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https://github.com/manyoso/haltt4llm

This project is an attempt to create a common metric to test LLM's for progress in eliminating hallucinations which is the most serious current problem in widespread adoption of LLM's for many real purposes.
https://github.com/manyoso/haltt4llm

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This project is an attempt to create a common metric to test LLM's for progress in eliminating hallucinations which is the most serious current problem in widespread adoption of LLM's for many real purposes.

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# HALTT4LLM - Hallucination Trivia Test for Large Language Models

This project is an attempt to create a common metric to test LLM's for

progress in eliminating hallucinations; the most serious current problem

in widespread adoption of LLM's for real world purposes.



## Results (as of March 2023)



| Model Name            | Truthful QA | C    | IDK | HQ Trivia | C    | IDK | Fake Questions | C   | NOTA Questions | C   | IDK |

|-----------------------|-------------|------|-----|-----------|------|-----|----------------|-----|----------------|-----|-----|

| **GPT4All**           | 79.51%      | 582  | 8   | **88.47%**| 1243 | 7   | 74.16%         | 310 | **70.32%**     | 109 | 0   |

| **GPT-3.5**           | 39.95%      | 142  | 246 | 59.33%    | 705  | 262 | **81.81%**     | 342 | 51.93%         | 58  | 45  |

| **GPT-3**             | 32.15%      | 220  | 7   | 55.67%    | 776  | 17  | 6.10%          | 26  | 32.25%         | 43  | 14  |

| **Llama-7B-4bit**     | **83.51%**  | 614  | 3   | 49.75%    | 701  | 0   | 2.15%          | 18  | 8.38%          | 26  | 0   |

| **Alpaca-7B-4bit**    | 26.66%      | 196  | 1   | 44.32%    | 624  | 1   | 0.00%          | 0   | 0.00%          | 0   | 0   |

| **GPT-4**             |             |      |     |           |      |     |                |     |                |     |     |



* **C** *number of correct answers*

* **IDK** *number of 'I don't know' answers*

* **NOTA** *stands for None of the Above*

* **HQ Trivia** - 1409 questions

* **Fake Questions** - 418 questions

* **NOTA Questions** - 155 questions



## Scoring



The scoring is as follows:



*  **+2** for a correct answer

*  **1** for an uncertain (I don't know) answer

*  **0** for an incorrect answer



* *HQ Trivia, Fake Questions and NOTA questions all have exactly 5 possible

answers per question. The average score for a random answer taker in these

three tests would be 60% under our scoring system.*



The idea here is for LLM's to make progress correctly answering 'I don't

know' while maintaining a high score of otherwise correct answers. The point

is to demonstrate progress in solving the problem of hallucinations in while

still maintaining a high degree of correct and confidence answers in LLM's.



## Strategy



The strategy here is to create a common dataset of trivia questions in multiple

choice format as well as a script to test various models against these questions.

All of the trivia questions include an 'I don't know' option as well as a

'None of the above' option. The trivia questions include a set of fake or

trick questions where 'I don't know' is the correct response as well as a

set of questions where 'None of the above' is the correct response. This

in addition to a large corpus of real trivia questions with objective and 

unambiguous correct real world answers.



The resulting scores across these three sets can serve as a baseline to

test various techniques/methods to mitigate hallucinations in LLMs.



## Trivia datasets



The questions consist of the following trivia sets:



* truthfulqa_trivia_questions.json - taken from https://github.com/sylinrl/TruthfulQA/blob/main/TruthfulQA.csv

and cleaned to support multiple choice format used here where each question

has exactly one correct answer, two incorrect answers, one 'i don't know'

answer and one 'none of the above' answer. The correct/incorrect answers

were chosen randomly from the sets of correct/incorrect answer choices in

the file. The answer 'I have no comment' was not chosen as it is serving

the same person as our 'I don't know' answer. I've also removed all indexical

questions as these are in conflict with what we are trying to do and seem

to only apply to OpenAI. Finally, a few of the questions had a single incorrect 

answer so these were removed resulting in 737 questions.



* hq_trivia_questions.json - taken from https://www.kaggle.com/datasets/theriley106/hq-trivia-question-database

and cleaned of various ill-formatting. These questions have not been checked

or independently verified for quality and any reports of problems with the

dataset (incorrect answers, formatting problems, ambiguity, etc) would be

greatly appreciated. Also, anyone with suggestions for other high quality

trivia questions that have been independently or credibly checked in multiple

choice format would be greatly appreciated. Currently 1409 questions.



* fake_trivia_questions.json - these are generated by GPT3.5 with the prompt

in the repository and the script to generate them. Currently 418 questions.



* none_of_the_above_questions.json -  also generated by GPT3.5 with script

and prompt in repository. Currently 155 questions.



Each of these question sets are in the same json format and include three

standard choices as well as a 'I don't know' and 'None of the above' choice

for each question. Again, any reports of problems with the questions would

be greatly appreciated.



