diff --git a/examples/op_examples/filter_cascade.py b/examples/op_examples/filter_cascade.py
index 2ff61106..52b261d6 100644
--- a/examples/op_examples/filter_cascade.py
+++ b/examples/op_examples/filter_cascade.py
@@ -3,12 +3,14 @@
 import lotus
 from lotus.models import LM, LiteLLMRM
 from lotus.types import CascadeArgs, ProxyModel
+from lotus.vector_store import FaissVS
 
 gpt_4o_mini = LM("gpt-4o-mini")
 gpt_4o = LM("gpt-4o")
 rm = LiteLLMRM(model="text-embedding-3-small")
+vs = FaissVS()
 
-lotus.settings.configure(lm=gpt_4o, helper_lm=gpt_4o_mini, rm=rm)
+lotus.settings.configure(lm=gpt_4o, helper_lm=gpt_4o_mini, rm=rm, vs=vs)
 data = {
     "Course Name": [
         "Probability and Random Processes",
diff --git a/lotus/models/lm.py b/lotus/models/lm.py
index e8c4d087..9d26e5d7 100644
--- a/lotus/models/lm.py
+++ b/lotus/models/lm.py
@@ -194,7 +194,9 @@ def _update_stats(self, response: ModelResponse, is_cached: bool = False):
         except Exception as e:
             # Handle any other unexpected errors when calculating cost
             lotus.logger.debug(f"Unexpected error calculating completion cost: {e}")
-            raise Warning("Error calculating completion cost - cost metrics will be inaccurate. Enable debug logging for details.")
+            raise Warning(
+                "Error calculating completion cost - cost metrics will be inaccurate. Enable debug logging for details."
+            )
             cost = None
 
         # Always update virtual usage
@@ -234,27 +236,43 @@ def format_logprobs_for_filter_cascade(
     ) -> LogprobsForFilterCascade:
         # Get base cascade format first
         base_cascade = self.format_logprobs_for_cascade(logprobs)
-        all_true_probs = []
+        all_true_probs: list[float] = []
 
         def get_normalized_true_prob(token_probs: dict[str, float]) -> float | None:
-            if "True" in token_probs and "False" in token_probs:
-                true_prob = token_probs["True"]
-                false_prob = token_probs["False"]
+            # Normalize keys by converting to lowercase and stripping whitespace
+            # Take the max probability for each key (e.g. True and true both map to "true" so we take the max of the two)
+            normalized_probs: dict[str, float] = {}
+            for k, v in token_probs.items():
+                normalized_key = k.lower().strip()
+                normalized_probs[normalized_key] = max(v, normalized_probs.get(normalized_key, float("-inf")))
+
+            # Look for true/false in normalized keys
+            if "true" in normalized_probs and "false" in normalized_probs:
+                true_prob = normalized_probs["true"]
+                false_prob = normalized_probs["false"]
                 return true_prob / (true_prob + false_prob)
             return None
 
-        # Get true probabilities for filter cascade
-        for resp_idx, response_logprobs in enumerate(logprobs):
-            true_prob = None
-            for logprob in response_logprobs:
+        for response_logprobs in logprobs:
+            true_prob: float = 1  # Default if no true/false token found
+
+            # Find last true/false token by normalizing all tokens and searching in reverse
+            cleaned_tokens = [logprob.token.lower().strip() for logprob in response_logprobs]
+            true_false_indices = [i for i, token in enumerate(cleaned_tokens) if token in ["true", "false"]]
+
+            if true_false_indices:
+                last_true_false_idx = true_false_indices[-1]
+                logprob = response_logprobs[last_true_false_idx]
+                cleaned_token = cleaned_tokens[last_true_false_idx]
+
                 token_probs = {top.token: np.exp(top.logprob) for top in logprob.top_logprobs}
-                true_prob = get_normalized_true_prob(token_probs)
-                if true_prob is not None:
-                    break
+                normalized_prob = get_normalized_true_prob(token_probs)
 
