arXiv — cs.AI preprintsInternational12 October 2026
How Reliable are AMR Parser Outputs? Uncertainty Estimation for AMR Parsing
This is an official announcement record
Firsthand records what arXiv — cs.AI preprints announced and links to the original. The wording below is theirs, not ours.
arXiv:2610.13236v1 Announce Type: cross Abstract: Recent advances have significantly improved the performance of Abstract Meaning Representation (AMR) parsers, yet determining when their outputs can be trusted remains largely unexplored. This work presents the first study of uncertainty estimation for AMR parsers in a black-box setting using top-k beam-search candidates. Uncertainty is estimated from pairwise candidate similarities and evaluated using the Prediction Rejection Ratio. This work characterises the relationship between parsing performance and uncertainty, evaluates the reliability
Read the official announcement
Opens arxiv.org
More from arXiv — cs.AI preprints
- Faithfulness Without Anchors: A Position Paper on the Structural Misalignments of Explainable AI Faithfulness Metrics for Feature Importance12 October 2026
- EliteTable: Self-Improving Program Search for Executable Control Policies12 October 2026
- Equal-Budget Monte Carlo Tree Search versus Randomized-Restart List Scheduling for DAG Workflows: A Controlled Equal-Budget Computational Study12 October 2026
- Know What You Don't Know: Agentic Data Synthesis by Evolving Generation Space for Closed-Loop Learning12 October 2026
- SpireBench: Evaluating Long-Horizon Strategic Play of Agentic Models with Slay the Spire12 October 2026
This content is for informational purposes only and is not professional advice. Specifications, prices, plan tiers, and features change frequently and may differ from what is shown here; verify current details on the manufacturer's or company's official page before purchasing. Ratings are based on analysis of published documentation, not independent lab testing.