fix typos and supplement README

README.md

@@ -24,6 +24,9 @@ The generate task is to answer the question based on the contexts provided by re
 * [Answer chrF Score](./rageval/metrics/_answer_chrf.py): also known as *character n-gram F-score* as used in [the paper (Popovic et al.)](https://aclanthology.org/W15-3049).
 * [Answer Disambig-F1](./rageval/metrics/_answer_disambig_f1.py): also known as *Disambig-F1* as used in [the paper (Ivan Stelmakh et al.)](https://arxiv.org/abs/2204.06092) and [the paper (Zhengbao Jiang et al.)](https://arxiv.org/abs/2305.06983).
 * [Answer Rouge Correctness](./rageval/metrics/_answer_rouge_correctness.py): also known as *Rouge* as used in [the paper (Chin-Yew Lin)](https://aclanthology.org/W04-1013.pdf).
+* [Answer Accuracy](./rageval/metrics/_answer_accuracy.py): also known as *Accuracy* as used in [the paper (Dan Hendrycks et al.)](https://arxiv.org/abs/2009.03300).
+* [Answer LCS Ratio](./rageval/metrics/_answer_lcs_ratio.py): also know as *LCS(%)* as used in [the paper (Nashid et al.)](https://ieeexplore.ieee.org/abstract/document/10172590).
+* [Answer Edit Distance](./rageval/metrics/_answer_edit_distance.py): also know as *Edit distance* as used in [the paper (Nashid et al.)](https://ieeexplore.ieee.org/abstract/document/10172590).
 
 (2) **Answer Groundedness**: this category of metrics is to evaluate the groundedness (also known as factual consistency) by comparing the generated answer with the provided contexts. Here are some commonly used metrics:
 

tests/units/test_answer_accuracy.py

@@ -28,7 +28,7 @@ def testset(sample):
 
 
 @pytest.mark.slow
-def test_case_on_answer_lcs_ratio(testset):
+def test_case_on_answer_accuracy(testset):
     metric = AnswerAccuracy()
     assert metric.name == "answer_accuracy"
     assert metric.homepage == ""