We lifted 8 functions out of this paper's own repositories and ran 8 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| instadeepai/debatellm | canonical | 8 of 8 |
| Function | Status | Where it lives |
|---|---|---|
| construct_agent_metrics | Ran | instadeepai/debatellm/debatellm/systems.py code served (permissive licence) · get_code("bb1148f2cec420ce") |
| construct_debate_metrics | Ran | instadeepai/debatellm/debatellm/systems.py code served (permissive licence) · get_code("0cdd7d0ef03c6c12") |
| construct_summary_message | Ran | instadeepai/debatellm/debatellm/utils/debate.py code served (permissive licence) · get_code("804cb18667163780") |
| eval_on_batch | Ran | instadeepai/debatellm/debatellm/eval/eval_system.py code served (permissive licence) · get_code("00df3fdd43bc6ce8") |
| load_openai_api_key | Ran | instadeepai/debatellm/debatellm/utils/openai.py code served (permissive licence) · get_code("3e468a12610c5a77") |
| partial_format | Ran | instadeepai/debatellm/debatellm/utils/debate.py code served (permissive licence) · get_code("63ceab3528839d6c") |
| remove_spaces_in_name | Ran | instadeepai/debatellm/debatellm/utils/debate.py code served (permissive licence) · get_code("640440f246b1877d") |
| try_except_decorator | Ran | instadeepai/debatellm/debatellm/agents.py code served (permissive licence) · get_code("fd00140a4206a01e") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Recent advancements in large language models (LLMs) underscore their potential for responding to inquiries in various domains. However, ensuring that generative agents provide accurate and reliable answers remains an ongoing challenge. In this context, multi-agent debate (MAD) has emerged as a promising strategy for enhancing the truthfulness of LLMs. We benchmark a range of debating and prompting strategies to explore the trade-offs between cost, time, and accuracy. Importantly, we find that multi-agent debating systems, in their current form, do not reliably outperform other proposed prompting strategies, such as self-consistency and ensembling using multiple reasoning paths. However, when performing hyperparameter tuning, several MAD systems, such as Multi-Persona, perform better. This suggests that MAD protocols might not be inherently worse than other approaches, but that they are more sensitive to different hyperparameter settings and difficult to optimize. We build on these results to offer insights into improving debating strategies, such as adjusting agent agreement levels, which can significantly enhance performance and even surpass all other non-debate protocols we evaluated. We provide an open-source repository to the community with several state-of-the-art protocols together with evaluation scripts to benchmark across popular research datasets.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2311.17371")
get_code_for_paper("2311.17371")
have("2311.17371")
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