license: cc-by-sa-4.0
language:
- he
pretty_name: Abstractive QA for LLM Evaluation
task_categories:
- question-answering
- summarization
size_categories:
- n<1K
tags:
- hebrew
- grounding
- hallucination-detection
- attribution
- faithfulness
Abstractive QA for LLM evaluation
A Hebrew-language dataset of 132 question-answer records, built for testing
whether a language model's generated answer is grounded in its source
document rather than hallucinated. Each record has a question, a
source_text, and a reference_answer whose individual claims are each
backed by exact quoted spans from source_text. Use it to check your own
model the same way: generate an answer, break it into claims, and see
whether each claim traces back to real text in the source.
Contents
- Quick start
- Support sentences: how grounding works
- Field reference
- Key numbers
- Annotator agreement
- License
- Credits
Quick start
import json
with open("abstractive_qa_for_llm_eval.json", encoding="utf-8") as f:
records = json.load(f)
record = records[0]
print("Question:", record["question"])
print("Reference answer:", record["reference_answer"])
The file is a single JSON array of 132 records. Load it once and index into it like any list.
Support sentences: how grounding works
reference_answer is not graded as one block of text. It is split into
claims, one sentence-level statement each. Every claim carries an
attribution array: one or more exact spans copied from source_text
that support it. Each span gives a start and end character offset into
source_text, so you can slice the source string and get the exact
quoted text back.
Here is a real claim from the dataset, with two support sentences from two different parts of the source document:
claim: החשש היה שהאיסור על קיום כיתות נפרדות על רקע "זהות מגדרית" יפורש
כאיסור על הפרדה בין בנים לבנות.
attribution[0]: start_char=1326, end_char=1603, verified=true
source_text[1326:1603] == <מיכל חורין: אני פשוט רוצה לחדד: הנוסח היום
מדבר טעמים של נטייה מינית או זהות מגדרית, בגלל שאני לא מכירה את המינוח
"זהות מגדרית" אני רק רוצה לוודא שלא מדובר מטעמים של מין בעצם, שזהות
מגדרית זה משהו אחר, על מנת שיתאפשר לנו להמשיך להפריד בנים ובנות במוסדות
של החינוך הממלכתי-דתי.
attribution[1]: start_char=4068, end_char=4565, verified=true
source_text[4068:4565] == " כפי שנאמר כאן זה משתלב ברישה שאומרת: "5
(א) רשות חינוך מקומית, מוסד חינוך או אדם הפועל מטעמם, לא יפלו תלמיד
מטעמים עדתיים, מטעמים של ארץ מוצא מטעמים ...
source_text[start_char:end_char] equals source_excerpt exactly. That
is what verified: true means: the span was checked to match the source
text character for character.
To score your own model, do the same two steps on its output: split the generated answer into individual claims, then search for a matching span in the source text for each claim. A claim you cannot find in the source is likely a hallucination. A claim you can find and quote is grounded.
Field reference
Top-level fields (one entry per record)
| Field | Type | Meaning |
|---|---|---|
id |
string | Short unique ID for the record. |
source_task_id |
string | ID of the underlying annotation task this record came from. |
genre |
string | Text genre: dialogue, encyclopedic, or journalistic. |
difficulty |
integer | Question difficulty: 0 = retrieval, 1 = simple, 2 = complex. |
document_id |
string | ID of the source document. |
question |
string | The question, in Hebrew. |
source_text |
string | The full source document the question is about. |
reference_answer |
string | The ground-truth answer, in Hebrew. |
claims |
array | The reference answer, split into individually attributed claims. See below. |
human_ratings |
array | Quality scores from four human annotators. See below. |
claims[]
| Field | Type | Meaning |
|---|---|---|
claim_id |
integer | Position of this claim within the record, starting at 0. |
text |
string | The claim text, a sentence-level piece of the reference answer. |
attribution |
array | One or more support spans backing this claim. See below. |
attribution[]
| Field | Type | Meaning |
|---|---|---|
sentence_id |
integer | ID of the sentence in source_text that this span comes from. |
source_excerpt |
string | The quoted text, copied exactly from source_text. |
start_char |
integer | Start offset of the span in source_text. |
end_char |
integer | End offset of the span in source_text (exclusive). |
verified |
boolean | Whether source_text[start_char:end_char] was checked to equal source_excerpt. |
verification_method |
string | How the check was done, for example auto. |
human_ratings[]
| Field | Type | Meaning |
|---|---|---|
annotator_id |
string | ID of the annotator who gave this rating, for example annotator_1. |
scores |
object | Five scores, each 0-2. Keys: coherence, relevance, completeness, faithfulness, attribution. |
Each score is 0, 1, or 2: 0 means incorrect or unsupported, 1 means partially correct, 2 means fully correct.
Key numbers
Counted directly from the file:
| Metric | Value |
|---|---|
| Records | 132 |
| Claims | 244 |
| Attribution spans | 286 |
| Attribution spans verified | 286 of 286 (100%) |
| Human rating entries | 528 (4 annotators x 132 records) |
Genre distribution:
| Genre | Records |
|---|---|
| dialogue | 47 |
| encyclopedic | 44 |
| journalistic | 41 |
Difficulty distribution:
| Difficulty | Meaning | Records |
|---|---|---|
| 0 | retrieval | 39 |
| 1 | simple | 45 |
| 2 | complex | 48 |
Annotator agreement
Two numbers describe how consistently the four annotators rated the same records. They are given together because they tell different stories.
- Perfect agreement: 0.379 (50 of 132 records). This is the fraction of records where all four annotators gave the exact same score, on all five dimensions. It is easy to understand, but it does not correct for chance. Most scores in this dataset are the maximum value, so annotators can match by both giving the top score without truly agreeing on quality.
- Krippendorff's alpha (ordinal): 0.348 pooled across all dimensions. By dimension: coherence 0.057, relevance 0.373, completeness 0.477, faithfulness 0.375, attribution 0.311. Alpha corrects for chance agreement, and is the standard measure for rating reliability. A published guideline treats alpha >= 0.800 as reliable and 0.667-0.800 as tentative; below 0.667 is unreliable. Every dimension here falls below 0.667.
In short: annotators looked consistent by the simple measure, but the
chance-corrected measure shows weak agreement across every rating
dimension. Anyone using human_ratings should keep this in mind and not
treat the scores as a precise ground truth.
License
This dataset is licensed under CC BY-SA 4.0. See LICENSE.md.