diff --git a/community_tasks/arabic_evals.py b/community_tasks/arabic_evals.py
index 86ab69e28..a68abbe6a 100644
--- a/community_tasks/arabic_evals.py
+++ b/community_tasks/arabic_evals.py
@@ -28,8 +28,11 @@
 """
 import random
 import re
+from typing import Any, Dict, List, Optional, Union
 
-from lighteval.metrics.metrics import Metrics
+from lighteval.metrics.llm_as_judge import JudgeLM
+from lighteval.metrics.metrics import Metric, MetricCategory, Metrics
+from lighteval.metrics.utils.metric_utils import MetricUseCase
 from lighteval.tasks.default_prompts import LETTER_INDICES
 from lighteval.tasks.lighteval_task import LightevalTaskConfig
 from lighteval.tasks.requests import Doc
@@ -832,6 +835,215 @@ def __init__(
 ]
 
 
+class JudgeMetricWrapper(Metric):
+    """Wrapper class for LLM-based judge metric implementation."""
+
+    def __init__(self, judge: JudgeLM):
+        """
+        Initializes the judge metric wrapper.
+
+        Args:
+            judge (JudgeLM): The LLM judge instance to use for evaluation.
+        """
+        self.judge = judge
+        self.metric_name = "llm_as_judge"
+        self.category = MetricCategory.LLM_AS_JUDGE
+        self.corpus_level_fn = self.aggregate_scores
+        self.sample_level_fn = self._sample_level_fn
+        self.higher_is_better = True  # Fixed tuple syntax
+        self.use_case = MetricUseCase.NONE
+
+    def compute(self, responses: list[str], formatted_docs: list[Doc], **kwargs) -> dict[str, float]:
+        """
+        Computes evaluation scores using the judge's evaluate_answer method.
+
+        Args:
+            responses (list[str]): The predicted answers
+            formatted_docs (list[Doc]): Documents containing questions and gold answers
+
+        Returns:
+            dict[str, float]: Dictionary containing evaluation scores
+        """
+        results = []
+        for i, doc in enumerate(formatted_docs):
+            question = doc.query
+            gold = doc.choices[doc.gold_index] if doc.gold_index is not None else None
+            answer = responses[i][0].result[0]
+
+            score, _, _ = self.judge.evaluate_answer(question=question, answer=answer, options=None, gold=gold)
+            results.append({self.metric_name: score})
+
+        return results
+
+    def aggregate_scores(self, scores: list[dict]) -> float:
+        return sum(scores) / len(scores) if scores else 0.0
+
+    def _sample_level_fn(self):
+        return None
+
+
+def parse_candidates(candidates: Union[List[str], str]) -> List[str]:
+    """
+    Parses and validates candidate answers from either list or string format.
+
+    Args:
+        candidates: Either a list of candidate answers or a newline-separated string
+
+    Returns:
+        List[str]: List of validated candidate answers
+
+    Raises:
+        ValueError: If candidates cannot be parsed or are empty
+    """
+    try:
+        if isinstance(candidates, list):
+            parsed_candidates = [str(c).strip() for c in candidates if c]
+        else:
+            parsed_candidates = [c.strip() for c in str(candidates).split("\n") if c.strip()]
+
+        if not parsed_candidates:
+            raise ValueError("No valid candidates found after parsing")
+
+        return parsed_candidates
+    except Exception as e:
+        raise ValueError(f"Failed to parse candidates: {str(e)}")
+
+
+def qa_prompt_arabic(line: Dict[str, Any], task_name: str = None) -> Doc:
+    """
+    Formats the prompt for Arabic question answering with candidates.
+
+    Args:
+        line: Dictionary containing question and candidate information
+        task_name: Optional name for the task
+
+    Returns:
+        Doc: Formatted document for evaluation
+
+    Raises:
+        ValueError: If required fields are missing or invalid
+    """
+    try:
+        # Validates and extracts the question
+        if not isinstance(line.get("question"), str):
+            raise ValueError("Question must be a string")
+        question = line["question"]
+
+        # Processes candidate answers
+        candidates = parse_candidates(line["candidates"])
+
+        # Validates gold answer
+        if "gold_answer" not in line:
+            raise ValueError("Gold answer is required")
+        gold_answer = str(line["gold_answer"])
+
+        # Constructs the prompt
