CLUAS: Comprehensive Listening and Understanding ASsessment is an Irish language Speech LLM evaluation dataset based on the Higher Level Irish Leaving Certificate listening-comprehension papers for 2013–2025.

13 exam years and 780 available marks (60 per year)
104 question-bearing, 16 kHz mono WAV audio clips (one per dataset row)
334 questions (text) grouped by their respective audio clip
gold transcript
marking-scheme rubrics

Exam Structure

The structure and mark allocation are consistent across all 13 exam years:

Section	Material	Dataset clips	Marks
Cuid A	Two announcements (`Fógra 1` and `Fógra 2`)	2	18
Cuid B	Two conversations (`Comhrá 1` and `Comhrá 2`), each divided into two `mír` question clips	4	28
Cuid C	Two spoken pieces (`Píosa 1` and `Píosa 2`)	2	14
Total	Six exam items represented by eight question-bearing clips	8	60

Announcements and pieces are heard twice in the exam. Each conversation is first heard in full and is then heard in two parts. CLUAS uses the question-bearing hearing for each clip. The number of question records varies between years because an exam question may contain several separately marked parts, but the section and overall mark totals do not change.

The official schemes award 60 content marks (Eolas) and then allow a separate overall deduction of 0–3 marks for the standard of Irish (Cumas Gaeilge). CLUAS currently stores and reports the 60 content marks question by question; it does not model that separate holistic language deduction.

Loading

from datasets import load_dataset

ds = load_dataset("jmcinern/CLUAS", split="test")
example = ds[0]
audio = example["audio"]
for question in example["questions"]:
    print(question["question_id"], question["question"])

The file_name field links one primary clip to each row. The Hugging Face audio-folder viewer renders that clip as the row's audio player.

Evaluation protocol

Run the same model in three conditions on every question:

Baseline: no_context: the models baseline performance with no context. Some of the questions are quite guessable / refer to real world events.
just_audio: the models performance with the speech clip as context
just_transcript: the models performance with the marking scheme provided transcript

Evaluate each free-text answer with a separate LLM judge. Give the judge the question, candidate answer, marking_scheme, marks_available, and blanks_required. Never give the marking scheme or gold transcript to the model being evaluated.

The functions answer_with_llm and judge_with_llm below stand for calls to your preferred speech LLM and judge APIs. Configure them with your API key and model.

import json
from datasets import load_dataset

ds = load_dataset("jmcinern/CLUAS", split="test")

JUDGE_PROMPT = """TASK: You are an LLM-as-a-judge tasked with marking a leaving cert Irish listening comprehension exam question
that has been repurposed for Speech LLM evaluation. The following are presented to you:

A: The question
B: The candidate's answer
C: The transcript of the audio clip
D: The marking scheme

You must use the marking scheme to score the the candidate's answer to the question following the marking scheme
using the transcript of the audio clip as context.

The exam does not penalise grammar unless it prohibits understanding.

The marking scheme is guidance to how you should mark the question in relation to the transcript. 

Return only the digit repsonse of the marks you award to this question"""

Example

Question ID	Question number	Question	Marking scheme	Marks
`2013-B-comhra1-q1`	1	Cad a rinne Orla inniu?	Chríochnaigh sí an Ardteist = 2 mharc; Bhí an scrúdú deireanach aici inniu = 2 mharc; Bitheolaíocht = 2 mharc; Scrúdú san Ardteist = 1 mharc; Ardteist = 0	2