Update README.md

README.md

@@ -28,6 +28,20 @@ SECQUE comprises 565 expert-written questions covering SEC filings analysis acro
 To assess model performance, we develop SECQUE-Judge, an evaluation mechanism leveraging multiple LLM-based judges, which demonstrates strong alignment with human evaluations. 
 Additionally, we provide an extensive analysis of various models’ performance on our benchmark. 
 
+
+## Results
+
+| Model                        | Baseline          | Financial        | Baseline CoT       | Financial CoT      | Flipped           | Avg Tokens by Model |
+|------------------------------|-------------------|------------------|--------------------|--------------------|-------------------|---------------------|
+| GPT-4o                       | **_0.69_**/0.79   | 0.62/0.71        | 0.67/0.76          | 0.63/0.73          | 0.68/0.78         | 319.84              |
+| GPT-4o-mini                  | _0.64_/0.73       | 0.38/0.47        | 0.60/0.72          | 0.56/0.65          | 0.62/0.73         | 289.76              |
+| Llama-3.3-70B-Instruct       | _0.65_/0.75       | 0.60/0.71        | 0.63/0.74          | 0.60/0.72          | 0.62/0.74         | 341.63              |
+| Qwen2.5-32B-Instruct         | 0.61/0.72         | 0.49/0.58        | 0.60/0.71          | 0.55/0.67          | _0.65_/0.75       | 331.34              |
+| Phi-4                        | 0.56/0.66         | 0.55/0.64        | _0.57_/0.67        | 0.56/0.66          | _0.57_/0.67       | 294.33              |
+| Meta-Llama-3.1-8B-Instruct   | _0.48_/0.60       | 0.41/0.54        | 0.44/0.56          | 0.40/0.53          | 0.47/0.59         | 338.38              |
+| Mistral-Nemo-Instruct-2407   | _0.46_/0.55       | 0.32/0.42        | 0.45/0.56          | 0.44/0.55          | 0.44/0.54         | 231.52              |
+| Avg Tokens by Prompt         | 283.04            | 151.97           | 437.38             | 334.71             | 317.57            | 304.93              |
+
 ## Citation
 
 ```bash