README.md

metadata

language:
  - en
task_categories:
  - text-retrieval
size_categories:
  - 1K<n<10K
dataset_info:
  - config_name: documents
    features:
      - name: chunk_id
        dtype: string
      - name: chunk
        dtype: string
      - name: source_url
        dtype: string
      - name: title
        dtype: string
      - name: chunk_idx
        dtype: int64
      - name: chunk_start_char
        dtype: int64
      - name: chunk_end_char
        dtype: int64
    splits:
      - name: test
        num_bytes: 1761506
        num_examples: 4936
    download_size: 600220
    dataset_size: 1761506
  - config_name: queries
    features:
      - name: original_query
        dtype: string
      - name: query
        dtype: string
      - name: answer
        list: string
      - name: score
        list: int64
      - name: source_url
        list: string
      - name: frag_start_char
        list: int64
      - name: frag_end_char
        list: int64
      - name: n_gold
        dtype: int64
      - name: roles
        list: string
      - name: fact
        dtype: string
    splits:
      - name: test
        num_bytes: 26959
        num_examples: 38
    download_size: 14279
    dataset_size: 26959
configs:
  - config_name: documents
    data_files:
      - split: test
        path: documents/test-*
  - config_name: queries
    data_files:
      - split: test
        path: queries/test-*

EDGAR 8-K — Context Chunk Retrieval (coreference + near-duplicate)

A contextualized sentence-level (query2chunk) retrieval eval built from SEC 8-K restructuring filings (EDGAR), for the Chunk-level Retrieval Eval collection.

Why this benchmark exists. Standard passage benchmarks (e.g. DAPR) don't discriminate contextual embedding models, because their answer passages already contain the distinguishing entity and have no near-duplicate distractors. This dataset is built to stress document-context disambiguation:

• Sentence chunks are coreference-dependent — the answer sentence says "the Company", never the company name, so resolving it requires the surrounding document.
• Near-identical distractors across companies — 8-Ks are templated, so the corpus holds dozens of near-identical sentences like "the Company estimates that it will incur charges of approximately $X million" differing only by amount/company. In isolation these are indistinguishable; only the document context resolves which "Company" is meant.

Example — query "What restructuring charges does Elastic expect to incur?" → answer "The Company expects to incur total non-recurring cash charges of approximately $22 million to $25 million…" competes against 58 near-identical charge sentences from other filings.

Stats

	count
documents (filings)	246
corpus (sentence chunks)	4,936
queries	38 (28 with a supporting-evidence fragment)
near-duplicate charge sentences	58
near-duplicate workforce sentences	53

Graded gold: answer vs supporting evidence

Gold is graded via the standard score field (no schema change vs the rest of the collection):

score	role	meaning
2	answer	the sentence that directly answers the query (uses coreference)
1	evidence	a supporting fact that disambiguates the answer (e.g. the sentence binding the company name to "the Company")

Derive the two metrics from one qrels:

standard_recall@k = |topk ∩ {score >= 2}| / |{score >= 2}|   # answer only
evidence_recall@k = |topk ∩ {score >= 1}| / |{score >= 1}|   # answer + supporting evidence

nDCG@k can consume the graded scores directly. A human-readable roles field (answer / evidence) mirrors the score. Backward compatible: datasets that use score = 1 uniformly are answer-only (no evidence tier).

Configs

documents

chunk_id, chunk (one sentence), source_url (filing accession — group chunks by this to give a contextual model its document view), title (company + 8-K date), chunk_idx (sentence position in the filing), chunk_start_char / chunk_end_char.

queries

original_query / query, answer[] (gold sentence texts), score[] (2=answer, 1=evidence), source_url[] (filing per gold), frag_start_char[] / frag_end_char[], n_gold, roles[] (answer/evidence), fact (workforce / charges).

Gold matching: a chunk is gold for a query if it shares source_url and its [chunk_start_char, chunk_end_char) overlaps a gold fragment's span (each gold maps to exactly one sentence chunk).

Construction & limitations

Built from EDGAR full-text search for restructuring 8-Ks (2022–2026), one primary filing per company. HTML cleaned (inline-XBRL/hidden/exhibit boilerplate stripped, sliced to the Item narrative), sentence-split with NLTK Punkt, junk/heading/fragment chunks removed.

• Prototype scale — 38 queries; the query set skews toward charges (workforce answers more often repeat the company name and are filtered out to preserve the coreference axis).
• Templated queries — 2 regex-generated templates (workforce, charges); phrasing overlaps the gold, so the difficulty is the coreference/near-duplicate disambiguation, not the wording. A natural/LLM-generated query pass is future work.
• Evidence coverage — 28/38 queries have a supporting-evidence fragment; the rest are answer-only.

Source: public SEC EDGAR filings (U.S. government works / public domain).