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.
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.
Related stories
arXiv AI
Jul 7
A Retrieval-Augmented Framework for Detecting and Resolving Pragmatic Ambiguities in Natural Language Requirements
arXiv:2607. 04436v1 Announce Type: cross Abstract: Natural language requirements (NLRs) are essential for bridging communication gaps among diverse stakeholders in software development.
arXiv Computation and Language
3d ago
AdaGEPA: Adaptive Feedback Allocation for Reflective Prompt Optimization
arXiv:2609.39927v1 Announce Type: new Abstract: Prompt optimization improves the performance of language-model systems on downstream tasks by refining their prompts. Classical methods evaluate prompt...
arXiv AI
Sep 17
Which LLM is Best for Translating Natural Language Goals to PDDL
The paper evaluates how well current Large Language Models can translate natural language goals, written by video game testers, into well‑formed PDDL targets for classical planning. Using a carefully designed prompt template, six state‑of‑the‑art LLMs were tested on correctness, speed, and error tendencies with real‑world benchmarks. All models achieved high correctness (>92%), with Gemini 2.5 Flash reaching 96% accuracy and the fewest false positives, while GPT‑4.1 was the fastest, yet differences in performance and occasional failures due to ambiguity and domain limits remain.
arXiv AI
Jun 17
PromptMN: Pseudo Prompting Language
arXiv:2606. 17164v1 Announce Type: cross Abstract: Prompting has become the primary interface between humans and generative AI, yet many natural language prompts remain fragile: roles, goals, constraints, and expected outputs are often buried in prose or left implicit.
arXiv AI
Aug 19
Parametric Knowledge in RAG-SFT for Domain-Specific Document Generation
The paper investigates Retrieval-Augmented Generation fine‑tuning (RAG‑SFT) for generating requirements documents in electronics engineering, comparing two 7B models trained with different data strategies. It introduces a claim‑based evaluation pipeline, C‑FEX, and a new metric, Parametric Knowledge Precision (PKP), to assess factuality of model‑generated claims. Results show that fine‑tuned 7B models can match or surpass a 72B baseline, but standard metrics may mislead, and fine‑tuning reduces hallucination by encouraging more reliable use of parametric knowledge.
arXiv AI
Jul 17
Automatically Evolving Prompt Guidelines for Task-Specific Optimization
arXiv:2607. 14105v1 Announce Type: cross Abstract: For Large Language Models to reliably answer user queries, users must clearly specify requirements, context, and constraints.
Hugging Face Trending Papers
Jul 29
SpecFirst: Behavioral Specification Elicitation as a First-Class Step in Agent-Based Program Synthesis from Scratch
LLM-based agents excel at software engineering tasks where an existing codebase provides context, but constructing a program from scratch remains fundamentally harder. Recent benchmarks such as ProgramBench quantify this gap: given only natural-language documentation and an execute-only binary as a behavioral oracle, even frontier models solve fewer than 1% of instances.
arXiv AI
Jun 3
From Context to Skills: Can Language Models Learn from Context Skillfully?
arXiv:2604. 27660v3 Announce Type: replace Abstract: Many real-world tasks require language models (LMs) to reason over complex contexts that exceed their parametric knowledge.
arXiv Machine Learning
Aug 6
From Brute Force to Semantic Insight: Performance-Guided Data Transformation Design with LLMs
arXiv:2601. 03808v2 Announce Type: replace-cross Abstract: Large language models (LLMs) have achieved notable performance in code synthesis; however, data-aware augmentation remains a limiting factor, handled via heuristic design or brute-force approaches.
arXiv Computation and Language
Sep 17
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 Machine Learning
Aug 27
Towards Reliable, Generalizable, and Specific In-Context Knowledge Editing via Multi-Objective Reinforcement Learning
The paper introduces Multi-Objective In-context Knowledge Editing (MO‑IKE), a reinforcement learning framework that treats prompt construction for knowledge editing as a constrained Markov decision process. MO‑IKE jointly optimizes three competing objectives—reliability, generality, and specificity—by training a dynamic retriever to balance these goals and produce globally coherent prompts. Experiments on Llama‑3.2 show that MO‑IKE raises edit success from 85.0 % to 92.0 %, improves paraphrase consistency from 77 % to 79 %, and boosts retention rate by 23 % compared to earlier RL‑based methods.