Horizon-Labs/hallucination-guard-small

Hallucination Guard (small, 141M)

A small, multilingual groundedness checker. Given a source (retrieved documents, a transcript, a tool result) and an AI response or claim, it predicts whether the response is supported by the source. Use it to flag RAG answers, summaries and agent outputs that add, change or contradict facts.

• Multilingual: trained on data in 30 languages. On HaluEval translated into six languages it stays at the level of its English score, while English-only checkers drop (table below).
• Long context: 8k-token window (trained at 2k); long sources can also be chunked (see below).
• Open: Apache-2.0, ungated. Trained on permissively licensed data and Qwen-generated synthetic data. ONNX included.
• Honest about where it loses: on English claim-level fact-checking (LLM-AggreFact), MiniCheck and HHEM are better. If you only need English, compare them on your data.

Try it in the browser: Horizon-Labs/hallucination-guard demo.

Part of Agent I/O Guards (prompt injection, PII, groundedness). Source code: github.com/horizon-ai-labs/agent-io-guards.

Quick start

from transformers import pipeline

clf = pipeline("text-classification", model="Horizon-Labs/hallucination-guard-small")
doc = "The Eiffel Tower is 330 metres tall and was completed in 1889 for the World's Fair."
clf({"text": doc, "text_pair": "The tower was finished in 1889."})   # SUPPORTED
clf({"text": doc, "text_pair": "The tower was finished in 1899."})   # UNSUPPORTED

Put the source first and the response or claim second. Labels: UNSUPPORTED (0) and SUPPORTED (1). The score for SUPPORTED is the support probability.

For long sources, or to find which sentence is unsupported, split the response into sentences and score each one against the source. For a response-level score, take the minimum over sentences:

import re
def check(source, response, clf=clf):
    sents = [s for s in re.split(r"(?<=[.!?。！？])\s+", response) if s.strip()]
    res = clf([{"text": source, "text_pair": s} for s in sents], truncation=True, max_length=2048)
    return [(s, r["label"], r["score"]) for s, r in zip(sents, res)]

Evaluation

Balanced accuracy at a 0.5 threshold. All models were run by us with the same script (ground/evaluate_ground.py). MiniCheck used context chunking with max over chunks, as its own library does. HHEM used contexts capped at 12,000 characters, because it ran out of memory on the longest documents. † marks sets that are in-distribution for our models: we trained on RAGTruth's train split, and those rows use its test split.

LLM-AggreFact (English claim verification, 11 datasets, up to 1,000 examples each)

	this model	base (308M)	MiniCheck-RoBERTa-L	MiniCheck-DeBERTa-L	HHEM-2.1-open
AggreFact-CNN	0.607	0.618	0.664	0.622	0.629
AggreFact-XSum	0.644	0.622	0.709	0.688	0.706
ClaimVerify	0.682	0.737	0.783	0.744	0.753
ExpertQA	0.553	0.593	0.606	0.597	0.568
FactCheck-GPT	0.657	0.674	0.754	0.728	0.729
LFQA	0.726	0.813	0.863	0.838	0.839
RAGTruth †	0.796	0.815	0.788	0.773	0.734
Reveal	0.798	0.834	0.896	0.871	0.865
TofuEval-MediaS	0.654	0.707	0.696	0.676	0.679
TofuEval-MeetB	0.677	0.736	0.765	0.724	0.721
Wice	0.719	0.732	0.717	0.664	0.751
Mean	0.683	0.716	0.749	0.721	0.725

Multilingual: HaluEval QA and dialogue, translated (400 items per language)

The items were machine-translated with Qwen3.8-27B, keeping their labels. They are disjoint from the English HaluEval items above. Caveat: the translations come from the same model family we used to generate synthetic training data, which may favour our model somewhat.

	this model	base (308M)	MiniCheck-RoBERTa-L	MiniCheck-DeBERTa-L	HHEM-2.1-open
English	0.657	0.720	0.589	0.670	0.701
German	0.719	0.724	0.691	0.672	0.670
Spanish	0.691	0.714	0.650	0.675	0.675
Chinese	0.713	0.738	0.627	0.621	0.546
Japanese	0.729	0.751	0.626	0.669	0.546
Arabic	0.700	0.710	0.616	0.691	0.544
Hindi	0.730	0.740	0.595	0.670	0.564
Mean of the 6 non-English languages	0.714	0.729	0.634	0.666	0.591

Other benchmarks

	this model	base (308M)	MiniCheck-RoBERTa-L	MiniCheck-DeBERTa-L	HHEM-2.1-open
HaluEval QA	0.691	0.782	0.635	0.768	0.762
HaluEval dialogue	0.643	0.635	0.522	0.524	0.625
HaluEval summarization	0.548	0.559	0.648	0.623	0.526
RAGTruth test, response level †	0.798	0.829	0.604	0.626	0.750

Limitations

• On English claim-level fact-checking, MiniCheck-RoBERTa-L (0.749) and HHEM (0.725) beat this model (0.683) on LLM-AggreFact. Our advantage is in other languages, and in QA and dialogue grounding.
• It judges support by the given source only. It is not a world-knowledge fact checker: a true statement that the source doesn't contain is UNSUPPORTED.
• Simple arithmetic or temporal inferences are often marked UNSUPPORTED. For example, "opened before 2022" given a source that says "opened in March 2021". The small model also misses some paraphrases ("weekdays" for "Monday to Friday") that the base model handles.
• Summaries with many small details (HaluEval summarization) and expert long-form answers (ExpertQA) are hard for every model here.
• Much of the training data is synthetic (Qwen3.8-27B). The multilingual numbers come from translated data, not native benchmarks.

Training

• Backbone: jhu-clsp/mmBERT-small (MIT), sequence-pair classification, max length 2048, bf16.
• About 340k (source, response) pairs:
  • Qwen3.8-27B-generated responses over FineWeb-Edu / FineWeb-2 passages (ODC-BY) in 29 languages: supported answers and minimally edited unsupported variants, at both response and sentence level.
  • Qwen-generated documents in 18 genres (news, meeting transcripts, support chats, retrieved snippets, reviews, contracts…) with supported and unsupported claims, in 30 languages.
  • RAGTruth train split (MIT), response level.
  • WANLI (CC-BY-4.0).
• Not used: ANLI and other non-commercial NLI data, DocNLI (derived from non-commercial sources), and every benchmark above except RAGTruth's train split.