AXN:0ACB.CAPTURE

AXN:0ACB.CAPTURE.📖🛡️🕐🔖❌↖️

Observation

Google AI Overview, 2026-10-29 · the capture

Description

ATTRIBUTIONBENCH, BY NAME, ON GOOGLE'S AI OVERVIEW, SIGNED OUT, 2026-10-29: 'a comprehensive research benchmark designed to evaluate automatic attribution and citation faithfulness in Large Language Model (LLM) and generative search engine responses.' A unified testbed of seven datasets (ExpertQA, Stanford-GenSearch, AttributedQA, LFQA, BEGIN, HAGRID, AttrEval-GenSearch), the task (is every claim fully supported by its cited evidence: attributable or not), the data on Hugging Face and GitHub; findings (about 80% macro-F1 for GPT-4 and fine-tuned GPT-3.5, 60–70% on harder sets; over 66% of errors from fine-grained information insensitivity, nearly 27% from a mismatch of access and context). Five cards: arXiv 2402.15089, the OSU-NLP-Group repository, the project page, the Hugging Face dataset, the ACL Anthology.

Reading

The innocent reading first. The Overview identifies the benchmark, what it compiles, what it asks and how hard models find it, all from the paper and its repositories.

Analysis

An attribution benchmark is composed at full grain from its own paper and kept distinct from its homonym, and its makers are left in the cards. The record's negative tag, that support by evidence is not credit to the original author, is the line between this entity and the next.

Match

BROAD MATCH — ATTRIBUTIONBENCH ON THE OVERVIEW — OSU'S BENCHMARK EXACT: SEVEN SOURCE DATASETS, THE BINARY TASK, ABOUT 80% MACRO-F1; KEPT APART FROM ATTRIBENCH

PER

0.5 — No person or institution in the answer; the arXiv identifier is in a card; the paper, repository and dataset cited.

Citations

5 (0 archive-controlled)

Sealed observation

/data/captures/sealed/attributionbench-aio-20261029.json — SHA-256 412d6248efdd3f8e12c1b0a03213243c60f7cfa1f19fe45012e43296d743133e