Model Safety and Alignment Data for RLHF
Model safety and alignment data teaches AI systems when to answer, refuse, clarify, or escalate. InfoBay supports RLHF, red-teaming, factuality checks, and expert preference workflows for safer model behavior.
The problem
Alignment fails quietly — a model that never refuses anything is unsafe, and a model that refuses too readily is useless, and generic crowd-sourced preference data can't tell the difference on a legal, medical, or financial edge case. A safety layer bolted on after training rarely holds up under real adversarial pressure — refusal behavior has to be trained in with data that reflects the specific ways your deployment will actually be tested. InfoBay builds RLHF, red-teaming, and refusal-calibration data with the same domain-expert reviewers who evaluate enterprise copilots, so preference signals reflect expert judgment on when a model should answer, clarify, refuse, or escalate.
Corpus assets & provenance
Expert preference data, not crowd rankings
+Preference data is authored by domain experts — attorneys ranking legal-AI outputs, physicians ranking clinical responses — rather than generalist crowdworkers. InfoBay's Q&A corpus (6.7M+ pairs) with explicit SME reasoning traces is the same underlying asset used for instruction tuning, giving reward models a consistent standard between SFT and preference optimization.
Red-teaming and refusal calibration
+Refusal calibration data is built to test the boundary cases that matter for a given deployment — when a model should answer directly, ask a clarifying question, refuse, or escalate to a human — rather than apply a blanket refusal policy that fails both the too-cautious and too-permissive edges of the same conversation.
Factuality checks feed alignment
+LLM factuality evaluation findings — hallucination patterns, unsupported claims, domain-specific errors — feed directly back into RLHF and DPO datasets, so alignment work is grounded in the failure modes an evaluation pass actually found rather than a generic safety checklist applied uniformly across domains.
Cross-lingual and cross-domain alignment
+Because the same expert-review infrastructure spans legal, medical, financial, and multilingual domains, alignment data can be scoped cross-lingually and cross-domain — refusal calibration for a voice AI system across 45+ languages looks different from refusal calibration for a legal copilot, and both draw on the same underlying expert pool.
Multimodal and voice safety calibration
+Alignment isn't limited to text. Refusal calibration for a voice AI system needs to account for how a request sounds and in which language it arrives, not only what a transcript says, and image- or video-grounded assistants need calibration for what a model should do when it sees — rather than reads — something it shouldn't act on. Because the same expert-review infrastructure spans audio, text, and multimodal review, safety and alignment data can be scoped to whichever modality your deployment actually uses instead of defaulting to text-only preference data.
Specifications
| Data type | Format | Volume | Domain coverage | Provenance |
|---|---|---|---|---|
| Expert preference data | JSONL, ranked comparison pairs | 6.7M+ underlying Q&A pairs | Legal, medical, financial, cross-lingual | SME-authored, domain-expert reviewed |
| Red-teaming / refusal calibration | JSONL, scenario + expected-behavior fields | 2,500+ red-team scenarios | Deployment-specific | Expert-reviewed |
| Factuality evaluation findings | Structured error taxonomy + reviewer rationale | 1,000+ scored responses per review cycle | Cross-domain | Expert reviewer sign-off |
How an engagement works
- 1
Baseline refusal and factuality behavior
Every engagement starts by measuring current refusal rate, factuality, and pass rate on evaluation prompts relevant to your deployment's risk profile.
- 2
Scope red-team and preference data
Preference rankings and red-team scenarios are scoped to the domain and languages your model operates in, drawing on the matched expert reviewer pool.
- 3
Deliver and re-baseline
Data ships with reviewer rationale and error taxonomy attached; refusal and factuality behavior are re-measured against the original baseline after alignment training.
Answers for buyers
FAQ
How is InfoBay's preference data different from crowd-sourced RLHF data?+
It's authored and ranked by domain experts — attorneys, physicians, finance professionals — rather than generalist crowdworkers, which matters for edge cases a non-expert reviewer can't reliably judge.
What is refusal calibration data?+
Data that tests the specific boundary between answering, clarifying, refusing, and escalating for a given deployment, rather than applying a single blanket refusal policy.
Does factuality evaluation feed into alignment data?+
Yes. Hallucination and error patterns found during factuality evaluation are typically converted into RLHF, DPO, and red-team datasets.
Can alignment data be scoped to a specific language or domain?+
Yes — the same expert-review infrastructure spans legal, medical, financial, and multilingual domains across 45+ languages.
Is red-teaming included as a standalone service or only with data curation?+
Red-teaming and refusal calibration are typically scoped alongside data curation and factuality evaluation, though they can be scoped separately depending on deployment stage.
How are results measured?+
In benchmark deltas, factuality improvement percentage, and evaluation pass rate against a baseline agreed before the engagement starts.
Does alignment data cover multimodal deployments, not just text?+
Yes. The same expert-review infrastructure that supports text alignment extends to audio and multimodal review, so refusal and safety calibration can be scoped to the modality your model actually uses.
Next steps