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Researching Alignment Research: Unsupervised Analysis

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arxiv 2206.02841 v1 pith:I5YL6YER submitted 2022-06-06 cs.CY cs.AI

classification cs.CYcs.AI
keywords researchfieldresearchersalignmentarticlesdatasetdifferentfound
verification ladder T0 review T1 audit T2 compute T3 formal
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AI alignment research is the field of study dedicated to ensuring that artificial intelligence (AI) benefits humans. As machine intelligence gets more advanced, this research is becoming increasingly important. Researchers in the field share ideas across different media to speed up the exchange of information. However, this focus on speed means that the research landscape is opaque, making it difficult for young researchers to enter the field. In this project, we collected and analyzed existing AI alignment research. We found that the field is growing quickly, with several subfields emerging in parallel. We looked at the subfields and identified the prominent researchers, recurring topics, and different modes of communication in each. Furthermore, we found that a classifier trained on AI alignment research articles can detect relevant articles that we did not originally include in the dataset. We are sharing the dataset with the research community and hope to develop tools in the future that will help both established researchers and young researchers get more involved in the field.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Similar Models Learn Differently: Final-Window Pretraining Shapes Post-Training Beyond SFT

    cs.AI 2026-07 conditional novelty 7.0 of 10

    A 500M-token final pretraining window of safety text leaves matched post-SFT models that lose far less refusal under identical DPO or GRPO than web-text counterparts.

  2. Toward a Theory of Value in AI Alignment

    cs.AI 2026-08 conditional novelty 6.0 of 10

    A systematic annotation of 94 AI alignment papers shows the field largely equates human values with measurable preferences, rarely defines values, and is increasingly removing humans from alignment evaluation.

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