sui-1: Grounded and Verifiable Long-Form Summarization
Large language models frequently generate plausible but unfaithful summaries that users cannot verify against source text, a critical limitation in compliance-sensitive domains such as government and legal analysis. We present sui-1, a 24B parameter model that produces abstractive summaries with inline citations, enabling users to trace each claim to its source sentence. Our synthetic data pipeline combines chain-of-thought prompting with multi-stage verification, generating over 22,000 high-quality training examples across five languages from diverse sources including parliamentary documents, web text, and Wikipedia. Evaluation shows sui-1 significantly outperforms all tested open-weight baselines, including models with 3x more parameters. These results demonstrate that task-specific training substantially outperforms scale alone for citation-grounded summarization. Model weights and an interactive demo are publicly available.
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Benedikt Droste
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2026-01-16T14:37:13.002Z
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Benedikt
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Jan Philipp Harries
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2026-01-16T10:33:08.204Z
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Jan Philipp Harries
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Maximilian Idahl
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2026-01-16T10:33:06.130Z
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Max Idahl
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Björn Plüster
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2026-01-16T10:32:54.925Z
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Björn Plüster
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bjoernp
sui-1: Grounded and Verifiable Long-Form Summarization
Large language models frequently generate plausible but unfaithful summaries that users cannot verify against source text, a critical limitation in compliance-sensitive domains such as government and legal analysis. We present sui-1, a 24B parameter model that produces abstractive summaries with inline citations, enabling users to trace each claim to its source sentence. Our synthetic data pipeline combines chain-of-thought prompting with multi-stage verification, generating over 22,000 high-quality training examples across five languages from diverse sources including parliamentary documents, web text, and Wikipedia. Evaluation shows sui-1 significantly outperforms all tested open-weight baselines, including models with 3x more parameters. These results demonstrate that task-specific training substantially outperforms scale alone for citation-grounded summarization. Model weights and an interactive demo are publicly available.
A 24 billion parameter model generates abstractive summaries with inline citations through synthetic data training, outperforming larger models in accuracy and verifiability.
large language models abstractive summaries inline citations chain-of-thought prompting multi-stage verification synthetic data pipeline task-specific training