ML Conference Paper: [Paper title stating the method or finding]

Machine learning conference paper template: abstract, introduction, related work, method, experiments, results, limitations, broader impact, reproducibility statement, references, appendix and a paper checklist aligned with the NeurIPS 2026 Paper Checklist (16 items), with a mapping note for the ACL Rolling Review Responsible NLP Research checklist. Replace every [bracketed] value and every row marked Example. Always follow the current call for papers, author handbook and style files of your venue, which take precedence over this template.

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Revision history

Contents

1. Abstract
2. 1. Introduction
3. 2. Related work
4. 3. Method
5. 4. Experimental setup
6. 5. Results
7. 6. Limitations
8. 7. Broader impact
9. 8. Conclusion
10. 9. Reproducibility statement
11. 10. References
12. 11. Appendix
13. 12. Paper checklist
14. Standard and licence note

Abstract

[Context: one sentence on the problem and why it matters.] [Gap: what current methods cannot do.] [Approach: what you propose, in one or two sentences.] [Results: the main finding with numbers and the setting.] [Implication: what this enables.]

Example: Retrieval-augmented language models often cite passages that do not support their answers. We introduce [Method], a training objective that rewards answers whose claims are entailed by the cited passage. Across three open-domain question answering benchmarks, [Method] raises citation precision from 71.2% to 84.5% at equal answer accuracy, and the gain holds for models from 1B to 13B parameters. We release code, trained checkpoints and an evaluation set of 2,000 human-verified citations.

1. Introduction

1.1 Problem and motivation

[Describe the problem, a concrete failure example and why it matters in practice or for science. Cite the most relevant prior work.]

1.2 Gap

[State precisely what existing approaches cannot do and why. Refer to section 2 for the detailed comparison.]

1.3 Contributions

1. [Method or theory contribution.] Example: A training objective that rewards citation entailment, with a variance-reduced estimator.
2. [Empirical contribution.] Example: Experiments on three benchmarks and four model sizes showing consistent gains in citation precision.
3. [Resource contribution.] Example: A human-verified evaluation set of 2,000 citations, released under CC BY 4.0.
4. [Analysis contribution.] Example: An analysis of failure cases showing where the objective does not help.

Figure 1 (Example): an overview diagram placed at the top of page 2 showing the pipeline and where the new component fits.

2. Related work

[Theme 1, for example retrieval-augmented generation]. [Two to four sentences summarising the line of work and the key citations, then one sentence on how this paper differs.]

[Theme 2, for example attribution and faithfulness evaluation]. [Summary and difference.]

[Theme 3, for example learning from automated feedback]. [Summary and difference.]

Concurrent work. [Note papers that appeared close to the submission date and how they relate, if any.]

3. Method

3.1 Problem setup and notation

[Define inputs, outputs, the model and the objective.] Example: Given a question $q$ and a set of retrieved passages $P$, the model produces an answer $a$ with citations $c \subseteq P$.

3.2 Approach

[Describe the method step by step. Use an algorithm box in the final LaTeX version if the procedure has more than three steps.]

Example: The training objective combines the standard likelihood with an entailment reward weighted by $\lambda$:

3.3 Theoretical results

[State each theorem or proposition with all its assumptions. Give a proof sketch here and the full proof in the appendix. If the paper has no theory, delete this subsection and answer checklist item 3 with N/A.]

3.4 Design choices

4. Experimental setup

4.1 Datasets

4.2 Baselines

[List each baseline, its source, and how it was tuned. Use the same tuning budget for baselines and the proposed method, or explain why not.]

4.3 Metrics

[Define each metric, how it is computed and why it fits the claim. For human evaluation, describe raters, instructions and agreement.]

4.4 Implementation and hyperparameters

[Framework and versions, optimiser, learning rate schedule, batch size, number of seeds, hyperparameter search space and selection rule. Put the full table in Appendix B.]

4.5 Compute

5. Results

5.1 Main results

Table 1 (Example): citation precision and answer accuracy, mean and standard deviation over 3 seeds.

[State the main finding in one or two sentences, with the comparison and the variability.] State what the error bars capture (for example seeds, data splits or bootstrap over test items), how they were computed, and whether they are standard deviation, standard error or a confidence interval.

5.2 Ablations

[Remove or replace each component and report the effect. Link each row to the design choices table in section 3.4.]

5.3 Analysis

[Scaling behaviour, robustness to distribution shift, qualitative examples, error analysis. Show at least one example where the method fails.]

6. Limitations

• [Assumption and where it may fail.] Example: The entailment scorer is trained on English; gains in other languages are untested.
• [Scope of evaluation.] Example: All benchmarks use Wikipedia passages; domains with conflicting sources may behave differently.
• [Cost.] Example: Training is 1.4 times slower than the baseline because of reward computation.
• [Other limitation.]

7. Broader impact

Positive impacts: [Example: Better-supported citations help users verify answers.]

Potential negative impacts: [Example: Higher citation precision may increase user trust in answers that are still wrong when retrieved sources are wrong.]

Mitigations and safeguards: [Example: The released model card states the scorer's language limits; checkpoints are released under a licence with acceptable-use terms.]

8. Conclusion

[Summarise what the paper showed, the evidence for it, and what remains open.]

9. Reproducibility statement

[One paragraph pointing to the method details (section 3), hyperparameters (Appendix B), compute (section 4.5) and proofs (Appendix A).]

10. References

[1] [Author, A., Author, B. (Year). Title. Venue.]

[2] [Author, C. (Year). Dataset name, version. Repository. DOI.]

[3] [Software name, version. URL.]

11. Appendix

11.1 Appendix A: Proofs

[Full proofs of the results in section 3.3, with all assumptions.]

11.2 Appendix B: Hyperparameters and training details

11.3 Appendix C: Additional results

[Per-dataset breakdowns, extra seeds, further ablations, qualitative examples.]

11.4 Appendix D: Human evaluation and data documentation

[Full rater instructions, interface screenshots, compensation, consent process and ethics approval if human participants were involved. Documentation for new datasets, for example a datasheet; see Datasheet for Datasets (ai-data/datasheet-for-datasets) and Model Card (ai-data/model-card).]

12. Paper checklist

12.1 NeurIPS 2026 Paper Checklist (paraphrased)

12.2 Note on the ACL Responsible NLP Research checklist

Papers submitted through ACL Rolling Review complete the Responsible NLP Research checklist in the submission form instead. It covers the same ground in five parts, summarised here in original words:

Check the current ARR checklist page before each cycle; it is revised between cycles.

Standard and licence note

Structure aligned with the NeurIPS 2026 Paper Checklist and Main Track Handbook (neurips.cc), and with the ACL Rolling Review Responsible NLP Research checklist. Checklist items are paraphrased in original words and are not the official wording; copy the official items from the venue's current style file or submission form when submitting. All other text and examples are original, and example numbers are illustrative. Check the current call for papers, handbook and checklist of your venue before use. Template licence: MIT.