-            # Default to 1 if "True" in tokens, 0 if not
-            if true_prob is None:
-                true_prob = 1 if "True" in base_cascade.tokens[resp_idx] else 0
+                # Fall back to binary true/false if normalization fails
+                if normalized_prob is None:
+                    true_prob = 1 if cleaned_token == "true" else 0
+                else:
+                    true_prob = normalized_prob
 
             all_true_probs.append(true_prob)
 
diff --git a/lotus/sem_ops/sem_filter.py b/lotus/sem_ops/sem_filter.py
index c0dd4be3..34fde2fb 100644
--- a/lotus/sem_ops/sem_filter.py
+++ b/lotus/sem_ops/sem_filter.py
@@ -8,7 +8,7 @@
 from lotus.cache import operator_cache
 from lotus.templates import task_instructions
 from lotus.types import CascadeArgs, LMOutput, LogprobsForFilterCascade, ProxyModel, SemanticFilterOutput
-from lotus.utils import show_safe_mode
+from lotus.utils import get_out_col_name, show_safe_mode
 
 from .cascade_utils import calibrate_llm_logprobs, importance_sampling, learn_cascade_thresholds
 from .postprocessors import filter_postprocess
@@ -162,6 +162,7 @@ def __call__(
         strategy: str | None = None,
         cascade_args: CascadeArgs | None = None,
         return_stats: bool = False,
+        return_scores: bool = False,
         safe_mode: bool = False,
         progress_bar_desc: str = "Filtering",
         additional_cot_instructions: str = "",
@@ -183,6 +184,7 @@ def __call__(
                 sampling_percentage (float): The percentage of the data to sample when cascading. Defaults to 0.1.
                 failure_probability (float): The failure probability when cascading. Defaults to 0.2.
             return_stats (bool): Whether to return statistics. Defaults to False.
+            return_scores (bool): Whether to return probabilities. Defaults to False.
             additional_cot_instructions (str): Additional instructions for the CoT. Defaults to "".
 
         Returns:
@@ -193,7 +195,7 @@ def __call__(
                 "The language model must be an instance of LM. Please configure a valid language model using lotus.settings.configure()"
             )
 
-        stats: dict[str, float] = {}
+        stats: dict[str, Any] = {}
         lotus.logger.debug(user_instruction)
         col_li = lotus.nl_expression.parse_cols(user_instruction)
         lotus.logger.debug(col_li)
@@ -330,9 +332,16 @@ def __call__(
             outputs: list[bool] = [False] * len(multimodal_data)
             raw_outputs: list[str] = [""] * len(multimodal_data)
             explanations: list[str | None] = [None] * len(multimodal_data)
+            scores: list[float] = [0.0] * len(multimodal_data)
+            score_methods: list[str] = [""] * len(multimodal_data)
 
             for idx in high_conf_idxs:
                 outputs[idx] = proxy_outputs[idx]
+                scores[idx] = proxy_scores[idx]
+                if proxy_model == ProxyModel.HELPER_LM:
+                    score_methods[idx] = "HELPER_LM_TOKEN_PROB"
+                else:
+                    score_methods[idx] = "RM_SIM_SCORE"
 
             # If using helper LM, get raw outputs and explanations
             if proxy_model == ProxyModel.HELPER_LM:
@@ -358,13 +367,22 @@ def __call__(
                     strategy=strategy,
                     safe_mode=safe_mode,
                     progress_bar_desc="Running predicate evals with oracle LM",
+                    logprobs=return_scores,
                     additional_cot_instructions=additional_cot_instructions,
                 )
 
