PAMI-GPT: knowledge-grounded LLMs for reliable pattern mining workflow generation

ExReg: Using Automated Example Generation to Guide Candidate Disambiguation and Validation in Regular Expression Construction

Regular expressions (regexes) are widely used in software development but remain difficult to author and validate due to their compact syntax and subtle semantics. While large language models (LLMs) can now generate regexes from natural-language descriptions, their outputs often miss developer intent, and existing refinement techniques rely on developers to craft positive and negative examples—especially discriminatory ones that expose fine-grained semantic differences. Producing such examples is cognitively demanding and often leaves regexes under-validated. This paper introduces ExReg, a human-in-the-loop workflow that shifts this burden away from developers. Given an ambiguous natural-language specification, an LLM first proposes multiple plausible regex candidates. Instead of requiring developers to devise discriminative examples, the system automatically generates them using SMT-based constraint solving. Developers need only affirm whether these synthesized test strings match their intent. The system further mutates candidates and reuses discriminative strings to systematically eliminate incorrect patterns. Across six benchmark datasets and six state-of-the-art LLMs, ExReg accurately identifies valid regexes—or determines that none are appropriate—while requiring only minimal developer validation. In particular, ExReg achieves an average accuracy of 87%, while requiring users to inspect only 5 examples on average, each with a mean length of 8 characters and an inter-example waiting time of under 13 seconds. These results highlight how automated example generation guided by formal methods and mutations can improve the reliability, efficiency, and trustworthiness of LLM-assisted regex pattern generation.

SymbolLKG: Towards Verifiable Logical Reasoning via Logical Knowledge Graph and Symbolic Solvers

Large Language Models (LLMs) have demonstrated remarkable proficiency in natural language understanding, yet they struggle with strict multi-step reasoning, frequently suffering from hallucinations and inconsistency. Existing solutions like Chain-of-Thought (CoT) lack rigorous verification mechanisms, while standard Retrieval-Augmented Generation (RAG) often misses the complex, structural dependencies inherent in logical tasks. To bridge this gap, we propose a Neuro-Symbolic architecture that integrates a Logical Knowledge Graph (LKG) with dynamic solver routing. Specifically, we introduce an ontology-based LKG that treats logical rules and constraints as first-class topological nodes, enabling explicit modeling of dependencies extracted from text. We further design a Logic Router to dynamically dispatch tasks to the optimal symbolic engine, which is supported by a topology-aware hybrid retrieval mechanism. Experimental results on logical reasoning benchmarks demonstrate that our framework significantly outperforms state-of-the-art prompting and RAG baselines, delivering higher accuracy and verifiable reasoning paths.

Automatic Chain of Concepts: Conceptual Prompting for LLMs by Constructing Concept Trees

Overall, AutoCoC contributes a reproducible and scalable alternative to expert-authored concept hierarchies by automatically deriving domain-specific prompt structures from documentation, making concept-based prompting more practical for structured knowledge generation.

Optimizing sample selection for large language model-based entity matching using AssistEM

AssistEM, a framework for efficient LLM adaptation to EM via principled data selection, demonstrates that selective fine-tuning not only accelerates adaptation but also improves training efficiency (requiring fewer GPU hours), enabling open-source LLMs to rival–and in some cases outperform–closed-source models.

Deterministic Validation for Reliable LLM-Based Causal Knowledge Extraction

Large Language Models produce stochastic outputs that undermine reproducibility in knowledge extraction. We present a deterministic post-processing framework with 14 explicit validation predicates that transforms unreliable LLM output into consistent, validated causal triplets. Evaluated on four benchmarks spanning 2,177 documents, the framework achieves 88% precision on DocRED (validated by 5-agent LLM-based inter-annotator agreement), 100% semantic F1 on causal-specific samples, and 100% byte-level determinism across 150 repeated extractions. Multi-model validation on three architectures (Qwen8B, Gemma-2B, Llama-3B) confirms that determinism is a property of the validation architecture, not the underlying model-all achieve perfect consistency despite extraction rates varying by $9 \times$. Stochastic sampling experiments (temperature 0.8) confirm the framework contains no hidden randomness. Against dependency-based Open IE, the framework produces complete causal triplets where Open IE yields 60% incomplete extractions. The key contribution: reliability emerges from deterministic validation architecture rather than model improvements. All code and results are publicly available.

Prompt Template-Driven Large Model SQL Generation