A new evaluation protocol called Seq@K has been introduced to better assess language models' ability to learn from sequential feedback. Unlike traditional metrics like Pass@1 or Pass@K, Seq@K measures a model's success in solving a problem within a set number of attempts, incorporating feedback after each failure. This method reveals a different landscape of model performance, highlighting variations in rankings and the potential for more efficient configurations. Analyzing model trajectories under Seq@K also provides insights into their iterative refinement behaviors and how feedback influences their responses, emphasizing the need for careful design of feedback loops in agentic systems. AI
IMPACT Introduces a more accurate evaluation metric for agentic AI systems, potentially guiding future model development and deployment.
RANK_REASON The cluster contains a research paper introducing a new evaluation metric for language models. [lever_c_demoted from research: ic=1 ai=1.0]
- alphaXiv
- arXiv
- CatalyzeX
- DagsHub
- Gotit.pub
- Hugging Face
- Influence Flower
- Pass@1
- Pass@K
- ScienceCast
- Seq@K
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