Update README.md

README.md

@@ -282,6 +282,7 @@ configs:
   - split: test
     path: data/test-*
 ---
+
 # Can Language Models Replace Programmers? REPOCOD Says 'Not Yet'
 
 Large language models (LLMs) have achieved high accuracy, i.e., more than 90 pass@1, in solving Python coding problems in HumanEval and MBPP. Thus, a natural question is, whether LLMs achieve comparable code completion performance compared to human developers? Unfortunately, one cannot answer this question using existing manual crafted or simple (e.g., single-line) code generation benchmarks, since such tasks fail to represent real-world software development tasks. In addition, existing benchmarks often use poor code correctness metrics, providing misleading conclusions.
@@ -298,9 +299,8 @@ REPOCOD_Lite_Unified is a variation of REPOCOD-Lite that has a similar format as
 
 * Check our [Leaderboard](https://lt-asset.github.io/REPOCOD/) for preliminary results using SOTA LLMs with RAG.
 
-* 
-```
 
+```
 "instance_id": Instance ID in REPOCOD
 "version": Version of REPOCOD
 "gold_patches": {
@@ -322,13 +322,11 @@ REPOCOD_Lite_Unified is a variation of REPOCOD-Lite that has a similar format as
     "code": Problem statement for code generation.
     "test": Problem statement for test generation.
 }
-# "problem_statement_source": "repocod",
 "environment_setup_commit": base commit
 "evaluation": {
     "FAIL_TO_PASS": list of relevant test cases
     "PASS_TO_PASS": None, (all remaining tests that passes, we choose not to run the PASS_TO_PASS tests to avoid the computational cost)
 }
-
 ```
 
 ## Citation