cd199c7ab7
* Initial commit of AutoGenBench * wording * typo * pre-commit reformulation * Updated README to point to contributor's guide earlier. * Simplified the description of the JSON format. * Added print statements to indicate when run.sh and scenario.py are starting. * Added SocietyOfMind scenario to GAIA. * Pointing autogenbench clone command to the latest branch. * Temporarily disable subsample option. * Updated the GAIA readme to specify how to define a BING API key. * Fixed and re-enabled the subsample option. * Added a draft of a blog post. * Updated authors. * Incorporating Gagan's feedback. * Fixed code formatting. * Updated the help string in the docs. * Light editing of the AutoGenBench blogpost. * Support filtering on model tags. * Added websurfer dependencies to Dockerfile. * Renamed testbed -> autogenbench * Attempting to fix formatting. * Added more gracefull handling of task timeouts (the script is allowed to terminate before Docker is stopped). * Updated the blogpost based on Saleema's and Julia's feedback. * Fixed formatting... again. * Added a main MANIFEST to list available scenarios. * Limit main manifest to directories. * Manifests now use relative paths. * All manifests are now relative. * Updated the contributing guide, and address windows path issues. * Updated the version. Fixed formatting. * Fixed formatting. * De-listing Examples, since it has no clear tabulate criteria. * Updated email in pyproject * typo in blogpost * wording --------- Co-authored-by: Qingyun Wu <qingyun.wu@psu.edu> Co-authored-by: Qingyun Wu <qingyun0327@gmail.com>
HumanEval Benchmark
This scenario implements a modified version of the HumanEval benchmark. Compared to the original benchmark, there are two key differences here:
- A chat model rather than a completion model is used.
- The agents get pass/fail feedback about their implementations, and can keep trying until they succeed or run out of tokens or turns.
Running the tasks
autogenbench run Tasks/human_eval_two_agents.jsonl
autogenbench tabulate Results/human_eval_two_agents
For faster development and iteration, a reduced HumanEval set is available via Tasks/r_human_eval_two_agents.jsonl, and contains only 26 problems of varying difficulty.
References
Evaluating Large Language Models Trained on Code
Mark Chen, Jerry Tworek, Heewoo Jun, Qiming Yuan, Henrique Ponde de Oliveira Pinto, Jared Kaplan, Harri Edwards, Yuri Burda, Nicholas Joseph, Greg Brockman, Alex Ray, Raul Puri, Gretchen Krueger, Michael Petrov, Heidy Khlaaf, Girish Sastry, Pamela Mishkin, Brooke Chan, Scott Gray, Nick Ryder, Mikhail Pavlov, Alethea Power, Lukasz Kaiser, Mohammad Bavarian, Clemens Winter, Philippe Tillet, Felipe Petroski Such, Dave Cummings, Matthias Plappert, Fotios Chantzis, Elizabeth Barnes, Ariel Herbert-Voss, William Hebgen Guss, Alex Nichol, Alex Paino, Nikolas Tezak, Jie Tang, Igor Babuschkin, Suchir Balaji, Shantanu Jain, William Saunders, Christopher Hesse, Andrew N. Carr, Jan Leike, Josh Achiam, Vedant Misra, Evan Morikawa, Alec Radford, Matthew Knight, Miles Brundage, Mira Murati, Katie Mayer, Peter Welinder, Bob McGrew, Dario Amodei, Sam McCandlish, Ilya Sutskever, Wojciech Zaremba
https://arxiv.org/abs/2107.03374