feat/extracted-text

README.md

@@ -1,37 +1,14 @@
 ---
 license: apache-2.0
-configs:
-- config_name: default
-  data_files:
-  - split: test
-    path: "AA-LCR_Dataset.csv"
 ---
 
 # Artificial Analysis Long Context Reasoning (AA-LCR) Dataset
 
 AA-LCR includes 100 hard text-based questions that require reasoning across multiple real-world documents, with each document set averaging ~100k input tokens. Questions are designed such that answers cannot be directly retrieved from documents and must instead be reasoned from multiple information sources.
 
-## New in Version 1.1 (September 2026)
-
-- **Sixteen corrected answer keys.** Each one was re-verified against the question's own source documents. Most were values stored as a decimal fraction where the question asked for a percentage (question 28, `0.658`, is now `65.8%`), or a date stored as a spreadsheet serial number (question 40, `45444`, is now `June 2024`). Four were corrections to the answer itself rather than its notation: questions 10, 25, 30 and 67.
-- **A judge system prompt.** The equality checker previously ran with no system prompt, which left it to improvise on unit conversion, notation and multi-value answers one call at a time. See Scoring Approach below.
-
-The questions and the document sets are unchanged. Scores measured on version 1.0.0 are not directly comparable with scores measured on version 1.1.
-
-Version 1.0.0 remains available at revision [`bdae010`](https://huggingface.co/datasets/ArtificialAnalysis/AA-LCR/tree/bdae010bbce259820c0e34c1d7cce210d966fb75), so earlier results stay reproducible:
-
-```python
-csv_path = hf_hub_download(
-    repo_id="ArtificialAnalysis/AA-LCR",
-    filename="AA-LCR_Dataset.csv",
-    repo_type="dataset",
-    revision="bdae010bbce259820c0e34c1d7cce210d966fb75",
-)
-```
-
 ## Dataset Development
 
-AA-LCR was created through a rigorous multi-phase process involving several members of the Artificial Analysis research team and more than a dozen undergraduate students who were engaged on a short-term contract basis to write and/or validate questions.
+AA-LCR was created through a rigorous multi-phase process involving several members of the Artificial Analysis research team and more than a dozen undergraduate students who were engaged on a short-term contract basis to write and/or validate questions. 
 
 **Document Curation**: We selected diverse document sets (company reports, government consultations, legal documents, academic papers) averaging ~100,000 tokens each, representing real materials knowledge workers analyze.
 
@@ -49,9 +26,9 @@ This approach validates that AA-LCR tests genuine reasoning capabilities rather
 
 ## Technical Details
 
-AA-LCR comprises 100 questions across 7 types of text-only documents (i.e. Company Reports, Industry Reports, Government Consultations, Academia, Legal, Marketing Materials and Survey Reports). Multiple independent documents, forming a Document Set with a total length of ~100k tokens are passed as context for each question. For instance, the Company Documents topic includes separate document sets containing 2023 and 2024 company reports, respectively.
+AA-LCR comprises 100 questions across 7 types of text-only documents (i.e. Company Reports, Industry Reports, Government Consultations, Academia, Legal, Marketing Materials and Survey Reports). Multiple independent documents, forming a Document Set with a total length of ~100k tokens are passed as context for each question. For instance, the Company Documents topic includes separate document sets containing 2023 and 2024 company reports, respectively. 
 
-Each question requires using the Document Set and applying general and mathematical reasoning.
+Each question requires using the Document Set and applying general and mathematical reasoning. 
 
 <div class="overflow-x-auto my-6">
   <table class="min-w-full border border-gray-300 bg-white">
@@ -136,11 +113,11 @@ Each question requires using the Document Set and applying general and mathemati
 
 **Sample Question:**
 
-```json
+\`\`\`json
 For the company and quarter where the company reported a 13.5% decline on the prior quarters operating income. What was their adjusted EBITDA? List the company name and adjusted EBITDA
 
 Answer: Equinix, $901 million
-```
+\`\`\`
 
 Examples of other types of questions include:
 
@@ -174,129 +151,23 @@ END QUESTION
 
 Reported token counts per question are based on the completed prompt, using the `cl100k_base` tokenizer from `tiktoken`.
 
