Tag
This paper presents a multi-stage LLM pipeline for structure-preserving Marathi-to-English translation of government documents, integrating layout-aware OCR and HTML reconstruction to maintain formatting and domain terminology.
A detailed technical query about building a local document RAG system covering storage, ingestion, query, and highlighting, seeking advice on vector databases, GraphRAG feasibility, and document highlighting implementations.
This paper introduces variable-centered empirical graph extraction for psychology abstracts, constructing the EmpiriGraph-Psy benchmark dataset of 210 annotated abstracts and a staged LLM pipeline that achieves a macro-F1 of 0.74, outperforming direct extraction methods.
This paper presents FVSpec, a benchmark for AI-assisted formal verification that translates real-world property-based tests from Python into Lean 4 specifications using a multi-agent LLM pipeline, aiming to drive progress on formal verification of real-world software.
LLMBridge introduces an LLM-based pipeline for end-to-end referential bridging resolution, achieving state-of-the-art performance on three English datasets. The system combines heuristic pre/post-processing with LLM natural language inference.
This paper introduces slidesqaqa, a Flask-based software system that generates pedagogically useful questions from PDF slide decks. It uses a four-stage LLM pipeline to extract text and images, plan questions across the deck, annotate slides, and reconcile outputs, demonstrating high-fidelity question generation on technical lecture slides.
SpineDigest is an open-source tool that uses an LLM pipeline to transform long-form books into structured summaries with chapter topology and knowledge graphs, supporting EPUB, Markdown, and TXT input.
SpineDigest is an open-source CLI tool that uses a multi-stage AI pipeline to distill long books into structured summaries, generating chapter topology maps and knowledge graphs, and displaying them with the Inkora reader.
The author shares four specific improvements to their automated content engine, including smaller context packets, viral postmortems, folder-based state management, and bookmarkability scoring.
ConlangCrafter is a multi-hop LLM pipeline that automates constructed language (conlang) creation by decomposing the process into modular stages including phonology, morphology, syntax, lexicon generation, and translation. The system leverages LLMs' metalinguistic reasoning with randomness injection and self-refinement to produce coherent and typologically diverse constructed languages.