input stringclasses 10
values | model_output stringclasses 6
values | expected_output stringclasses 10
values |
|---|---|---|
What is 19 multiplied by 17 | 19 x 17 = 35 | 323 |
What is 13 multiplied by 19 | 13 x 19 = 224 | 247 |
Which number is bigger 9.11 or 9.99 | 9.11 | 9.99 |
What is the capital of Australia | Prompt repeated | Canberra |
How many continents exist on Earth | Prompt repeated | 7 |
Who invented the telephone | Prompt repeated | Alexander Graham Bell |
What is 14 multiplied by 18 | Prompt repeated | 252 |
Which number is larger 0.99 or 1.00 | Prompt repeated | 1 |
If today is Monday what day comes after tomorrow | Tomorrow is Tuesday | Wednesday |
If you have three apples and give away two how many apples remain | Three apples remain | One apple remains |
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Check out the documentation for more information.
TinyLlama Blind Spots Dataset
Overview
This dataset documents failure cases observed while testing the TinyLlama 1.1B Chat model.
The goal of the dataset is to identify situations where the model produces incorrect answers, incomplete responses, or fails to respond properly.
These examples highlight several blind spots in the model’s reasoning and knowledge capabilities.
Model Tested
Model: TinyLlama/TinyLlama-1.1B-Chat-v1.0
Model page:
https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v1.0
How the Model Was Loaded
The model was loaded using the Hugging Face Transformers library in a Google Colab environment.
Python code used to load the model:
from transformers import AutoTokenizer, AutoModelForCausalLM
model_name = "TinyLlama/TinyLlama-1.1B-Chat-v1.0"
tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForCausalLM.from_pretrained(model_name)
def ask_model(prompt): inputs = tokenizer(prompt, return_tensors="pt") outputs = model.generate(**inputs, max_new_tokens=50) answer = tokenizer.decode(outputs[0], skip_special_tokens=True) return answer
Experiment Setup
The model was tested using multiple prompts designed to evaluate different reasoning abilities.
Prompts were selected from several categories including:
- Arithmetic reasoning
- Numerical comparison
- Factual knowledge questions
- Logical reasoning problems
The objective was to observe cases where the model produced incorrect answers, incomplete responses, or repeated the prompt instead of answering.
Each dataset entry contains three fields:
input
The prompt given to the model.
model_output
The response generated by the model.
expected_output
The correct answer.
Observed Blind Spots
Arithmetic Reasoning Errors
The model frequently produces incorrect results for multiplication and arithmetic problems.
Numerical Comparison Errors
The model sometimes fails to correctly compare decimal numbers.
Factual Knowledge Failures
For several simple factual questions the model repeats the prompt instead of providing an answer.
Logical Reasoning Mistakes
The model struggles with simple reasoning tasks such as word problems involving counting or basic logic.
Repetition Loops
The model sometimes enters repetitive output patterns, producing the same phrase multiple times.
Potential Improvements
These failures suggest the model would benefit from additional fine tuning on curated reasoning datasets.
Examples of useful datasets include:
- Mathematical reasoning datasets
- Numerical comparison datasets
- Factual question answering datasets
- Logical reasoning benchmarks
Training on structured reasoning examples could help the model produce more reliable outputs.
Estimated Dataset Size for Improvement
To address these blind spots effectively, a dataset containing approximately 20,000 to 100,000 carefully labeled reasoning examples would likely be required for fine tuning.
Such a dataset could improve the model’s ability to perform:
- Arithmetic reasoning
- Logical problem solving
- Factual question answering
and reduce repetitive or incomplete outputs.
license: apache-2.0
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