diff --git a/retrieval_lm/run_short_form.py b/retrieval_lm/run_short_form.py
index 34ba0ca..49f9777 100644
--- a/retrieval_lm/run_short_form.py
+++ b/retrieval_lm/run_short_form.py
@@ -50,9 +50,8 @@ def postprocess_answer_option_conditioned(answer):
 
 def call_model_rerank_w_scores_batch(prompt, evidences, model, max_new_tokens=15,
                                      ret_tokens=None, rel_tokens=None, grd_tokens=None, ut_tokens=None,
-                                     use_seqscore=False, threshold=0.5, beam_width=2, max_depth=1,
+                                     use_seqscore=False, threshold=0.5, 
                                      w_rel=1.0, w_sup=1.0, w_use=0.5, mode="adaptive_retrieval", closed=False):
-    # max_inpt_tokens = tokenizer.model_max_length if model_max_length is None else model_max_length
     results = {}
     if mode != "always_retrieve":
         sampling_params = SamplingParams(
@@ -285,8 +284,6 @@ def main():
                         help="reward weight for generation support (attribution)")
     parser.add_argument("--w_use",  type=float, default=1.0,
                         help="reward weight for overall completeness / utility.")
-    parser.add_argument("--ignore_cont", action="store_true",
-                        help="filter out sentences that include [No support / Contradictory] ")
     parser.add_argument('--mode', type=str, help="mode to control retrieval.",
                         default="default", choices=['adaptive_retrieval', 'no_retrieval', 'always_retrieve'],)
     parser.add_argument('--metric', type=str, help="metric to be used during evaluation")
@@ -316,7 +313,7 @@ def generate(prompt, evidences, max_new_tokens):
         return call_model_rerank_w_scores_batch(prompt, evidences=evidences, model=model, max_new_tokens=max_new_tokens,
                                                 rel_tokens=rel_tokens, ret_tokens=ret_tokens, grd_tokens=grd_tokens, ut_tokens=ut_tokens,
                                                 threshold=args.threshold, beam_width=args.beam_width, max_depth=args.max_depth, use_seqscore=args.use_seqscore,
-                                                w_rel=1.0, w_sup=1.0, w_use=0.5, mode=args.mode, closed=args.task in ["fever", "arc_c"])
+                                                w_rel=args.w_rel, w_sup=args.w_sup, w_use=args.w_use, mode=args.mode, closed=args.task in ["fever", "arc_c"])
 
     preds = []
     prompts = []
@@ -338,7 +335,6 @@ def generate(prompt, evidences, max_new_tokens):
         all_results.append(results)
         if do_retrieve is True:
             count += 1
-        # golds.append(row["output"])
         if "answers" not in row and "answer" in row:
             row["answers"] = [row["answer"]] if type(
                 row["answer"]) is str else row["answer"]