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Multi-Modality

Swarm Evals

Evaluations comparing multi-agent collaboration to individual agents.

Install

$ pip3 install swarms-eval

License

MIT

Citation

Please cite Swarms in your paper or your project if you found it beneficial in any way! Appreciate you.

@misc{swarms,
  author = {Gomez, Kye},
  title = {{Swarms: The Multi-Agent Collaboration Framework}},
  howpublished = {\url{https://github.com/kyegomez/swarms}},
  year = {2023},
  note = {Accessed: Date}
}

Evals

  • ARC (AI2 Reasoning Challenge)
  • HellaSwag
  • Multiverse Math
  • MMLU
  • GLUE
  • SuperGPU
  • HumanEval:
  • MBPP
  • SWE-BENCH

Evals to make

Math

[ ] MATH

code

[ ] HumanEval

[ ] MBPP

[ ] Natural2Code

[ ] MBPP (early)

[ ] SWE-bench

commonsense reasoning

[ ] ARC (AI2 Reasoning Challenge)

[ ] HellaSwag

[ ] Winogrande

[ ] PIQA

[ ] SIQA

[ ] OpenbookQA

[ ] CommonsenseQA

Question Answering - world knowledge

[ ] WebQuestions

[ ] NaturalQuestions

[ ] TriviaQA

[ ] ComplexWebQuestions

[ ] WebQuestionsSP

[ ] SearchQA

[ ] HotpotQA

[ ] DROP

[ ] WikiHop

[ ] QAngaroo

[ ] Multi

[ ] GLUE (early)

[ ] SuperGLUE

reading comprehension

[ ] BoolQ

[ ] QuAC

[ ] DROP

aggregated

[ ] MMLU

[ ] HELM

[ ] BBH

[ ] AGI Eval

multi-agent

[ ] ChatBot Arena

[ ] MT Bench

Metrics

[ ] Task accomplished y/n

[ ] Task quality 1-5

[ ] was a tool used y/n [ ] which tool [ ] how was it used [ ] were tools called in the right order [ ] were the tools used correctly? Did the environment change? [ ] Did the agent output match the expected reference output?

[ ] Compute load

[ ] Cost $ " the cost per 1000 function callings alt text"

[ ] Average Latency (s) "Latency: we measure the latency by timing each request to the endpoint ignoring the function document preprocessing time."

[ ] Model size needed (Small, Medium, Large) [ ] Model type needed (LM, VLM, predictive, point cloud, NeRF/Splat) [ ] Number of trials to acceptable performance [ ] Number of trials to best performance

References

[1]Functional Benchmarks for Robust Evaluation of Reasoning Performance, and the Reasoning Gap (https://arxiv.org/html/2402.19450v1) Code (https://github.com/consequentai/fneval/)

[2]explanation of metrics (https://gorilla.cs.berkeley.edu/blogs/8_berkeley_function_calling_leaderboard.html)

[3] tool use benchmarks (https://langchain-ai.github.io/langchain-benchmarks/notebooks/tool_usage/intro.html?ref=blog.langchain.dev) (https://blog.langchain.dev/benchmarking-agent-tool-use/)

[4] Blog on evaluating LLM apps (https://humanloop.com/blog/evaluating-llm-apps)