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eicv_suppress_hallucinations

Verify and suppress hallucinated claims in LLM responses using a local, deterministic engine. Rewrite or annotate output with per-claim audit trail.

Instructions

Verify an LLM response and optionally rewrite hallucinated claims.

Returns the (possibly rewritten) output and per-claim audit trail. Computation is fully local — no neural model, no LLM calls.

Modes: audit — analyze only; no rewrite. Use for telemetry/dashboards. annotate — keep output; append verification warnings at end. strict — graduated 4-action policy: supported → PASS (no change) abstain → HEDGE (append "[unverified]") hallucinated → SUPPRESS (remove claim sentence)

Profiles tune the abstain band: rag (default) — strict, for retrieval-augmented generation qa — moderate-strict for QA outputs summarization — tolerant of paraphrase dialogue — broader abstain band fact_check — hardest (FEVER-like setting)

Returns SuppressionResult with:

  • rewritten_output: the (possibly modified) response

  • n_claims / n_supported / n_abstained / n_hallucinated

  • suppressed_count / warned_count

  • hallucination_rate: 0..1 (n_hallucinated / n_claims)

  • certificates: list of per-claim EICVCertificate

  • latency_ms

Accuracy on public datasets is documented in benchmarks/results/. False-positive and false-negative rates are non-zero — a truthful claim can be wrongly suppressed, and a false claim can pass through. Audit-mode is the safe default for compliance- sensitive applications.

Args: context: The grounding evidence the LLM was supposed to use output: The LLM's response text to verify and possibly rewrite profile: Suppression profile (default "rag") mode: "audit" | "annotate" | "strict" (default "strict")

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
modeNostrict
outputYes
contextYes
profileNorag

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Despite no annotations, the description fully discloses behavior: fully local computation, no neural model, no LLM calls. It details the suppression policy, return structure, and accuracy limitations, including non-zero false positive and false negative rates.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured with sections and bullet points, front-loaded with the main purpose. Every sentence adds value, but it is slightly verbose. Still, it is appropriately sized for the tool's complexity.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity (4 parameters, multiple modes/profiles, return structure, accuracy considerations), the description is very complete. It covers all relevant aspects, including return fields (though output schema exists) and usage caveats.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema has 4 parameters with no descriptions (0% coverage), but the description extensively explains each parameter: context (grounding evidence), output (LLM response), profile (options listed), mode (three options with behaviors). This adds significant value beyond the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: 'Verify an LLM response and optionally rewrite hallucinated claims.' It also explains the modes and profiles, distinguishing it from sibling tools like eicv_verify_claim by focusing on suppression.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides explicit guidance on when to use each mode (audit for telemetry, annotate for warnings, strict for active suppression) and each profile (rag, qa, etc.). It recommends audit mode for compliance-sensitive applications and warns about false positives/negatives.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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