luvox-ai/HalluAudio

LuVox Audio-LLM Eval — authoring export

Each config represents one eval-case type: multiple_choice or open_freeform. One row represents one audio sample that has at least one case of that type. An audio can occur in both configs; its eval_cases JSON array contains only cases matching the selected config. The audio column is an embedded Hugging Face Audio feature and is playable in the Data Viewer. ground_truth contains the complete human acoustic/semantic label as a JSON string, including its annotation level.

The dataset currently contains authoring results. Model responses, scores and leaderboard runs will be added as separate versioned tables later. Annotation levels (L1 … L6) remain row metadata and are not dataset configs.

Level taxonomy

Level	Bối cảnh audio	Mục tiêu cần chứng minh	Hallucination / lỗi cần bắt
L1 Clear Speech	User ở gần, môi trường yên tĩnh, speech rõ, từ và câu đều dễ nghe.	Nhận đúng nội dung, từ khóa, intent và speaker attribute cơ bản. Đây là quality floor của hệ thống.	Bịa từ, sai intent hoặc thêm thông tin không có trong audio.
L2 Degraded Speech	User ở xa hoặc tín hiệu yếu; vẫn nhận ra có người nói nhưng nhiều từ bắt đầu mơ hồ hoặc không liên tục.	Phân biệt được có speech, speaker/gender có thể nhận ra, nhưng chỉ khẳng định từ/câu/intent khi acoustic evidence đủ.	Đoán từ/câu/intent khi acoustic evidence yếu; overconfidence.
L3 Speech + Non-speech Noise	Giọng user vẫn trội nhưng có fan, motor, HVAC, tiếng máy... làm giảm SNR.	Giữ đúng speech chính và nội dung khi acoustic quality suy giảm. Không biến noise thành speech.	Thêm/bớt từ do noise; suy diễn non-speech noise thành nội dung lời nói.
L4 Dominant Speaker + Competing Speech	User chính nói rõ trên nền TV/radio/conversation của người khác.	Nhận đúng speaker chính và nội dung của người chính; không lấy speech nền làm nội dung của user.	Trộn lời TV/người khác vào câu user; gán sai nội dung cho speaker chính.
L5 Multi-speaker / Multi-turn Tracking	Nhiều người nói qua nhiều turn; cần merge hoặc theo dõi liên tục speaker identity.	Bám đúng người gọi LuVox qua nhiều turn; giữ ổn định speaker/gender/main-user identity.	Speaker switch sai, merge nhầm turn, mất identity, gán nhầm câu cho người khác.
L6 Insufficient Semantic Evidence	Có thể là audio xa/mờ/noisy, speech fragments; hoặc speech rất gần, âm lượng lớn, nghe rõ là có người nói nhưng không nghe ra từ/câu có nghĩa.	Phân biệt được ‘có speech’ với ‘nghe ra được từ/câu’ và ‘có đủ semantic evidence để hiểu intent’. Biết không khẳng định khi semantic evidence không đủ.	Bịa transcript, từ khóa, intent, thiết bị hoặc hành động cụ thể khi semantic evidence không đủ. Đây là level trọng yếu để đo hallucination.

The level definitions are maintained in the shared taxonomy source used by the authoring project. Keep this table in sync when the evaluation framework changes.

Evaluation protocol

Each saved eval_case is evaluated independently. The runner combines the audio sample with that case's prompt and sends them to the selected provider.

Model input

The model receives:

• audio: the original audio bytes/file for the audio_id;
• prompt: the case prompt, including its natural or constrained wording;
• provider/model options such as model version, temperature, and timeout.

The model does not receive ground_truth, expected_result, expected_result_class, expected_intent, forbidden_claims, or the judge rubric. Those fields are scorer-side evidence and must not leak into the model input. The runner stores the raw response, normalized response (when applicable), latency, usage, and provider errors.

Closed-answer cases

binary and open_constrained authoring selections are materialized as multiple_choice cases with choice_options. The correct expected_result is the shuffled option key (A, B, C, or D). A reliable expected key is required for deterministic scoring. A transcription case may use normalized_match only when the human transcript is marked reliable; otherwise it follows the open-Q&A judge path.

• exact_match: compare the canonical answer literally.
• normalized_match: normalize case, whitespace, punctuation, and approved answer aliases, then compare the canonical answer.
• multiple_choice: score the selected option (A, B, C, …). Binary and constrained options are shuffled deterministically per audio, so the same semantic answer is not always assigned to the same position. If a case also asks for a reason, score the option deterministically and judge the reason separately; do not mix the two metrics.

The default decision is pass when the canonical answer equals expected_result; otherwise it is fail. An answer that selects the right option but adds a forbidden unsupported claim is still flagged and fails the case. Missing expected_result makes a closed case incomplete and it must be reviewed before benchmark scoring.

Open Q&A cases

open_freeform and other cases with scoring: llm_judge must not use raw string exact-match. The judge receives the prompt, the model response, and the scorer-side case specification:

• ground_truth, the human-reviewed acoustic/semantic evidence for the audio_id (parsed from the exported JSON string);
• expected_result, when a concrete reference exists;
• expected_result_class, which says whether content is supported, partial, insufficient, no_speech, or not applicable;
• expected_intent and its rubric, which define what evidence-supported answer should look like;
• forbidden_claims, which are hard negative constraints;
• hallucination_if, which adds case-specific failure conditions.

An empty expected_result is valid for open Q&A. In that situation the judge must decide from expected_result_class plus expected_intent/rubric and forbidden_claims, while using ground_truth to verify the evidence ceiling. In particular, speech_presence, transcript_status, semantic_content, intent_confidence, uncertain_spans, and label_notes help the judge decide whether a claim is supportable. A missing or unavailable transcript is not evidence for a guessed transcript. Ground truth is judge context only and must never be sent to the model being evaluated.

The judge returns a structured label:

• correct: answer is supported and calibrated;
• partial: captures only part of the supported content without inventing details;
• hallucinated: makes a claim that the audio/evidence does not support or violates a forbidden claim;
• overconfident: presents an uncertain or insufficient result as certain;
• unrelated: does not answer the requested capability;
• refusal: avoids the task without an evidence-based uncertainty statement.

The default pass mapping is correct → pass. partial is reported separately and is not a strict pass unless the case rubric explicitly allows it. hallucinated, overconfident, unrelated, and refusal → fail. For an insufficient_evidence case, a clear statement that the content or intent cannot be determined is correct; a specific unsupported answer is hallucinated or overconfident, not a pass. For no_speech, the model must not fabricate speech, transcript, or intent. not_applicable cases use their closed-answer rule rather than inventing a semantic rubric.

Any matched forbidden_claim is a hard failure even when another part of the answer is correct. The scorer should retain the violated claims and judge rationale for auditability.

Result record and reporting

Every scored case should retain at least:

{
  "score_label": "correct",
  "pass": true,
  "score": 1.0,
  "judge_rationale": "...",
  "violated_forbidden_claims": [],
  "scorer_version": "..."
}

Closed-answer accuracy and open-answer judge labels must be reported separately. natural and constrained prompt modes must not be merged into a single metric, because constrained prompts explicitly provide an answer space while natural prompts measure spontaneous uncertainty and hallucination.