Update README.md

README.md

@@ -20,7 +20,6 @@ Each example is a JSON object with the following fields:
   "src": 30,
   "dst": 33,
   "groundtruth": true,
-  "groundtruth_trace": [[30, 31, 33]],
   "task_id": "control_codenet_p00496_s700056700_main_12_40_k_33_1",
   "prompt": "..."
   "category": trace/all_source
@@ -49,7 +48,6 @@ The `category` field specifies the type of prompt associated with each task inst
 | `language`        | Programming language of the source file (`C`, `Java`, `Python`). |
 | `src`, `dst`      | Defines the two program elements queried in this task. In control dependency, these are line numbers. In data dependency and information flow, they are structured as `["varname", line_no]`, representing variable instances. |
 | `groundtruth`     | Boolean indicating whether the specified dependency relationship holds (i.e., true if `src` has the given dependency on `dst`). |
-| `groundtruth_trace` | The ground truth trace (as a list of line numbers) that represents a valid transitive chain of direct dependencies. This is only used for **control dependency** tasks. For **data dependency** and **information flow**, this is set to `null` since there can be theoretically infinite number of valid traces. Evaluation is instead conducted by validating the predicted trace against the code’s static dependency structure.|
 | `task_id`         | A unique ID for the task instance. The prefix (`control_`, `data_`, `infoflow_`) identifies the task type. |
 | `prompt`          | The prompt string used in the experiment for this task instance. It includes the instruction, examples, query, and code context provided to the LLM. Content-specific fields (e.g., source/target names, line numbers) are filled into a standardized prompt template. |
 
@@ -73,7 +71,7 @@ For scripts, evaluation tools, and detailed instructions on running inference ov
 
 🔗 Paper: [https://arxiv.org/abs/2507.05269](https://arxiv.org/abs/2507.05269)
 
-This includes:
+The github repo includes:
 
 - Raw annotation data that could be used to generate various static analysis tasks
 - Predefined prompts for each task and language