LangDex

AI & LLM Grounding

LLMs are statistical pattern matchers—they don't know linguistic facts. When your model claims 食べる means 'to eat,' it's pattern-matching, not querying a knowledge base. LangDex provides the symbolic verification layer: stable concept IDs that ground neural outputs in structured, citable linguistic reality.

The Challenge

The Linguistic Hallucination Problem

  • 1LLMs hallucinate translations for low-resource languages—no training data means confident confabulation
  • 2Vector embeddings flatten logographic structure: 食 (eat) and 飲 (drink) may cluster incorrectly without radical-level encoding
  • 3RAG on Wiktionary dumps returns fuzzy, contradictory definitions without semantic disambiguation
  • 4Aligning multilingual embedding spaces requires seed dictionaries—but structured pairs don't exist for 5,000+ languages
  • 5Enterprise clients demand citation and provenance; 'the model said so' isn't auditable
The Solution

Symbolic Verification Layer

LangDex provides the 'Symbolic' half of Neuro-Symbolic AI. Every lexeme links to a PanLex concept ID—a stable semantic anchor across 5,700+ languages. When your model generates a translation or definition, query LangDex to verify: does this word actually link to that concept? Is this synonym relationship attested? Ground neural fluency in symbolic precision.

Features

Key Capabilities

Concept ID Verification

Map model outputs to PanLex concept IDs. Verify claims like 'X means Y' against the graph before surfacing to users.

Seed Dictionary Extraction

Pull structured translation pairs for any language pair in 5,700+. The anchor points for multilingual embedding alignment.

GraphRAG-Ready Schema

Retrieve structured triples (lexeme→relation→lexeme) instead of fuzzy text chunks. Enable multi-hop reasoning over linguistic entities.

Hallucination Scoring

Compare generated linguistic claims against the knowledge graph. Flag outputs with no supporting evidence or contradicting attested facts.

Low-Resource Data Augmentation

Generate training data for under-resourced languages by traversing concept→lexeme links. Systematic coverage, not random sampling.

Full Provenance

Every fact traced to source (Wiktionary revision, JMDict entry ID, PanLex expression). Auditable citations for enterprise compliance.

Who Is This For

Perfect For

Foundation model companies (OpenAI, Anthropic, Cohere)Sovereign AI initiatives needing local language coverageEnterprise AI teams requiring auditable outputsGraphRAG and knowledge graph teamsMultilingual NLP researchersAI safety teams building evaluation harnessesTranslation AI (DeepL, Google Translate competitors)Benchmark creators (FLORES, WMT participants)

Ready to Get Started?

Join our waitlist to get early access to LangDex's API and start building.