arXiv:2603. 27435v2 Announce Type: replace-cross Abstract: Large language models (LLMs) are increasingly being used to generate comprehensive, knowledge-intensive reports.
arXiv:2604. 28076v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) have advanced Table Question Answering, where most queries can be answered by extracting information or simple aggregation.
arXiv:2604. 09497v2 Announce Type: replace-cross Abstract: Accurate evaluation is central to the large language model (LLM) ecosystem, guiding model selection and downstream adoption across diverse use cases.
Large language models increasingly rely on long-form reasoning for complex tasks, yet their reasoning traces may drift away from the supplied context when evidence is sparse, noisy, or in conflict with parametric knowledge. Existing grounding methods either attach citations after generation or encourage evidence retrieval inside the trace, but they often do not ensure that cited content is sufficient for the local inference and final answer.
arXiv:2504. 07385v3 Announce Type: replace-cross Abstract: As Large Language Models (LLMs) become increasingly used for question-answering (QA), relying on static, pre-annotated references for evaluation poses significant challenges in cost, scalability, and completeness.
arXiv:2609.24650v1 Announce Type: new Abstract: Readability assessment is essential for tailoring texts to intended audiences across educational, healthcare, and information retrieval domains. Howeve...
Intent-Driven Dynamic Chunking (IDC) segments documents by predicting user queries with a Large Language Model and then applying dynamic programming to find optimal chunk boundaries. This method outperforms traditional fixed-length or coherence-based segmentation on five out of six question-answering datasets, improving top-1 retrieval accuracy by 5% to 67% and reducing the number of chunks by 40–60% while maintaining 93–100% answer coverage. IDC demonstrates that aligning document structure with anticipated information needs can significantly boost retrieval performance for long and heterogeneous documents.
The paper introduces "question archaeology," an evaluation task that asks models to infer the single, authentic question that motivated a text. It presents a new dataset of commissioned texts paired with their original research questions and distractors, and evaluates both proprietary and open‑source LLMs. Results show newer models outperform older ones, with BERT-based models lagging, and current LLMs even surpassing human performance on this task.
arXiv:2509. 21028v4 Announce Type: replace Abstract: We introduce SciTrek, a synthetic question-answering dataset for assessing and improving long-context numerical reasoning in large language models (LLMs).
The paper "Learning to Ideate for Scientific Impact" explores using delayed signals of scientific uptake—specifically citation-normalized impact—as feedback to steer large language models toward generating high‑impact research ideas. The authors build a dataset of over 100,000 computer science papers, train a reward model to predict citation impact from goal‑idea pairs, and align an idea generator via supervised fine‑tuning and reinforcement learning. Evaluation with a reference‑grounded protocol shows that the RL‑tuned model consistently produces ideas with higher estimated impact than baseline models.
arXiv:2505.17571v2 Announce Type: replace Abstract: Personalization is a critical task in modern intelligent systems, with applications spanning diverse domains, including interactions with large lan...
ConvergeWriter introduces a bottom‑up, data‑driven framework for long‑form document generation that first retrieves exhaustive knowledge from a source corpus and clusters it into distinct knowledge groups. These clusters then guide the creation of a hierarchical outline and the final text, ensuring the output is strictly grounded in the retrieved material and traceable to its sources. Experiments on 14B and 32B LLMs show that this approach matches or surpasses state‑of‑the‑art baselines, especially in scenarios requiring high factual fidelity and structural coherence.
DeepWeaver addresses the evidence synthesis gap in open‑ended question answering by weaving noisy retrieved evidence into comprehensive answers. It introduces Thought Block Chains (TBCs) that organize claims, key information, and citations, allowing the system to revise and expand evidence before final generation. Evaluations on LoQA and DeepResearch Bench show improved content sufficiency, citation grounding, and detail preservation across multiple LLMs.