Source code for the EMNLP 2019 paper "Multi-Input Multi-Output Sequence Labeling for Joint Extraction of Fact and Condition Tuples from Scientific Text" (给定科研文本如生物医药文献，联合抽取其中事实三元组、条件三元组，即对文献进行信息结构化)
AAAI-24 CFEVER: A Chinese Fact Extraction and VERification Dataset
TypeScript library providing persistent memory for AI agents — conversation history, long-term memory with vector search, and automatic fact extraction.
The first dataset for Farsi fact extraction and verification
A persistent, reconcilable memory graph for LLM agents — detects conflicts, validates facts, resolves contradictions, and maintains a full audit trail across sessions.
Russian-Facts-200 (RF-200): A novel benchmark for fact extraction from Russian tabular data.
A beautiful, interactive web application that fetches random cat facts using a clean neumorphic design. Built with Next.js, TypeScript, and Tailwind CSS.
The SEntence-Level FActual Reasoning (SELFAR) is a new method to improve explainable fact-checking. It relies on fact extraction and verification by predicting the news source reliability and factuality (veracity) of news articles or claims at the sentence level, generating post-hoc explanations using SHAP/LIME and zero-shot prompts.
Governed, time-aware fact-retrieval sidecar for RAG. SIGIR 2026.
Evidence-first, AI-powered report generation system using multi-agent architecture with fact extraction, knowledge graphs, and hallucination detection.
Turn research corpora into auditable, evidence-graded claim inventories. A Claude skill with archaeology and medical-scientific grading presets.
AI-powered Fact Knowledge Layer that ingests corporate and macroeconomic PDFs, extracts grounded facts with source provenance, normalizes data, and reconciles cross-document inconsistencies using deterministic reasoning. Built with Streamlit, Gemini, PyMuPDF, and SQLite.
Fully offline, deterministic intelligence-collapse engine — turn documents into a governed, sourced fact graph on your own hardware with a local LLM. Abstains instead of guessing.
An end-to-end pipeline for ingesting PDF documents, extracting atomic facts with verbatim evidence, and automatically reconciling claims across documents. Combines LLM extraction, deterministic rule engines, and grounded knowledge verification. Built for the SuperJoin on campus recruitment VIT Chennai.
Evidence-grounded fact extraction and cross-document reasoning from PDFs.
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