[D] ACL ARR Jan 2026 Reviews

2026-02-16 · Source: Machine Learning · Field: Technology & Digital — Artificial Intelligence & Machine Learning · Depth: Advanced, quick

Summary

Multiple authors on a Reddit thread are discussing their review scores for the ACL ARR Jan 2026 submission cycle, seeking opinions on their chances for acceptance or "findings." Initial scores reported include average overall assessments (OA) ranging from 2.33 to 3.67, with corresponding average confidence scores between 3.33 and 3.67. For instance, one user received OA scores of 2, 2.5, 2.5 and confidence scores of 4, 4, 3. Another reported OA scores of 3, 3, 4 with confidence 3, 4, 4. The discussions revolve around whether these scores are sufficient for "mains" (main conference) or "findings" (a specific track), and strategies for rebuttal or resubmission are being considered.

Key takeaway

For research scientists submitting to competitive venues like ACL ARR, carefully analyze your average Overall Assessment and reviewer Confidence scores immediately upon receipt. If your average OA is below 3, focus your rebuttal on addressing reviewer concerns to improve scores by at least 0.5 points, particularly for those below 3. Be prepared to consider resubmission if scores are consistently low, as this may be a more viable path than a difficult rebuttal.

Key insights

ACL ARR Jan 2026 submitters are evaluating their review scores to gauge acceptance odds.

Principles

• Average OA scores below 3 often indicate a need for strong rebuttal or resubmission.
• Higher confidence scores from reviewers can strengthen a submission's standing.

Method

Authors analyze their average Overall Assessment (OA) and reviewer Confidence scores to predict acceptance outcomes for ACL ARR submissions, often considering rebuttal strategies.

In practice

• Aim to raise scores below 3 by 0.5 to 1 point during rebuttal.
• Prepare for resubmission if initial scores are consistently low.

Topics

• ACL Rolling Review
• Peer Review Scores
• Conference Submissions
• Machine Learning Research
• Rebuttal Strategy

Best for: AI Scientist, Research Scientist, AI Researcher, AI Student, NLP Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Machine Learning.