README.md

---
license: mit
language:
- en
task_categories:
- text-retrieval
tags:
- information-retrieval
- reasoning
- benchmark
- bright
size_categories:
- 100K<n<1M
pretty_name: Bright-Pro
configs:
- config_name: examples
  data_files:
  - split: biology
    path: examples/biology.parquet
  - split: earth_science
    path: examples/earth_science.parquet
  - split: economics
    path: examples/economics.parquet
  - split: psychology
    path: examples/psychology.parquet
  - split: robotics
    path: examples/robotics.parquet
  - split: stackoverflow
    path: examples/stackoverflow.parquet
  - split: sustainable_living
    path: examples/sustainable_living.parquet
- config_name: aspects
  data_files:
  - split: biology
    path: aspects/biology.parquet
  - split: earth_science
    path: aspects/earth_science.parquet
  - split: economics
    path: aspects/economics.parquet
  - split: psychology
    path: aspects/psychology.parquet
  - split: robotics
    path: aspects/robotics.parquet
  - split: stackoverflow
    path: aspects/stackoverflow.parquet
  - split: sustainable_living
    path: aspects/sustainable_living.parquet
- config_name: documents
  data_files:
  - split: biology
    path: documents/biology.parquet
  - split: earth_science
    path: documents/earth_science.parquet
  - split: economics
    path: documents/economics.parquet
  - split: psychology
    path: documents/psychology.parquet
  - split: robotics
    path: documents/robotics.parquet
  - split: stackoverflow
    path: documents/stackoverflow.parquet
  - split: sustainable_living
    path: documents/sustainable_living.parquet
---

# Bright-Pro

**Bright-Pro** is an expert-annotated extension of the [BRIGHT](https://huggingface.co/datasets/xlangai/BRIGHT) benchmark for *reasoning-intensive retrieval*. Each query is paired with a multi-aspect reasoning decomposition, weighted importance scores per aspect, and a curated set of gold passages organized by aspect — enabling fine-grained analysis of whether a retriever covers the **complementary reasoning aspects** required to answer a query, rather than just surfacing one relevant passage.

Bright-Pro builds on the seven StackExchange subsets of BRIGHT. Annotators (1) decomposed each query's information need into reasoning aspects, (2) assigned an importance weight (Likert 1–3) to each aspect, (3) re-audited and consolidated BRIGHT's original positives, (4) collected new aspect-grounded positives, and (5) had a second annotator from the same field re-examine the result.

## Subsets and statistics

Bright-Pro covers **739 queries** across **7 StackExchange domains** with **2,763 reasoning aspects** and **5,272 gold passages** drawn from a unified corpus of **526,319 documents**.

| Task | Queries | Aspects | Gold docs | Corpus docs |
|---|---:|---:|---:|---:|
| biology | 103 | 406 | 804 | 59,513 |
| earth_science | 115 | 440 | 856 | 123,575 |
| economics | 99 | 367 | 773 | 52,240 |
| psychology | 100 | 384 | 707 | 54,741 |
| robotics | 101 | 375 | 623 | 63,920 |
| stackoverflow | 115 | 382 | 529 | 109,188 |
| sustainable_living | 106 | 409 | 980 | 63,142 |
| **Total** | **739** | **2,763** | **5,272** | **526,319** |

## Configurations

Bright-Pro has three configurations. Each configuration has 7 splits, one per StackExchange domain.

### `examples` — query-level annotations

| Field | Type | Description |
|---|---|---|
| `id` | int | Per-task query id |
| `query` | string | Natural-language query (verbatim from BRIGHT StackExchange) |
| `gold_ids` | list[string] | All positive document ids for this query (from any aspect) |
| `reference_answer` | string | Reference long-form answer synthesized from the aspects, with `[doc_N]` citations to gold docs |