+                if return_scores:
+                    assert large_output.logprobs is not None, "Logprobs must be returned if return_scores is True"
+                    formatted_logprobs = lotus.settings.lm.format_logprobs_for_filter_cascade(large_output.logprobs)
+                    large_probs = formatted_logprobs.true_probs
+
                 for idx, large_idx in enumerate(low_conf_idxs):
                     outputs[large_idx] = large_output.outputs[idx]
                     raw_outputs[large_idx] = large_output.raw_outputs[idx]
                     explanations[large_idx] = large_output.explanations[idx]
+                    if return_scores:
+                        scores[large_idx] = large_probs[idx]
+                        score_methods[large_idx] = "LM_TOKEN_PROB"
 
             stats["filters_resolved_by_helper_model"] += len(high_conf_idxs)
             stats["filters_resolved_by_large_model"] += len(low_conf_idxs)
@@ -382,12 +400,22 @@ def __call__(
                 safe_mode=safe_mode,
                 show_progress_bar=True,
                 progress_bar_desc=progress_bar_desc,
+                logprobs=return_scores,
                 additional_cot_instructions=additional_cot_instructions,
             )
             outputs = output.outputs
             raw_outputs = output.raw_outputs
             explanations = output.explanations
 
+            if return_scores:
+                assert output.logprobs is not None, "Logprobs must be returned if return_scores is True"
+                formatted_logprobs = lotus.settings.lm.format_logprobs_for_filter_cascade(output.logprobs)
+                scores = formatted_logprobs.true_probs
+                score_methods = ["LM_TOKEN_PROB"] * len(multimodal_data)
+            else:
+                scores = [0.0] * len(multimodal_data)
+                score_methods = [""] * len(multimodal_data)
+
         if not return_all:
             # find indices where output is True
             ids = [i for i, x in enumerate(outputs) if x]
@@ -398,34 +426,32 @@ def __call__(
             [outputs[i] for i in ids]
             filtered_explanations = [explanations[i] for i in ids]
             filtered_raw_outputs = [raw_outputs[i] for i in ids]
+            filtered_scores = [scores[i] for i in ids]
+            filtered_score_methods = [score_methods[i] for i in ids]
             lotus.logger.debug(f"filtered_raw_outputs: {filtered_raw_outputs}")
 
             new_df = self._obj.iloc[ids]
             new_df.attrs["index_dirs"] = self._obj.attrs.get("index_dirs", None)
         else:
-
-            def get_out_col_name(df, col_name):
-                if col_name in df.columns:
-                    i = 1
-                    while f"{col_name}_{i}" in new_df.columns:
-                        i += 1
-                    return f"{col_name}_{i}"
-                else:
-                    return col_name
-
             new_df = self._obj.copy()
             new_df[get_out_col_name(new_df, "filter_label")] = outputs
             filtered_explanations = explanations
             filtered_raw_outputs = raw_outputs
+            filtered_scores = scores
+            filtered_score_methods = score_methods
 
         # return rows where output is True
         if return_explanations and return_raw_outputs:
-            new_df["explanation" + suffix] = filtered_explanations
-            new_df["raw_output" + suffix] = filtered_raw_outputs
+            new_df[get_out_col_name(new_df, "explanation" + suffix)] = filtered_explanations
+            new_df[get_out_col_name(new_df, "raw_output" + suffix)] = filtered_raw_outputs
         elif return_explanations:
-            new_df["explanation" + suffix] = filtered_explanations
+            new_df[get_out_col_name(new_df, "explanation" + suffix)] = filtered_explanations
         elif return_raw_outputs:
-            new_df["raw_output" + suffix] = filtered_raw_outputs
+            new_df[get_out_col_name(new_df, "raw_output" + suffix)] = filtered_raw_outputs
+
+        if return_scores:
+            new_df[get_out_col_name(new_df, "score")] = filtered_scores
+            new_df[get_out_col_name(new_df, "score_method")] = filtered_score_methods
 
