Update README.md

README.md

@@ -63,6 +63,24 @@ Each instance consists of a question, a pair of contradictory passages extracted
 
 The dataset has been used in the paper to assess LLMs performance when augmented with retrieved passages containing real-world knowledge conflicts.
 
+The following figure illustrates the evaluation process:
+
+<p align="center">
+  <img src="./figs/Evaluation.png" width=70%/>
+</p>
+
+And the following table shows the performance of five LLMs (Mistral-7b-inst, Mixtral-8x7b-inst, Llama-2-70b-chat, Llama-3-70b-inst, and GPT-4) on the Wikipedia Contradict Benchmark based on rigorous human evaluations on a subset of answers for 55 instances, which corresponds to 1,375 LLM responses in total.
+
+<p align="center">
+  <img src="./figs/table2.png" width=70%/>
+</p>
+
+Notes: “C”, “PC” and “IC” stand for “Correct”, “Partially correct”, “Incorrect”, respectively. “all”, “exp”, and “imp” represent for instance
+types: all instances, instances with explicit conflicts, and instances with implicit conflicts. The
+numbers represent the ratio of responses from each LLM that were assessed as “Correct, “Partially
+correct, or “Incorrect for each instance type under a prompt template. The bold numbers highlight
+the best models that correctly answer questions for each type and prompt template.
+
 ### Out-of-Scope Use
 
 <!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
@@ -203,7 +221,7 @@ For further information, see the annotation guidelines of the paper.
 
 <!-- This section describes the people or systems who created the annotations. -->
 
-Yufang Hou, Alessandra Pascale, Javier Carnerero-Cano, Tigran Tchrakian, Radu Marinescu, Elizabeth Daly, Inkit Padhi, and Prasanna Sattigeri.
+Yufang Hou, Alessandra Pascale, Javier Carnerero-Cano, Tigran Tchrakian, Radu Marinescu, Elizabeth Daly, Inkit Padhi
 
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