HebArabNlpProject/abstractive-qa-llm-eval

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.

Credits