result | ”We achieve state-of-the-art on GLUE” |
| benchmark dataset | Standard dataset for fair comparison | ”We evaluate on three benchmark datasets” |
| ablation study | Removing parts to measure their effect | ”The ablation study confirms each component matters” |
| evaluation metric | How performance is measured | ”F1 is our primary evaluation metric” |
| experimental setup | Full description of how tests were run | ”See Appendix B for the experimental setup” |
| hyperparameter search | Systematic tuning of model settings | ”Grid hyperparameter search over learning rate” |
| convergence | When training loss stabilises | ”Trained until convergence at epoch 20” |
| contribution statement | Explicit list of novelties claimed | ”Our contributions are as follows…“ |
| limitations section | Honest description of what doesn’t work | ”We acknowledge two limitations of our approach” |
Mastering this vocabulary will not only help you read ML papers more efficiently — it will make you a more credible and persuasive communicator when you present your own research, respond to reviewers, or collaborate with international teams. The language of ML research is a professional skill worth investing in.