sui-1: Grounded and Verifiable Long-Form Summarization

• URL: http://arxiv.org/abs/2601.08472v1
• Date: Tue, 13 Jan 2026 11:59:15 GMT
• Title: sui-1: Grounded and Verifiable Long-Form Summarization
• Abstract summary: Large language models generate plausible but unfaithful summaries that users cannot verify against source text.<n>We present sui-1, a 24B parameter model that produces abstractive summaries with inline citations.<n>Our synthetic data pipeline combines chain-of-thought prompting with multi-stage verification, generating over 22,000 high-quality training examples.
• Score: 0.10888485668490162
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• Inferring the Size of Large Language Models From Popular Text Memorization [0.989901717499058]
  How accurately a model predicts the next word across text fragments of varying length is a reliable signal of how much it has memorized them.<n>We aggregate this memorization signal across a diverse corpus of texts and fragment lengths into a single accuracy profile vector per model.<n>We build two complementary inference methods on top of it: a pairwise statistical test that determines which of two models is larger, and a scaling-law estimator.
  arXiv  Detail & Related papers  (2026-05-28T01:20:35Z)
• A Reproducible, Scalable Pipeline for Synthesizing Autoregressive Model Literature [0.0]
  Autoregressive generative models have produced thousands of papers, making manual literature surveys and reproduction studies increasingly impractical.<n>We present a fully open-source, reproducible pipeline that automatically retrieves candidate documents from public repositories.<n>Experiments on corpora of up to 1000 papers demonstrate near-linear scalability with eight CPU workers.
  arXiv  Detail & Related papers  (2025-08-06T16:33:20Z)
• Cite Pretrain: Retrieval-Free Knowledge Attribution for Large Language Models [44.31597857713689]
  We introduce Active Indexing for the first stage, which creates generalizable, source-anchored bindings.<n>Experiments with Qwen-2.5-7B&3B show that Active Indexing consistently outperforms a Passive Indexing baseline.<n> internal citations complement external ones by making the model more robust to retrieval noise.
  arXiv  Detail & Related papers  (2025-06-21T04:48:05Z)
• Unstructured Evidence Attribution for Long Context Query Focused Summarization [53.08341620504465]
  We propose to extract unstructured (i.e., spans of any length) evidence in order to acquire more relevant and consistent evidence than in the fixed granularity case.<n>We show how existing systems struggle to copy and properly cite unstructured evidence, which also tends to be "lost-in-the-middle"
  arXiv  Detail & Related papers  (2025-02-20T09:57:42Z)
• Attacking Misinformation Detection Using Adversarial Examples Generated by Language Models [1.8759938228229573]
  We investigate the challenge of generating adversarial examples to test the robustness of text classification algorithms.<n>We focus on simulation of content moderation by setting realistic limits on the number of queries an attacker is allowed to attempt.
  arXiv  Detail & Related papers  (2024-10-28T11:46:30Z)
• AugSumm: towards generalizable speech summarization using synthetic labels from large language model [61.73741195292997]
  Abstractive speech summarization (SSUM) aims to generate human-like summaries from speech. conventional SSUM models are mostly trained and evaluated with a single ground-truth (GT) human-annotated deterministic summary. We propose AugSumm, a method to leverage large language models (LLMs) as a proxy for human annotators to generate augmented summaries.
  arXiv  Detail & Related papers  (2024-01-10T18:39:46Z)
• Split and Rephrase with Large Language Models [2.499907423888049]
  Split and Rephrase (SPRP) task consists in splitting complex sentences into a sequence of shorter grammatical sentences. We evaluate large language models on the task, showing that they can provide large improvements over the state of the art on the main metrics.
  arXiv  Detail & Related papers  (2023-12-18T10:16:37Z)
• On Context Utilization in Summarization with Large Language Models [83.84459732796302]
  Large language models (LLMs) excel in abstractive summarization tasks, delivering fluent and pertinent summaries. Recent advancements have extended their capabilities to handle long-input contexts, exceeding 100k tokens. We conduct the first comprehensive study on context utilization and position bias in summarization.
  arXiv  Detail & Related papers  (2023-10-16T16:45:12Z)
• mFACE: Multilingual Summarization with Factual Consistency Evaluation [79.60172087719356]
  Abstractive summarization has enjoyed renewed interest in recent years, thanks to pre-trained language models and the availability of large-scale datasets. Despite promising results, current models still suffer from generating factually inconsistent summaries. We leverage factual consistency evaluation models to improve multilingual summarization.
  arXiv  Detail & Related papers  (2022-12-20T19:52:41Z)
• Exemplar-Controllable Paraphrasing and Translation using Bitext [57.92051459102902]
  We adapt models from prior work to be able to learn solely from bilingual text (bitext) Our single proposed model can perform four tasks: controlled paraphrase generation in both languages and controlled machine translation in both language directions.
  arXiv  Detail & Related papers  (2020-10-12T17:02:50Z)
• On Faithfulness and Factuality in Abstractive Summarization [17.261247316769484]
  We analyzed limitations of neural text generation models for abstractive document summarization. We found that these models are highly prone to hallucinate content that is unfaithful to the input document. We show that textual entailment measures better correlate with faithfulness than standard metrics.
  arXiv  Detail & Related papers  (2020-05-02T00:09:16Z)
• Unsupervised Opinion Summarization with Noising and Denoising [85.49169453434554]
  We create a synthetic dataset from a corpus of user reviews by sampling a review, pretending it is a summary, and generating noisy versions thereof. At test time, the model accepts genuine reviews and generates a summary containing salient opinions, treating those that do not reach consensus as noise.
  arXiv  Detail & Related papers  (2020-04-21T16:54:57Z)
• Pre-training for Abstractive Document Summarization by Reinstating Source Text [105.77348528847337]
  This paper presents three pre-training objectives which allow us to pre-train a Seq2Seq based abstractive summarization model on unlabeled text. Experiments on two benchmark summarization datasets show that all three objectives can improve performance upon baselines.
  arXiv  Detail & Related papers  (2020-04-04T05:06:26Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.