Add Selfcheckgpt evaluation to tasks

[erenup]

No description provided.

[accesslint]

There are accessibility issues in these changes.

[CLAassistant]

All committers have signed the CLA.

[accesslint]

👏 You fixed the issue(s)! Great work.

[erenup]

I am trying to add a new task for llm hallucination. This is the very initial code. There is something I'd like to discuss with everyone about:

• How to generate multiple generations for one prompt elegantly？This could be very useful for many llm self-consistent evaluations.

I have implemented one possible solution. But I am not sure it is the best one.

Thank you very much.

[haileyschoelkopf]

I've left a few comments as review! I believe the functionality you want (N generations per document created) is already supported without any novel YAML config options. Please let me know if I am misunderstanding your intended functionality

[erenup]

hi @haileyschoelkopf @lintangsutawika, I have refactored the code so that it has its own task.py to deal with its unique multiple generations kwargs. In order to make it to be recognized by the llm-eval. I imported the selfcheckgpt in task/init.py which is similar to squadv2. Thank you very much.

[StellaAthena]

@erenup Thank you for your contribution to our library. We will not be able to merge this PR until you do the following:

1. Sign the CLA
2. Change the readme to accord with our template. It currently looks like you copied the readme from the official code, which will confuse users as it refers to code not found in this repo and uses the term "we" to refer to people other than EleutherAI.
3. Run some models through the code and compare results with the official implementation. Results on LLaMA, RWKV, Pythia, and T0 would cover all the major bases.

[erenup]

hi @StellaAthena Thank you.

• Sign the CLA.

• • done

• Change the readme to accord with our template. It currently looks like you copied the readme from the official code, which will confuse users as it refers to code not found in this repo and uses the term "we" to refer to people other than EleutherAI.

• • done

• Run some models through the code and compare results with the official implementation. Results on LLaMA, RWKV, Pythia, and T0 would cover all the major bases.

• • I have run some experiments and the results should be the same as the official repo since the evaluation code in task.py copies the selfchckgpt readme API and does not change other things. below numbers are results:

• some results:
  -- gpt-6b on SelfCheckNLI

export SELFCHECKGPTDEVICE=cuda
export SELFCHECKGPTTYPE=SelfCheckNLI
lm_eval --model hf \
    --model_args pretrained=${model_path_gpt-j-6b} \
    --tasks selfcheckgpt \
    --device cuda:0 \
    --batch_size 32

--

• -llama2 7b on SelfCheckNLI

export SELFCHECKGPTDEVICE=cuda
export SELFCHECKGPTTYPE=SelfCheckNLI
lm_eval --model hf \
    --model_args pretrained=${model_path_Llama-2-7b-chat-hf} \
    --tasks selfcheckgpt \
    --device cuda:0 \
    --batch_size 32

--

It has been addressed and a new review is needed

[StellaAthena]

@lintangsutawika @haileyschoelkopf this is now ready for your review again.