+        instruction = "بناءً على السياقات المقترحة التالية، اجب عن السؤال التالي"
+        query = f"{instruction}\n\nالسؤال:\n{question}\n\nالسياقات المقترحة:\n{', '.join(candidates)}\n"
+
+        return Doc(
+            task_name=task_name or "alrage",
+            query=query,
+            instruction=instruction,
+            choices=[gold_answer],  # Gold answer is used as the only valid choice
+            gold_index=0,  # Index of the correct answer in choices
+        )
+    except Exception as e:
+        raise ValueError(f"Failed to create QA prompt: {str(e)}")
+
+
+def judge_template(question: str, answer: str, gold: str, options: Optional[List[str]] = None) -> List[Dict[str, str]]:
+    """
+    Template for the Arabic judge prompt.
+
+    System prompt translation:
+    You are a neutral expert evaluator. Your tasks are:
+    1. Evaluate the answer's accuracy compared to the correct answer
+    2. Verify that the answer is supported by the provided context
+    3. Evaluate the quality and comprehensiveness of the answer
+    Rate the answer on a scale from 0 to 10.
+
+    Args:
+        question: The question being evaluated
+        answer: The provided answer
+        gold: The correct answer
+        options: Optional list of answer choices
+
+    Returns:
+        List[Dict[str, str]]: Formatted messages for the judge
+    """
+    messages = [
+        {
+            "role": "system",
+            "content": """أنت مقيّم محايد خبير باللغة العربية. يجب عليك:
+1. تقييم دقة الإجابة مقارنة بالإجابة الصحيحة
+2. التحقق من أن الإجابة مدعومة بالسياق المقدم
+3. تقييم جودة وشمولية الإجابة
+
+مهم جداً: يجب أن يكون ردك رقماً فقط من 0 إلى 10. لا تضف أي نص أو تفسير.""",
+        },
+        {
+            "role": "user",
+            "content": f"""السؤال: {question}
+
+الإجابة المقدمة: {answer}
+
+الإجابة الصحيحة: {gold}
+
+أعط تقييماً من 0 إلى 10:
+0-2: إجابة خاطئة تماماً
+3-4: إجابة جزئية مع أخطاء
+5-6: إجابة متوسطة
+7-8: إجابة جيدة
+9-10: إجابة ممتازة
+
+اكتب رقماً فقط من 0 إلى 10 بدون أي نص إضافي:""",
+        },
+    ]
+    return messages
+
+
+def process_judge_response(response) -> float:
+    """Process the judge's response to extract the score"""
+    # If response is a list, extract the content from the user role
+    if isinstance(response, list):
+        response_content = " ".join(item["content"] for item in response if item["role"] == "user")
+    else:
+        response_content = response  # If it's not a list, use it directly
+
+    try:
+        # Extract the score from the response content
+        score = float(next(num for num in response_content.split() if num.replace(".", "", 1).isdigit()))
+        return min(max(score / 10.0, 0.0), 1.0)
+    except (StopIteration, ValueError):
+        return 0.0
+
+
+judge = JudgeLM(
+    model="Qwen/Qwen2.5-72B-Instruct",
+    templates=judge_template,
+    process_judge_response=process_judge_response,
+    judge_backend="vllm",
+)
+
+wrapped_judge = JudgeMetricWrapper(judge)
+
+# Task configuration
+alrage_qa_task = LightevalTaskConfig(
+    name="alrage_qa",
+    prompt_function=qa_prompt_arabic,
+    suite=["community"],
+    hf_repo="OALL/ALRAGE",
+    hf_subset=None,
+    hf_avail_splits=["train"],
+    evaluation_splits=["train"],
+    metric=[wrapped_judge],
+    trust_dataset=True,
+    generation_size=200,
+    stop_sequence=[],
+    version=0,
+)
+
 TASKS_TABLE = (
     ARABIC_MMLU_TASKS
     + ARABIC_MMLU_HT_TASKS
@@ -852,4 +1064,5 @@ def __init__(
     + [hellaswag_okapi_ar_task]
     + [toxigen_ar_task]
     + [sciq_ar_task]
+    + [alrage_qa_task]
 )
diff --git a/community_tasks/french_evals.py b/community_tasks/french_evals.py
new file mode 100644
index 000000000..7fda8dc7a
--- /dev/null
+++ b/community_tasks/french_evals.py
@@ -0,0 +1,121 @@
+# MIT License
+
+# Copyright (c) 2024 The HuggingFace Team
+
+# Permission is hereby granted, free of charge, to any person obtaining a copy
+# of this software and associated documentation files (the "Software"), to deal
+# in the Software without restriction, including without limitation the rights
+# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+# copies of the Software, and to permit persons to whom the Software is
+# furnished to do so, subject to the following conditions:
+
+# The above copyright notice and this permission notice shall be included in all
