Update README release and evaluation protocol

README.md

@@ -14,23 +14,22 @@ tags:
 pretty_name: TransitLM
 ---
 
-# TransitLM
+# TransitLM: Dataset Release & Evaluation Protocol
 
 ## Dataset Description
 
-TransitLM is a dataset for public transit route planning in Chinese urban environments. It is designed to support training and evaluation of language models that generate structured transit routes from origin-destination information.
+TransitLM is a dataset for public transit route planning in Chinese urban environments, designed to support training and evaluation of language models that generate structured transit routes from origin-destination information. The full dataset covers four cities: Beijing, Shanghai, Shenzhen, and Chengdu, and includes coordinates, station sequences, transfer structure, line information, and route annotations.
 
-The full dataset covers transit planning data from four Chinese cities: Beijing, Shanghai, Shenzhen, and Chengdu.
+### Released Files
 
-It includes route-related information such as coordinates, station sequences, transfer structure, line information, and route annotations.
-
-The current repository contains multiple CSV exports, including benchmark-specific train/test splits (`transit-lm-benchmark*-train.csv`, `transit-lm-benchmark*-test.csv`), general benchmark test splits (`transit-lm-general-benchmark*-test.csv`), and a static station information file (`transit-lm-station-info.csv`).
-
-The `transit-lm-benchmark*-train.csv` files are the benchmark-aligned SFT training splits. The `transit-lm-benchmark*-test.csv` files are the corresponding benchmark test splits used for task evaluation. The `transit-lm-general-benchmark*-test.csv` files are the real-world benchmark test sets intended for evaluating general-purpose LLMs on natural station names and coordinates. The `transit-lm-station-info.csv` file provides supporting station-level reference information for the transit network.
-
-## Dataset Release & Evaluation Protocol
+| File Pattern | Content | Section |
+|---|---|---|
+| `transit-lm-benchmark*-train.csv` | Anonymized SFT training splits (benchmark-aligned) | §2 |
+| `transit-lm-benchmark*-test.csv` | Anonymized SFT test splits for task evaluation | §2 |
+| `transit-lm-general-benchmark*-test.csv` | Real-world benchmark test sets (real station names + coordinates) | §1 |
+| `transit-lm-station-info.csv` | Static station-level reference information | — |
 
-All released data follows the anonymization principles described in our paper.
+All released data follows the anonymization principles described in our paper. The sections below explain the data treatment and evaluation protocol for each component.
 
 **Overview.** We release two parallel test sets that correspond one-to-one, serving different model categories:
 
@@ -39,6 +38,8 @@ All released data follows the anonymization principles described in our paper.
 
 This parallel design ensures fair evaluation for both model types while keeping results directly comparable.
 
+---
+
 ## 1. Benchmark Test Samples
 
 30,000 test examples across three tasks: optimal route generation, preference-aware planning, and multi-route generation.
@@ -52,6 +53,8 @@ This parallel design ensures fair evaluation for both model types while keeping
 
 **Privacy.** These samples contain no user identifiers, no timestamps, and no cross-record linkage. Each is an isolated OD request. All station coordinates and names are already publicly accessible through mapping APIs, so releasing them in benchmark format introduces no additional privacy risk.
 
+---
+
 ## 2. CPT + SFT Corpus (Anonymized)
 
 13.9M CPT records, 90K SFT training examples, and 30K SFT test examples.
@@ -60,7 +63,7 @@ This parallel design ensures fair evaluation for both model types while keeping
 
 | Transformation | Method | Training Impact |
 |---|---|---|
-| Coordinate perturbation | Deterministic hash-based offset, +/-100m on a ~500m grid | Negligible. Grid-coherent offset preserves spatial structure. Error < 0.1% of mean route length |
+| Coordinate perturbation | Deterministic hash-based offset, ±100m on a ~500m grid | Negligible. Grid-coherent offset preserves spatial structure. Error < 0.1% of mean route length |
 | Station name removal | Names removed from station sequences, retaining only numeric IDs | None. Topological learning relies on IDs and connectivity |
 
 **Rationale.** Bulk harvesting coordinate-ID mappings from a 13.9M-record corpus could enable unauthorized commercial use. OD coordinates reflect real user trip intentions. Perturbation combined with structural safeguards prevents re-identification.
@@ -73,6 +76,8 @@ The 30K SFT test samples undergo identical anonymization. See the Overview above
 
 Despite anonymization, this corpus fully supports continual pre-training for transit topology and spatial reasoning, supervised fine-tuning for all three benchmark tasks, and reproducibility of all paper results.
 
+---
+
 ## 3. Privacy Architecture
 
 Unlike GPS trajectory datasets such as T-Drive or GeoLife that record continuous multi-day traces enabling re-identification, each record here is an isolated OD planning request with no temporal continuity.
@@ -84,17 +89,15 @@ Structural safeguards enforced at the data construction stage:
 
 These properties make it infeasible to associate records with the same individual or reconstruct mobility patterns, regardless of coordinate perturbation.
 
+---
+
 ## 4. Why Both Perturbed and Real Data?
 
 | Concern | Anonymized corpus | Benchmark samples |
 |---|---|---|
-| Coordinate privacy | Yes. +/-100m perturbation prevents bulk harvesting | Public data, no new risk |
-| Fair evaluation | No. Perturbed coordinates disadvantage general LLMs | Yes. Real coordinates enable direct comparison |
-| Reproducibility | Yes. Full training pipeline reproducible | Yes. Standardized community test set |
-| Training utility | Yes. < 0.1% spatial error, topology preserved | Not used for training |
+| Coordinate privacy | ✅ ±100m perturbation prevents bulk harvesting | Public data, no new risk |
+| Fair evaluation | ❌ Perturbed coordinates disadvantage general LLMs | ✅ Real coordinates enable direct comparison |
+| Reproducibility | ✅ Full training pipeline reproducible | ✅ Standardized community test set |
+| Training utility | ✅ < 0.1% spatial error, topology preserved | Not used for training |
 
 This dual-release design maximizes research utility through reproducible training and fair evaluation, while minimizing privacy exposure by applying perturbation only where data scale creates risk.
-
-## Dataset Status
-
-This repository is currently being uploaded incrementally. The files currently visible on Hugging Face may represent only part of the full dataset.