ArtificialAnalysis/Earnings22-Cleaned-AA
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kiriillb
published a
dataset about 1 month ago
kiriillb
updated 2
datasets about 1 month ago
declanjackson
authored a
paper about 2 months ago
Update topic name for geography and environmental sciences and update 2 duplicate questions
#6 opened 2 months ago
by 
declanjackson
declanjackson
updated a
dataset 2 months ago
add document ordering snippets
#10 opened 4 months ago
by 
kartikey-aa
kartikey-aa
updated a
dataset 4 months ago
Factual errors
1
#3 opened 4 months ago
by 
jordo45
update_questions
#5 opened 4 months ago
by declanjackson
Update README.md
#1 opened 4 months ago
by declanjackson
declanjackson
published a
dataset 4 months ago
What is passed to the LLM Equality Checker
1
#9 opened 5 months ago
by 
sophies-cb
Processing the dataset
👍 6
1
#3 opened 8 months ago
by 
stoshniwal
Unable to download all pdfs from the provided data_source_urls
🔥 9
3
#4 opened 8 months ago
by 
ronanki123
add-extracted-text
1
#6 opened 7 months ago
by kartikey-aa
feat/extracted-text
#7 opened 7 months ago
by kartikey-aa
Upload AA-LCR_Dataset.csv
#5 opened 7 months ago
by 
georgewritescode
georgewritescode
posted an update 8 months ago
Post
3333
Announcing Artificial Analysis Long Context Reasoning (AA-LCR), a new benchmark to evaluate long context performance through testing reasoning capabilities across multiple long documents (~100k tokens)
The focus of AA-LCR is to replicate real knowledge work and reasoning tasks, testing capability critical to modern AI applications spanning document analysis, codebase understanding, and complex multi-step workflows.
AA-LCR is 100 hard text-based questions that require reasoning across multiple real-world documents that represent ~100k input tokens. Questions are designed so answers cannot be directly found but must be reasoned from multiple information sources, with human testing verifying that each question requires genuine inference rather than retrieval.
Key takeaways:
➤ Today’s leading models achieve ~70% accuracy: the top three places go to OpenAI o3 (69%), xAI Grok 4 (68%) and Qwen3 235B 2507 Thinking (67%)
➤👀 We also already have gpt-oss results! 120B performs close to o4-mini (high), in-line with OpenAI claims regarding model performance. We will be following up shortly with a Intelligence Index for the models.
➤ 100 hard text-based questions spanning 7 categories of documents (Company Reports, Industry Reports, Government Consultations, Academia, Legal, Marketing Materials and Survey Reports)
➤ ~100k tokens of input per question, requiring models to support a minimum 128K context window to score on this benchmark
➤ ~3M total unique input tokens spanning ~230 documents to run the benchmark (output tokens typically vary by model)
We’re adding AA-LCR to the Artificial Analysis Intelligence Index, and taking the version number to v2.2. Artificial Analysis Intelligence Index v2.2 now includes: MMLU-Pro, GPQA Diamond, AIME 2025, IFBench, LiveCodeBench, SciCode and AA-LCR.
Link to dataset: ArtificialAnalysis/AA-LCR
The focus of AA-LCR is to replicate real knowledge work and reasoning tasks, testing capability critical to modern AI applications spanning document analysis, codebase understanding, and complex multi-step workflows.
AA-LCR is 100 hard text-based questions that require reasoning across multiple real-world documents that represent ~100k input tokens. Questions are designed so answers cannot be directly found but must be reasoned from multiple information sources, with human testing verifying that each question requires genuine inference rather than retrieval.
Key takeaways:
➤ Today’s leading models achieve ~70% accuracy: the top three places go to OpenAI o3 (69%), xAI Grok 4 (68%) and Qwen3 235B 2507 Thinking (67%)
➤👀 We also already have gpt-oss results! 120B performs close to o4-mini (high), in-line with OpenAI claims regarding model performance. We will be following up shortly with a Intelligence Index for the models.
➤ 100 hard text-based questions spanning 7 categories of documents (Company Reports, Industry Reports, Government Consultations, Academia, Legal, Marketing Materials and Survey Reports)
➤ ~100k tokens of input per question, requiring models to support a minimum 128K context window to score on this benchmark
➤ ~3M total unique input tokens spanning ~230 documents to run the benchmark (output tokens typically vary by model)
We’re adding AA-LCR to the Artificial Analysis Intelligence Index, and taking the version number to v2.2. Artificial Analysis Intelligence Index v2.2 now includes: MMLU-Pro, GPQA Diamond, AIME 2025, IFBench, LiveCodeBench, SciCode and AA-LCR.
Link to dataset: ArtificialAnalysis/AA-LCR
mhillsmith
updated a
dataset 8 months ago