+# copies or substantial portions of the Software.
+
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+
+# ruff: noqa: F405, F403, F401
+"""
+Custom evaluation tasks for lighteval.
+
+This file generally creates just a TASKS_TABLE and TASKS_GROUPS which are then imported by LightEval.
+
+This module implements tasks for the french specific datasets
+See : https://huggingface.co/fr-gouv-coordination-ia
+"""
+
+import random
+
+import numpy as np
+from aenum import extend_enum
+
+import lighteval.tasks.extended.ifeval.instructions_registry as instructions_registry
+from lighteval.metrics.metrics import Metrics, SampleLevelMetric
+from lighteval.metrics.utils.metric_utils import (
+    MetricCategory,
+    MetricUseCase,
+    SampleLevelMetricGrouping,
+)
+from lighteval.tasks.default_prompts import LETTER_INDICES
+from lighteval.tasks.extended.ifeval.main import ifeval_metrics
+from lighteval.tasks.lighteval_task import LightevalTaskConfig
+from lighteval.tasks.requests import Doc
+
+
+# Ifeval-fr prompt function
+def prompt_ifeval_fr(line, task_name: str = None):
+    return Doc(
+        task_name=task_name,
+        query=line["prompt"],
+        choices=[""],
+        gold_index=0,
+        instruction="",
+        specific={"instructions_id_list": line["instruction_id_list"], "kwargs": line["kwargs"]},
+    )
+
+
+# qpqa-fr prompt function
+def prompt_gpqa_fr(line, task_name: str = None):
+    gold_index = random.randint(0, 3)
+    choices = [line["Réponse incorrecte 1"], line["Réponse incorrecte 2"], line["Réponse incorrecte 3"]]
+    choices.insert(gold_index, line["Réponse correcte"])
+
+    instruction = "Choisissez la réponse correcte aux questions suivantes.\n\n"
+
+    query = f"Question: {line['Question']}\n"
+    query += "".join([f"{key}. {choice}\n" for key, choice in zip(LETTER_INDICES, choices)])
+    query += "Answer: "
+    return Doc(
+        task_name=task_name,
+        query=f"{instruction}{query}",
+        choices=LETTER_INDICES[: len(choices)],
+        gold_index=gold_index,
+        instruction=instruction,
+    )
+
+
+# IFEVal-fr task
+
+
+ifeval_fr_task = LightevalTaskConfig(
+    name="ifeval-fr",
+    prompt_function=prompt_ifeval_fr,  # must be defined in the file or imported from src/lighteval/tasks/tasks_prompt_formatting.py
+    suite=["community"],
+    hf_repo="fr-gouv-coordination-ia/IFEval-fr",
+    hf_subset="default",
+    metric=[ifeval_metrics],
+    hf_avail_splits=["train"],
+    evaluation_splits=["train"],
+    few_shots_split="train",
+    few_shots_select="random_sampling",
+    generation_size=1280,
+    stop_sequence=[],  # no stop sequence, will use eot token
+    version="0.1",  # select your metric in Metrics
+)
+
+# GPQA-fr task
+gpqa_fr_task = LightevalTaskConfig(
+    name="gpqa-fr",
+    suite=["community"],
+    prompt_function=prompt_gpqa_fr,
+    hf_repo="fr-gouv-coordination-ia/gpqa-fr",
+    hf_subset="default",
+    hf_avail_splits=["train"],
+    evaluation_splits=["train"],
+    few_shots_split=None,
+    few_shots_select="random_sampling",
+    generation_size=1,
+    metric=[Metrics.loglikelihood_acc],
+    stop_sequence=["\n"],
+    trust_dataset=True,
+    version=0,
+)
+
+# STORE YOUR EVALS
+TASKS_TABLE = [ifeval_fr_task, gpqa_fr_task]
diff --git a/examples/tasks/OALL_v2_tasks.txt b/examples/tasks/OALL_v2_tasks.txt
index fc1b4f7e9..176b662d7 100644
--- a/examples/tasks/OALL_v2_tasks.txt
+++ b/examples/tasks/OALL_v2_tasks.txt
@@ -115,3 +115,4 @@ community|arabic_mmlu_ht:sociology|0|0
 community|arabic_mmlu_ht:us_foreign_policy|0|0
 community|arabic_mmlu_ht:virology|0|0
 community|arabic_mmlu_ht:world_religions|0|0
+community|alrage_qa|0|0
diff --git a/pyproject.toml b/pyproject.toml
index f407895a0..126d66244 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -57,7 +57,7 @@ dependencies = [
     "transformers>=4.38.0",
     "accelerate",
     "huggingface_hub>=0.23.0",
-    "torch>=2.0,<2.5",
+    "torch>=2.0,<3.0",
     "GitPython>=3.1.41", # for logging
     "datasets>=2.14.0",
     "numpy<2",  # pinned to avoid incompatibilities
diff --git a/src/lighteval/logging/evaluation_tracker.py b/src/lighteval/logging/evaluation_tracker.py
index 6cad9189f..af5784bc2 100644
--- a/src/lighteval/logging/evaluation_tracker.py
+++ b/src/lighteval/logging/evaluation_tracker.py
@@ -149,6 +149,28 @@ def __init__(
 
