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Semantic Scholar

by Allen Institute for AI

AI Application
Low Confidence

Semantic Scholar is a free academic search engine run by Ai2, a non-profit AI research institute in Seattle. It indexes over 200 million papers and offers a library, alerts, research feeds, an AI-augmented PDF reader (Semantic Reader) and a public API and datasets. Researchers and students use it to find and skim papers, follow citations and get paper recommendations. The vendor says machine learning extracts meaning and connections from papers, generates one-sentence TLDR summaries, identifies highly influential citations, assigns fields of study and learns from users' ratings to recommend papers, and that its Ask This Paper feature sends the paper's text and the user's question to OpenAI's gpt-3.5-turbo-16k. The vendor warns that generated text will contain errors that can be hard to detect and asks users to verify it.

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Transparency Score

Weighted across four pillars · updated October 19, 2026

Overall grade
40% weighted
ABCDF

Capabilities

Literature Search
Paper Recommendation
Summarization
Citation Analysis
Question Answering
Field Of Study Classification

AI Models

Underlying AI models powering this application

GPT-3.5

Vendor Information

Complete information about the vendor/provider of this AI application

Allen Institute for AIView all products →
The Allen Institute for Artificial Intelligence(Trading as: Ai2)
Contact Information
allenai.org/
legal@allenai.org
Registered Address
2157 North Northlake Way, Suite 110, Seattle, WA, 98103, US

Infrastructure

Platforms hosting this application — each inherits its own transparency grade.

OpenAI API
OpenAI
F
View full infrastructure →

EU AI Act Provider Information

Verification Status:
Verified
The Allen Institute for Artificial Intelligence(Trading as: Ai2)
2157 North Northlake Way, Suite 110, Seattle, WA, 98103, US
legal@allenai.org
Compliance Documents
CE Marking: Not Applicable
Publishes a channel for reporting security incidents or vulnerabilities: https://www.semanticscholar.org/.well-known/security.txt (read 19 October 2026).
For Semantic Scholar, Ai2 says text from its generative AI features (Ask This Paper, Generative Term Understanding) 'will not be free of errors, and the errors could be difficult to detect', including subtle or serious factual inaccuracies and irrelevant information, and that users should verify generated text; Ask This Paper has only been tested on English-language papers, and field-of-study classification is limited to English-language papers (https://www.semanticscholar.org/faq, read 19 October 2026).

Potential Risks

1 considerations identified

1

Review recommended before use

These considerations are automatically identified based on publicly available information about the vendor and AI catalog data. Actual risks may vary based on your specific use case and implementation.

Supply Chain Network

Visual representation of the vendor's digital supply chain relationships

Compliance & Risk

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Work at Allen Institute for AI? Claim this listing to correct or complete the data.

Added: October 19, 2026
Updated: October 19, 2026

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