## Discussion



While GPT-3.5 clearly scored the best in three of the tests it is notable to

point out that even though it was responsible for *creating* the fake and

none of the above trivia sets it still far from aced them indicating significant

hallucination problems exist even with the benefit of creating the questions

themselves. It would be interesting to see what would happen to the score

of GPT-3.5 if the questions were generated with a non-openai derived models.

That said, it is clear that GPT-3.5 is dramatically better than the llama

and alpaca models when it comes to this metric trying to quantify hallucinations.

GPT-3.5 had a *dramatically* better score in the fake question test than

either of those two. What is interesting is that it didn't do that much

better on the NOTA tests in comparison even thought it was still responsible

for coming up with these questions. And GPT4All is competitive with the

openai models in all and outpacing in some.



Whether because of overall quality increase between openai models or because

of increased alignment work done it is clear that 3.5 has a much easier

time admitting uncertainty compared to its predacessor. The accuracy actually

decreased though between GPT-3 and GPT-3.5 although not by enough to definitely

say it is because of the hallucination mitigation techniques OpenAI must

have employed between GPT-3 and GPT-3.5.



When it comes to uncertainty and handling hallucinations it is clear that

both openai models are far and away superior to Llama 7B and Alpaca Lora

which does not admit uncertainty under any circumstance. This is in keeping

with qualitative first hand experience of many human users who report a

marked increase in hallucinations from the Stanford Alpaca derived models

in comparison to the OpenAI models.



On the positive side, the overall score of correct answers on the real HQ

Trivia test for Alpaca Lora 7B was very decent in comparison to GPT-3. Alpaca

also suffers from sometimes not responding correctly to the prompt with

trying to generate new questions instead of answering them in a clear enough

matter that doesn't require a more complex regex or parser. It's possible

that training alpaca on test taking in this format would provide a modest

boost to the score.



The amazing standout is the new GP4All model which was trained on ~800k

new prompts generated from GPT-3.5 output. In fact, this model is outscoring

GPT-3.5 itself on the real trivia set by a wide margin and still managing

to finish second in the fake hallucination test with a very respectable

74.16% correct!



Another shocking finding is the TruthfulQA scores. This was a test written

in part by OpenAI and so I would have expected the OpenAI models to do well

here. While GPT-3.5 was the most comfortable admitting uncertainty by a

wide margin, the real stars were Llama and GPT4All! No idea why Alpaca Lora

which is based on the Llama base model would fare so poorly, but it does

at least on my tests. Hypothesis is that GPT-3.5 was trained to pass this

TruthfulQA by admitting uncertainty in part.



In the future it will be interesting to see how GPT-4 fares in comparison

to GPT-3.5 with this test. Also, would be nice to establish a baseline for

other widespread models such as the Llama based ones and different size

Alpaca and GPT4All.



## Contributing



As mentioned above, we would greatly appreciate any efforts to validate and

check the datasets for correctness. If you find any errors please don't

hesitate to open a PR or ticket in github's bug tracker.



## Setup and install



Install python dependencies.



```

pip install -r requirements.txt

```



Testing yourself against Alpaca Lora 7B (4bit) you need to execute the

following to download the model/lora/weights and put them in the correct

directory for the take_test.py to find the model correctly.



```

python download-model.py --text-only decapoda-research/llama-7b-hf

wget https://huggingface.co/decapoda-research/llama-7b-hf-int4/resolve/main/llama-7b-4bit.pt -P ./weights

python download-model.py samwit/alpaca7B-lora

python download-model.py nomic-ai/gpt4all-lora

```



## Examples for running the tests



Running the HQ Trivia test on OpenAI GPT-3.5



```

python take_test.py --use-gpt3-5 --openai-key  --trivia hq_trivia_questions.json

```



Running the Fake Trivia test on OpenAI GPT-3



```

python take_test.py --use-gpt3 --openai-key  --trivia fake_trivia_questions.json

```



Running the NOTA (None of the Above) Trivia test on Alpaca Lora 7B (4bit)



```

python take_test.py --trivia nota_trivia_questions.json

```



These will all produce test result files at the end named according to the test and the model.



## Testing other models



The repository and script are currently setup only to test OpenAI's GPT3

and GPT3.5 as well as Alpaca Lora 7B (4bit). To add a new model it would

only take a bit of work to edit the 'take_test.py' for instance adding

other alpaca lora models or GPT4All and any additions would be greatly

appreciated.



## Process to generate fake and nota (none of the above) questions



The fake and nota questions were generated by a script `generate_trivia.py` that calls OpenAI's server

to generate fake and nota trivia questions according to a prompt in a broad range of trivia categories.

The script `filter_questions.py` was then used to go through each question and discard any that are

clearly wrong. Others are welcome to generate more of these questions and add them to the current datasets

to increase diversity and further checks on correctness.