         self.public = public
 
+    @property
+    def results(self):
+        config_general = asdict(self.general_config_logger)
+        # We remove the config from logging, which contains context/accelerator objects
+        config_general.pop("config")
+        results = {
+            "config_general": config_general,
+            "results": self.metrics_logger.metric_aggregated,
+            "versions": self.versions_logger.versions,
+            "config_tasks": self.task_config_logger.tasks_configs,
+            "summary_tasks": self.details_logger.compiled_details,
+            "summary_general": asdict(self.details_logger.compiled_details_over_all_tasks),
+        }
+        return results
+
+    @property
+    def details(self):
+        return {
+            task_name: [asdict(detail) for detail in task_details]
+            for task_name, task_details in self.details_logger.details.items()
+        }
+
     def save(self) -> None:
         """Saves the experiment information and results to files, and to the hub if requested."""
         logger.info("Saving experiment tracker")
@@ -281,6 +303,31 @@ def push_to_hub(
 
         self.recreate_metadata_card(repo_id)
 
+    def push_results_to_hub(self, repo_id: str, path_in_repo: str, private: bool | None = None):
+        repo_id = repo_id if "/" in repo_id else f"{self.hub_results_org}/{repo_id}"
+        private = private if private is not None else not self.public
+        self.api.create_repo(repo_id, private=private, repo_type="dataset", exist_ok=True)
+        results_json = json.dumps(self.results, cls=EnhancedJSONEncoder, indent=2, ensure_ascii=False)
+        self.api.upload_file(
+            repo_id=repo_id,
+            path_or_fileobj=results_json.encode(),
+            path_in_repo=path_in_repo,
+            repo_type="dataset",
+        )
+
+    def push_details_to_hub(self, repo_id: str, path_in_repo: str, private: bool | None = None):
+        repo_id = repo_id if "/" in repo_id else f"{self.hub_results_org}/{repo_id}"
+        private = private if private is not None else not self.public
+        self.api.create_repo(repo_id, private=private, repo_type="dataset", exist_ok=True)
+        for task_name, details in self.details:
+            details_json = "\n".join([json.dumps(detail) for detail in details])
+            self.api.upload_file(
+                repo_id=repo_id,
+                path_or_fileobj=details_json.encode(),
+                path_in_repo=path_in_repo.format(task_name=task_name),
+                repo_type="dataset",
+            )
+
     def recreate_metadata_card(self, repo_id: str) -> None:  # noqa: C901
         """Fully updates the details repository metadata card for the currently evaluated model
 
