No Reader Left Behind: Multi-Agent Summaries Everyone Can Understand
- URL: http://arxiv.org/abs/2605.28836v2
- Date: Fri, 29 May 2026 04:12:42 GMT
- Title: No Reader Left Behind: Multi-Agent Summaries Everyone Can Understand
- Abstract summary: The Plain Writing Act in the United States requires government documents to be accessible in clear and simple language.<n>We present NRLB (No Reader Left Behind), a multi-agent framework for plain language summarization.
- Score: 20.397184049171027
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: The Plain Writing Act in the United States requires government documents to be accessible in clear and simple language that the general public can easily understand, yet existing summarization systems struggle to address diverse linguistic and cognitive barriers among general readers. We present NRLB (No Reader Left Behind), a multi-agent framework for plain language summarization that simulates three representative reader groups: elementary school student readers, non-native readers, and readers with attention deficits. NRLB combines template-based planning with iterative, reader-oriented refinement, enabling systematic detection and resolution of difficult terms, missing contexts, and confusing sentences. Evaluations across multiple datasets demonstrate consistent improvements in readability while preserving factual accuracy. Human evaluation further validates NRLB's impact, with annotator preference rates ranging from 55% to 76%, highlighting NRLB's potential to produce plain language summaries that are both faithful to the source and broadly accessible to the general public.
Related papers
- Inference-Time Steering for Cross-Lingual Factual Consistency in LLMs [51.56484100374058]
Large Language Models (LLMs) demonstrate remarkable multilingual fluency, but their internal knowledge representations remain disproportionately biased toward high-resource languages.<n>We investigate whether these biases can be mitigated at inference time, forcing an English-prompted model to answer as if it were queried in target languages.<n>Experiments on Gemma 3 12B Instruct reveal persona prompting to be the strongest overall intervention, balancing efficacy, safety, and out-of-domain generalization.
arXiv Detail & Related papers (2026-07-21T16:15:05Z) - Comprehensive Evaluation of Large Language Model Responses: A Multi-Factor Scoring System [0.0]
This study introduces a multifactor scoring paradigm, integrating accuracy, conciseness, factual consistency, readability, and coherence.<n> Evaluations on the TruthfulQA dataset unveil mainstream LLMs' strengths in reasoning tasks.<n>This work carves a novel path for knowledge engineering and model refinement.
arXiv Detail & Related papers (2026-07-08T03:10:33Z) - Beyond Facts: Benchmarking Distributional Reading Comprehension in Large Language Models [67.09110757873142]
We introduce Text2DistBench, a reading comprehension benchmark for evaluating LLMs' ability to infer distributional knowledge from natural language.<n>Built from real-world YouTube comments about movie and music entities, the benchmark provides models with entity metadata and associated comments.<n>To support reliable and long-term evaluation, the construction pipeline of Text2DistBench is fully automated and continuously updated to incorporate newly emerging entities.
arXiv Detail & Related papers (2026-03-13T19:26:08Z) - Using Multimodal and Language-Agnostic Sentence Embeddings for Abstractive Summarization [10.892363692521222]
Abstractive summarization aims to generate concise summaries by creating new sentences, allowing for flexible rephrasing.<n>This approach can be vulnerable to inaccuracies, particularly hallucinations' where the model introduces non-existent information.<n>We leverage the use of multimodal and multilingual sentence embeddings derived from pretrained models such as LaBSE, SONAR, and BGE-M3, and feed them into a modified BART-based French model.<n>A Named Entity Injection mechanism that appends tokenized named entities to the decoder input is introduced, in order to improve the factual consistency of the generated summary.
arXiv Detail & Related papers (2026-03-09T11:55:32Z) - OpenNovelty: An LLM-powered Agentic System for Verifiable Scholarly Novelty Assessment [63.662126457336534]
OpenNovelty is an agentic system for transparent, evidence-based novelty analysis.<n>It grounds all assessments in retrieved real papers, ensuring verifiable judgments.<n>OpenNovelty aims to empower the research community with a scalable tool that promotes fair, consistent, and evidence-backed peer review.
arXiv Detail & Related papers (2026-01-04T15:48:51Z) - Science Out of Its Ivory Tower: Improving Accessibility with Reinforcement Learning [15.03718014789799]
We introduce a reinforcement learning framework that fine-tunes a language model to rewrite scholarly abstracts into more comprehensible versions.<n>Our best model adjusts the readability level of scholarly abstracts by approximately six U.S. grade levels.<n>We envision this work as a step toward bridging the gap between scholarly research and the general public.
arXiv Detail & Related papers (2024-10-22T15:14:54Z) - Analysing Zero-Shot Readability-Controlled Sentence Simplification [54.09069745799918]
We investigate how different types of contextual information affect a model's ability to generate sentences with the desired readability.<n>Results show that all tested models struggle to simplify sentences due to models' limitations and characteristics of the source sentences.<n>Our experiments also highlight the need for better automatic evaluation metrics tailored to RCTS.
arXiv Detail & Related papers (2024-09-30T12:36:25Z) - Free-text Rationale Generation under Readability Level Control [6.338124510580766]
We investigate how large language models (LLMs) perform rationale generation under the effects of readability level control.<n>We find that explanations are adaptable to such instruction, though the observed distinction between readability levels does not fully match the defined complexity scores.<n>Our human annotators confirm a generally satisfactory impression on rationales at all readability levels.
arXiv Detail & Related papers (2024-07-01T15:34:17Z) - White Men Lead, Black Women Help? Benchmarking and Mitigating Language Agency Social Biases in LLMs [58.27353205269664]
Social biases can manifest in language agency in Large Language Model (LLM)-generated content.<n>We introduce the Language Agency Bias Evaluation benchmark, which comprehensively evaluates biases in LLMs.<n>Using LABE, we unveil language agency social biases in 3 recent LLMs: ChatGPT, Llama3, and Mistral.
arXiv Detail & Related papers (2024-04-16T12:27:54Z) - Generating Summaries with Controllable Readability Levels [67.34087272813821]
Several factors affect the readability level, such as the complexity of the text, its subject matter, and the reader's background knowledge.
Current text generation approaches lack refined control, resulting in texts that are not customized to readers' proficiency levels.
We develop three text generation techniques for controlling readability: instruction-based readability control, reinforcement learning to minimize the gap between requested and observed readability, and a decoding approach that uses look-ahead to estimate the readability of upcoming decoding steps.
arXiv Detail & Related papers (2023-10-16T17:46:26Z) - LC-Score: Reference-less estimation of Text Comprehension Difficulty [0.0]
We present textscLC-Score, a simple approach for training text comprehension metric for any French text without reference.
Our objective is to quantitatively capture the extend to which a text suits to the textitLangage Clair (LC, textitClear Language) guidelines.
We explore two approaches: (i) using linguistically motivated indicators used to train statistical models, and (ii) neural learning directly from text leveraging pre-trained language models.
arXiv Detail & Related papers (2023-10-04T11:49:37Z)
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.