### `aspects` — reasoning-aspect annotations

| Field | Type | Description |
|---|---|---|
| `id` | string | Aspect id, of the form `{task}-{qid}-a{k}` |
| `content` | string | Natural-language description of the reasoning aspect |
| `weight` | int | **Raw Likert weight ∈ {1, 2, 3}** (1 = minor, 2 = important, 3 = critical). Normalize per query via `weight / Σ_a weight` to get probabilities summing to 1 |
| `supporting_docs` | list[string] | Document ids that support this aspect (the inverse of a doc → aspect map) |

### `documents` — corpus

| Field | Type | Description |
|---|---|---|
| `id` | string | Document id, of the form `{task}-{qid}/extraction_{k}.txt` |
| `content` | string | Document text (cleaned and segmented) |

## Quick start

```python
from datasets import load_dataset

# Per-task loading (any of the 7 SE domains)
examples = load_dataset("yale-nlp/Bright-Pro", "examples", split="biology")
aspects  = load_dataset("yale-nlp/Bright-Pro", "aspects",  split="biology")
docs     = load_dataset("yale-nlp/Bright-Pro", "documents", split="biology")

print(examples[0]["query"])
print(aspects[0]["content"], aspects[0]["weight"])
print(docs[0]["content"][:200])
```

To recover the per-query aspect weights as probabilities (Σ = 1):

```python
from collections import defaultdict

raw_sum = defaultdict(float)
for a in aspects:
    qstem = a["id"].rsplit("-a", 1)[0]      # e.g. "biology-0"
    raw_sum[qstem] += a["weight"]

aspect_weight = {a["id"]: a["weight"] / raw_sum[a["id"].rsplit("-a", 1)[0]] for a in aspects}
```

To build the inverse `doc_id → aspect_id` map for a task:

```python
doc_to_aspect = {}
for a in aspects:
    for d in a["supporting_docs"]:
        doc_to_aspect[d] = a["id"]
```

## Evaluation

Bright-Pro supports two complementary evaluation regimes:

1. **Static retrieval.** A retriever ranks the per-task corpus once. Primary metric: **α-nDCG@k** (with α = 0.5) over the aspect-weighted gold set, complemented by Aspect-Recall@k, NDCG@k, and Recall@k. The metric rewards covering complementary aspects rather than over-retrieving from a single one.

2. **Agentic retrieval.** A retriever is plugged into an LLM agent that iteratively issues search queries and synthesizes a final answer. The agent loop is evaluated under two protocols:
   - **Fixed-round** (R ∈ {1, 2, 3} rounds, top-5 per round) — measures retriever quality under matched interaction budgets via cumulative α-nDCG@5R, reasoning completeness, and overall answer quality.
   - **Adaptive-round** — the agent decides when to stop. Measures both answer quality and interaction efficiency via the **Efficiency-Quality Reward (AER)** = OQ × exp(−γ (R−1)), with γ = 0.05.

Reasoning completeness and overall quality are rated by an LLM-as-Judge against a reference answer constructed from the annotated aspects and their supporting passages.

## Differences from BRIGHT

Bright-Pro keeps BRIGHT's *queries* and *corpus URLs* but extends the gold-side annotation:

- **Aspects.** Each query is decomposed into 2–6 reasoning aspects with Likert importance weights (BRIGHT has no aspect annotation).
- **Aspect-grounded gold.** Original BRIGHT positives are re-audited (some discarded as topic-only), and new aspect-relevant passages are collected from the live web. Each gold passage is tied to exactly one aspect.
- **Reference answers.** Each query has a reference long-form answer with citations to gold docs, used to drive the LLM-as-Judge evaluation.
- **Scope.** Bright-Pro covers only the 7 StackExchange domains of BRIGHT — the Coding (LeetCode, Pony) and Theorem (AOPS, TheoremQA) subsets are excluded because they rely on syntactic or formal-logic matching rather than open-domain natural-language reasoning.

## License

Released under the [MIT License](LICENSE). The underlying StackExchange queries and the BRIGHT corpus retain their original licenses; consult the [BRIGHT dataset card](https://huggingface.co/datasets/xlangai/BRIGHT) for upstream attribution.