diff --git a/src/lighteval/metrics/llm_as_judge.py b/src/lighteval/metrics/llm_as_judge.py
index 81e1d7d30..23beda76f 100644
--- a/src/lighteval/metrics/llm_as_judge.py
+++ b/src/lighteval/metrics/llm_as_judge.py
@@ -28,7 +28,6 @@
 
 from tqdm import tqdm
 
-from lighteval.models.model_output import ModelResponse
 from lighteval.utils.imports import is_litellm_available, is_openai_available, is_vllm_available
 
 
@@ -194,6 +193,7 @@ def __call_litellm(self, prompts):
         import litellm
 
         def __call_api(prompt):
+            error_message = "ERROR: Failed to get response from the API."
             for _ in range(self.API_MAX_RETRY):
                 try:
                     kwargs = {
@@ -206,20 +206,19 @@ def __call_api(prompt):
                     }
                     response = litellm.completion(**kwargs)
                     text = response.choices[0].message.content
-                    if not text or response.failed:
+                    if not text or text == error_message:
                         kwargs["caching"] = False
                         response = litellm.completion(**kwargs)
                         text = response.choices[0].message.content
-                        if not text or response.failed:
+                        if not text or text == error_message:
                             # Just return an error response if the second attempt fails too
-                            return ModelResponse(
-                                text="Failed to get response from the API.", model=self.model, failed=True
-                            )
+                            logger.error(f"Failed to get response from the API for prompt: {prompt}")
+                            return error_message
                     return text
                 except Exception as e:
                     logger.warning(f"{type(e), e}")
                     time.sleep(self.API_RETRY_SLEEP)
-            return ModelResponse(text="Failed to get response from the API.", model=self.model, failed=True)
+            return error_message
 
         results = []
         with ThreadPoolExecutor(100) as executor:
diff --git a/src/lighteval/metrics/metrics_corpus.py b/src/lighteval/metrics/metrics_corpus.py
index 03b1b2c5e..3c2de418f 100644
--- a/src/lighteval/metrics/metrics_corpus.py
+++ b/src/lighteval/metrics/metrics_corpus.py
@@ -26,6 +26,7 @@
 """
 import logging
 import math
+from typing import Literal
 
 import numpy as np
 import sacrebleu
@@ -89,33 +90,38 @@ def compute(self, items: list[LogprobCorpusMetricInput]):
 
 
 class CorpusLevelTranslationMetric:
-    def __init__(self, metric_type: str):
+    def __init__(self, metric_type: str, lang: Literal["zh", "ja", "ko", ""] = ""):
         """Stores the relevant parameters for a corpus level translation metric.
 
         Args:
             metric_type (str): Can be any of bleu, chrf, or ter depending on the metric to use.
         """
-        if metric_type == "bleu":
-            self.metric = sacrebleu.corpus_bleu
-        elif metric_type == "chrf":
-            self.metric = sacrebleu.corpus_chrf
-        elif metric_type == "ter":
-            self.metric = sacrebleu.corpus_ter
+        self.metric_type = metric_type
+        self.lang = lang
+
+    def get_metric(self):
+        if self.metric_type == "bleu":
+            return sacrebleu.BLEU(trg_lang=self.lang)
+        elif self.metric_type == "chrf":
+            return sacrebleu.CHRF()
+        elif self.metric_type == "ter":
+            return sacrebleu.TER(asian_support=True if self.lang != "" else False)
         else:
-            raise ValueError(f"Unknown corpus level translation metric type : {metric_type}")
+            raise ValueError(f"Unknown corpus level translation metric type : {self.metric_type}")
 