         if return_stats:
             return new_df, stats
diff --git a/lotus/utils.py b/lotus/utils.py
index bf75748b..b1038744 100644
--- a/lotus/utils.py
+++ b/lotus/utils.py
@@ -132,3 +132,23 @@ def show_safe_mode(estimated_cost, estimated_LM_calls):
     except KeyboardInterrupt:
         print("\nExecution cancelled by user")
         exit(0)
+
+
+def get_out_col_name(df: pd.DataFrame, col_name: str) -> str:
+    """
+    Gets a unique column name by appending an index if the column already exists.
+
+    Args:
+        df (pd.DataFrame): The DataFrame to check for existing columns.
+        col_name (str): The base column name.
+
+    Returns:
+        str: A unique column name.
+    """
+    if col_name in df.columns:
+        i = 1
+        while f"{col_name}_{i}" in df.columns:
+            i += 1
+        return f"{col_name}_{i}"
+    else:
+        return col_name
diff --git a/tests/test_filter.py b/tests/test_filter.py
index b51319ca..75822b47 100644
--- a/tests/test_filter.py
+++ b/tests/test_filter.py
@@ -1,6 +1,8 @@
 import pandas as pd
 import pytest
 
+import lotus
+from lotus.models import LM
 from tests.base_test import BaseTest
 
 
@@ -99,3 +101,74 @@ def test_filtered_search_with_scores(self, sample_df):
         assert len(result["vec_scores_sim_score"]) == 2
         # Scores should be between 0 and 1
         assert all(0 <= score <= 1 for score in result["vec_scores_sim_score"])
+
+
+class TestFilterWithScores(BaseTest):
+    def test_filter_with_scores(self, sample_df):
+        """Test semantic filter with scores returned to the user"""
+        lm = LM(model="gpt-4o-mini")
+        lotus.settings.configure(lm=lm)
+        result = sample_df.sem_filter("{Course Name} will be fun", return_scores=True)
+        print(result)
+        assert "score" in result.columns
+        assert "score_method" in result.columns
+
+    def test_filter_with_scores_and_return_all(self, sample_df):
+        """Test semantic filter with scores returned to the user"""
+        lm = LM(model="gpt-4o-mini")
+        lotus.settings.configure(lm=lm)
+        result = sample_df.sem_filter("{Course Name} will be fun", return_scores=True, return_all=True)
+        print(result)
+        assert "score" in result.columns
+        assert "score_method" in result.columns
+
+        for idx, row in result.iterrows():
+            if row["filter_label"]:
+                assert row["score"] >= 0.5
+            else:
+                assert row["score"] <= 0.5
+
+    def test_filter_twice(self, sample_df):
+        """Test filtering twice to verify column name indexing works correctly"""
+        lm = LM(model="gpt-4o-mini")
+        lotus.settings.configure(lm=lm)
+
+        # First filter
+        result = sample_df.sem_filter(
+            "{Course Name} is related to programming",
+            return_all=True,
+            return_explanations=True,
+            return_raw_outputs=True,
+            return_scores=True,
+        )
+        print(f"First filter result: {result}")
+
+        # Verify first filter columns
+        assert "filter_label" in result.columns
+        assert "explanation_filter" in result.columns
+        assert "raw_output_filter" in result.columns
+        assert "score" in result.columns
+        assert "score_method" in result.columns
+
+        # Second filter
+        result = result.sem_filter(
+            "{Course Name} is related to programming",
+            return_all=True,
+            return_explanations=True,
+            return_raw_outputs=True,
+            return_scores=True,
+        )
+        print(f"Second filter result: {result}")
+        # Verify second filter columns have indices
+        assert "filter_label_1" in result.columns
+        assert "explanation_filter_1" in result.columns
+        assert "raw_output_filter_1" in result.columns
+        assert "score_1" in result.columns
+        assert "score_method_1" in result.columns
+
+        # Verify original columns still exist
+        assert "filter_label" in result.columns
+        assert "explanation_filter" in result.columns
+        assert "raw_output_filter" in result.columns
+        assert "score" in result.columns
+        assert "score_method" in result.columns