A Benchmark for Scalable Oversight Protocols
Fuente:
arXiv
Salvato in:
| Autori principali: | , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2025
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866912310499475456 |
|---|---|
| author | Sudhir, Abhimanyu Pallavi Kaunismaa, Jackson Panickssery, Arjun |
| author_facet | Sudhir, Abhimanyu Pallavi Kaunismaa, Jackson Panickssery, Arjun |
| contents | As AI agents surpass human capabilities, scalable oversight -- the problem of effectively supplying human feedback to potentially superhuman AI models -- becomes increasingly critical to ensure alignment. While numerous scalable oversight protocols have been proposed, they lack a systematic empirical framework to evaluate and compare them. While recent works have tried to empirically study scalable oversight protocols -- particularly Debate -- we argue that the experiments they conduct are not generalizable to other protocols. We introduce the scalable oversight benchmark, a principled framework for evaluating human feedback mechanisms based on our agent score difference (ASD) metric, a measure of how effectively a mechanism advantages truth-telling over deception. We supply a Python package to facilitate rapid and competitive evaluation of scalable oversight protocols on our benchmark, and conduct a demonstrative experiment benchmarking Debate. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_03731 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Benchmark for Scalable Oversight Protocols Sudhir, Abhimanyu Pallavi Kaunismaa, Jackson Panickssery, Arjun Artificial Intelligence As AI agents surpass human capabilities, scalable oversight -- the problem of effectively supplying human feedback to potentially superhuman AI models -- becomes increasingly critical to ensure alignment. While numerous scalable oversight protocols have been proposed, they lack a systematic empirical framework to evaluate and compare them. While recent works have tried to empirically study scalable oversight protocols -- particularly Debate -- we argue that the experiments they conduct are not generalizable to other protocols. We introduce the scalable oversight benchmark, a principled framework for evaluating human feedback mechanisms based on our agent score difference (ASD) metric, a measure of how effectively a mechanism advantages truth-telling over deception. We supply a Python package to facilitate rapid and competitive evaluation of scalable oversight protocols on our benchmark, and conduct a demonstrative experiment benchmarking Debate. |
| title | A Benchmark for Scalable Oversight Protocols |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2504.03731 |