arXiv Computation and Language

Relationally Guided Use Case Modeling with LLMs

The paper introduces FlowGen, a system that automates the construction of use case flows using large language models (LLMs). FlowGen extracts semantic elements via an LLM-based Semantic Information Processing module, builds a Semantic Relational Graph encoded by an enhanced R-GAT for basic flow generation (BFGen), and adds branch point prediction (BPP) and branch-conditioned alternative flow generation (AFGen). Experiments on 13 public and 7 industrial datasets show FlowGen outperforms baselines across precision, recall, F1, and AUC metrics for all three components.

arXiv AI

Sep 1

Generating Workflow DAGs from Natural Language with Non-Reasoning LLMs

arXiv:2608.30250v1 Announce Type: new Abstract: This paper addresses the problem of translating natural-language routing rules written by business administrators into executable workflow graphs for e...

By Anand Iyer, Bhanu Khetharpal, Srinivas Upadhya, Ramkumar Rajagopal

llms benchmarks

arXiv AI

Sep 15

ModiGen: A Large Language Model-Based Workflow for Multi-Task Modelica Code Generation

arXiv:2503.18460v2 Announce Type: replace-cross Abstract: Modelica is a widely adopted language for simulating complex physical systems, yet effective model creation and optimization require substant...

By Jiahui Xiang, Tong Ye, Peiyu Liu, Yinan Zhang, Wenhai Wang

llms rag fine-tuning benchmarks

arXiv AI

Aug 5

Evaluating LLM-Based Goal Extraction in Requirements Engineering: Prompting Strategies and Their Limitations

arXiv:2604. 22207v2 Announce Type: replace-cross Abstract: Due to the textual and repetitive nature of many Requirements Engineering (RE) artefacts, Large Language Models (LLMs) have proven useful to automate their generation and processing.

By Anna Arnaudo, Riccardo Coppola, Maurizio Morisio, Flavio Giobergia, Andrea Bioddo, Angelo Bongiorno, Luca Dadone

llms rag benchmarks

arXiv Machine Learning

4d ago

Harnessing Large Language Models to Compile Task-Relevant Context into Bayesian Optimisation

arXiv:2609.36788v1 Announce Type: new Abstract: Incorporating rich task-relevant context, such as domain knowledge and external observations, is a key capability yet remains challenging for Bayesian...

By Zhongwei Yu, Sourabh Roy, Bin Cao, Xue Yan, Anjie Liu, Jun Wang

llms rag benchmarks

arXiv Machine Learning

Jun 9

OpenCompass: A Universal Evaluation Platform for Large Language Models

arXiv:2605. 19276v3 Announce Type: replace-cross Abstract: In recent years, the field of artificial intelligence has undergone a paradigm shift from task-specific small-scale models to general-purpose large language models (LLMs).

By Maosong Cao, Kai Chen, Haodong Duan, Yixiao Fang, Zhiwei Fei, Tong Gao, Ge Jiaye, Mo Li, Hongwei Liu, Junnan Liu, Yuan Liu, Chengqi Lyu, Han Lyu, Ningsheng Ma, Zerun Ma, Yu Sun, Zhiyong Wu, Linchen Xiao, Zhuozhi Xiong, Jun Xu, Haochen Ye, Zhaohui Yu, Yike Yuan, Songyang Zhang, Yufeng Zhao, Fengzhe Zhou, Peiheng Zhou, Dongsheng Zhu, Lin Zhu, Jingming Zhuo

llms benchmarks

arXiv AI

Aug 18

Automating the Detection of Requirement Dependencies Using Large Language Models

arXiv:2602. 22456v2 Announce Type: replace-cross Abstract: Requirements are inherently interconnected through various types of dependencies.

By Ikram Darif, Feifei Niu, Manel Abdellatif, Lionel C. Briand, Ramesh S., Arun Adiththan

llms rag benchmarks

arXiv AI

Jun 16

Graphical-Probabilistic Modeling of Generative Flows in LLM-Native Software Systems

arXiv:2606. 15943v1 Announce Type: cross Abstract: Engineering LLM-native software remains a challenging and immature field.

By V\'ictor A. Braberman, Flavia Bonomo-Braberman

llms

arXiv AI

Jul 22

BatchDAG: LLM-Planned Execution Graphs for Scalable Ad-Hoc Analysis Over Enterprise Data

arXiv:2607. 18241v1 Announce Type: new Abstract: Large language models (LLMs) excel at analyzing individual documents but break down on exhaustive, cross-entity analytical questions over enterprise-scale datasets due to context overflow, loss of per-entity attribution, and linear latency from sequential tool calls.

By Anupreet Walia

llms agents benchmarks safety data-engineering

arXiv Machine Learning

Jul 28

Benchmarking LLMs for Verilog Design Flows

arXiv:2607. 22759v1 Announce Type: cross Abstract: Large language models (LLMs) show promise in code generation, but their capabilities to produce correct, synthesizable hardware description language (HDL) code still remain to be properly benchmarked.

By Angshuman Chakravertty, Rahul Koshti, Buddhi Prakash Sharma, Vinay Chamola

llms benchmarks

arXiv Machine Learning

Aug 12

FlowScout: From Execution Feedback to Reliable Tool-Using Agent Workflows

arXiv:2608. 10039v1 Announce Type: new Abstract: Agentic workflows have become an important abstraction for building reliable LLM-based automation systems by organizing large language models (LLMs), tools, and control logic into explicit execution structures.

By Shuo Hao, You Lu, Bihuan Chen, Xin Peng

llms agents

arXiv AI

Jul 7

Reachability Across the NL/PL Boundary: A Taxonomy-Driven Dataflow Model for LLM-Integrated Applications

arXiv:2603. 28345v3 Announce Type: replace-cross Abstract: LLM API calls have become a standard programming primitive, but they create a program boundary that disrupts traditional dataflow analysis.

By Zihao Xu, Xiao Cheng, Ruijie Meng, Yuekang Li

llms

arXiv AI

6d ago

Semantic Navigation for Issue Localization in Code Repository

arXiv:2609. 31176v1 Announce Type: new Abstract: Repository-level issue localization aims to identify and rank the files and functions relevant to resolving a reported issue.

By Yunxiang Wei, Zhenyu Lei, Jundong Li

llms agents

Related stories