Add image

README.md

@@ -29,6 +29,8 @@ Real-FIM-Eval is built using data from GitHub commits between Jan. 2025 and Feb.
 
 We process git commits using `diff_match_patch` ([https://github.com/google/diff-match-patch](https://github.com/google/diff-match-patch)) to identify line-level changes. The commits are then categorized into two splits: `Add` and `Edit`.
 
+![Construction of Fill-in-the-Middle (FIM) examples for the proposed Real-FIM-Eval benchmark splits, derived from real-world git commits.](https://huggingface.co/datasets/gonglinyuan/real_fim_eval/resolve/main/example.png)
+
 - `Add` (17,879 examples): This split uses git commits where a developer added a new segment of code into an existing file. To create the FIM prompt, we treat the added code segment as the "middle" part that the language model needs to predict. The code surrounding the addition forms the prefix (code before) and the suffix (code after).
 
 - `Edit` (13,922 examples): This split uses git commits where a developer modified existing code by removing a segment and replacing it with a new one. We present this task to LLMs in a *conflict-merge* format. The prompt includes the code context (prefix and suffix) and marks the original code segment (to be removed). The model is asked to infill the updated code segment.
@@ -41,7 +43,7 @@ $$
 \exp\left(-\frac{1}{\mathrm{n\_chars}(\mathrm{mid})} \sum_{i\in\mathrm{mid}} \log p_{i}\right)
 $$
 
-In this formula, $p_{i}$ represents the probability the model assigns to the $i$-th token of the ground truth (only the "middle" part that the model is tasked to infill), and $\mathrm{n\_chars}(\mathrm{mid})$ is the total number of characters in that ground truth middle segment.
+In this formula, `p_i` represents the probability the model assigns to the `i`-th token of the ground truth (only the "middle" part that the model is tasked to infill), and `n_char(mid)` is the total number of characters in that ground truth middle segment.
 
 ## Statistics of Programming Languages