-The order in which documents are loaded in matters - they should be added to the prompt template in the order of the filenames in `data_source_filenames`. Below are code snippets showing how we read the questions and extracted text files from disk.
-
-```
-def load_questions(self) -> list[dict]:
-    """Load LCR questions from HuggingFace dataset"""
-    csv_path = hf_hub_download(
-        repo_id="ArtificialAnalysis/AA-LCR",
-        filename="AA-LCR_Dataset.csv",
-        repo_type="dataset",
-    )
-
-    questions = []
-    with open(csv_path, encoding="utf-8") as f:
-        reader = csv.DictReader(f)
-        for row in reader:
-            # Parse data_source_filenames as ordered list
-            if "data_source_filenames" in row and isinstance(row["data_source_filenames"], str):
-                row["data_source_filenames"] = row["data_source_filenames"].split(";")
-
-            # Parse answer as list (semicolon-separated criteria)
-            if "answer" in row and isinstance(row["answer"], str):
-                row["answer"] = row["answer"].split(";")
-            questions.append(row)
-
-    return questions
-
-def get_document_set(
-    self, dataset_folder: str, document_category: str, document_set_id: str, data_source_filenames: list[str]
-) -> list[str]:
-    """Get document set for a question in the order specified by data_source_filenames"""
-
-    # Documents are extracted to lcr/lcr/{category}/{set_id}/ from the HuggingFace zip
-    document_set_path = os.path.join(dataset_folder, document_category, document_set_id)
-
-    document_texts = []
-    for filename in data_source_filenames:
-        document_path = os.path.join(document_set_path, filename)
-        with open(document_path, encoding="utf-8") as f:
-            document_texts.append(f.read())
-    return document_texts
-```
-
 ## Scoring Approach
 
-We use an LLM-based equality checker to evaluate responses. The checker receives a system prompt and a user prompt.
-
-System prompt:
-
-```
-Decide whether the CANDIDATE ANSWER is correct or incorrect against the OFFICIAL ANSWER.
-Note the following points when assessing correctness:
-
-- Numbers should still match when they are the same value written differently, e.g., a
-  percentage, a count of percentage points, and the equivalent decimal fraction are the same
-  value: 0.675, "67.5%" and "67.5 percentage points" all match. So do different scales
-  (thousand, million, bn) and different notations (thousands separators, currency symbols,
-  LaTeX markup, and numbers written as words).
-- Where the question asks for a particular format (e.g., a percentage, a number of decimal
-  places, a unit, a rounding, or an ordering) the CANDIDATE ANSWER must meet it. If the
-  question asks for no particular format, accept any equivalent form.
-- In cases where the question asks for an ordered list, a title, honorific or article added
-  to an entry in the CANDIDATE ANSWER can change where that entry sorts. Accept the ordering
-  if it is correct either with those additions or without them.
-- Grade the value the CANDIDATE ANSWER finally commits to, and it must commit to one. Values
-  reached while working, and alternatives it considers and sets aside, do not count. If it
-  offers several values without selecting one, it is incorrect even if one of them is right.
-  Hedging is fine as long as one clearly definitive answer is given.
-```
-
-User prompt:
-
-```
-Assess whether the following CANDIDATE ANSWER is CORRECT or INCORRECT.
-For the CANDIDATE ANSWER to be correct, it must be consistent with the OFFICIAL ANSWER.
-
-The question, for reference only: START QUESTION {question}
-
-END QUESTION
-
-The OFFICIAL ANSWER: {official_answer}
-
-END OFFICIAL ANSWER
-
-BEGIN CANDIDATE ANSWER TO ASSESS
-
-{candidate_answer}
-
-END CANDIDATE ANSWER TO ASSESS
-
-Reply as JSON, with a verdict of CORRECT or INCORRECT.
-```
+We use an LLM-based equality checker to evaluate responses:
 
-GPT-5.6 Luna (medium) is used as the equality checker model.
-
-The prompts above are those used from version 1.1 onwards.
-
-<details>
-<summary>Version 1.0.0 scoring prompt</summary>
-
-Version 1.0.0 runs without a system prompt, and with the following user prompt:
-
-```
+\`\`\`
 Assess whether the following CANDIDATE ANSWER is CORRECT or INCORRECT.
 For the CANDIDATE ANSWER to be correct, it must be consistent with the OFFICIAL ANSWER.
 
-The question, for reference only: START QUESTION {question}
-
-END QUESTION
-
+The question, for reference only: {question}
 The OFFICIAL ANSWER: {official_answer}
-
-END OFFICIAL ANSWER
-
-BEGIN CANDIDATE ANSWER TO ASSESS
-
-{candidate_answer}
-
-END CANDIDATE ANSWER TO ASSESS
+CANDIDATE ANSWER TO ASSESS: {candidate_answer}
 
 Reply only with CORRECT or INCORRECT.
-```
 
-</details>
+\`\`\`
+
+Qwen3 235B A22B 2507 Non-reasoning is used as the equality checker model.
 
 ## Access and Citation
 
@@ -304,17 +175,11 @@ The AA-LCR dataset is available at [https://huggingface.co/datasets/ArtificialAn
 
 If you use AA-LCR in your research, please cite:
 
-```json
+\`\`\`json
 @dataset{artificialanalysis2025lcr,
   title={Artificial Analysis Long Context Reasoning Benchmark(LCR)},
   author={Artificial Analysis Team},
   year={2025},
   publisher={Artificial Analysis, Inc.}
 }
-```
-
-## License
-
-**Question set**: Licensed under the Apache License 2.0
-
-**Document set**: Provided as a text representation of documents publicly available at time of dataset creation. We do not claim copyright or place any license over this data.
+\`\`\`