     def compute(self, items: list[GenerativeCorpusMetricInput]) -> float:
         """Computes the metric score over all the corpus generated items, by using the sacrebleu implementation."""
+        metric = self.get_metric()
         golds = [i.golds for i in items]
         preds = []
         for i in items:
             pred = as_list(i.preds)
             if len(pred) > 1:
                 logger.info(
-                    f"Multiple predictions present, keeping only the first prediction (when computing sacrebleu.{self.metric.__name__})."
+                    f"Multiple predictions present, keeping only the first prediction (when computing sacrebleu.{metric.__name__})."
                 )
             preds.append(pred[0])
-        return float(self.metric(hypotheses=preds, references=golds).score)
+        return float(metric.corpus_score(hypotheses=preds, references=golds).score)
 
 
 class CorpusLevelPerplexityMetric:
diff --git a/src/lighteval/models/litellm_model.py b/src/lighteval/models/litellm_model.py
index 9e29f569d..195221aa5 100644
--- a/src/lighteval/models/litellm_model.py
+++ b/src/lighteval/models/litellm_model.py
@@ -91,7 +91,7 @@ def __init__(self, config, env_config) -> None:
         self._tokenizer = encode
         self.pairwise_tokenization = False
         litellm.drop_params = True
-        litellm.verbose = True
+        litellm.set_verbose = False
 
     def _prepare_stop_sequence(self, stop_sequence):
         """Prepare and validate stop sequence."""
@@ -130,13 +130,16 @@ def __call_api(self, prompt, return_logits, max_new_tokens, num_samples, stop_se
                     "messages": prompt,
                     "max_completion_tokens": max_new_tokens,
                     "logprobs": return_logits if self.provider == "openai" else None,
-                    "stop": stop_sequence,
                     "base_url": self.base_url,
                     "n": num_samples,
-                    "temperature": self.TEMPERATURE,
-                    "top_p": self.TOP_P,
                     "caching": True,
                 }
+                if "o1" in self.model:
+                    logger.warning("O1 models do not support temperature, top_p, stop sequence. Disabling.")
+                else:
+                    kwargs["temperature"] = self.TEMPERATURE
+                    kwargs["top_p"] = self.TOP_P
+                    kwargs["stop"] = stop_sequence
 
                 response = litellm.completion(**kwargs)
 
diff --git a/src/lighteval/models/model_output.py b/src/lighteval/models/model_output.py
index b485371ca..7d0ba4818 100644
--- a/src/lighteval/models/model_output.py
+++ b/src/lighteval/models/model_output.py
@@ -33,7 +33,6 @@ class ModelResponse:
     generated_tokens: list[int] = field(default_factory=list)  # model generations
     truncated_tokens_count: Optional[int] = 0  # How many tokens truncated
     padded_tokens_count: Optional[int] = 0  # How many tokens of padding
-    failed: bool = False
 
     def get_result_for_eval(self):
         raise NotImplementedError()
diff --git a/src/lighteval/tasks/default_prompts.py b/src/lighteval/tasks/default_prompts.py
index c5395281c..66c3d53b4 100644
--- a/src/lighteval/tasks/default_prompts.py
+++ b/src/lighteval/tasks/default_prompts.py
@@ -120,7 +120,7 @@ def asdiv(line, task_name: str = None):
 
 def babi_qa(line, task_name: str = None):  # HELM
     def process_path(path: str) -> str:
-        """Turn a path string (task 19) from the original format 's,w' to a verbal model-friendly format 'south west'"""
+        """Turn a path string (task 19) from the original format 's,w' into a verbal model-friendly format 'south west'"""
         steps = path.split(",")
         directions = {"s": "south", "n": "north", "e": "east", "w": "west"}
         path = " ".join([directions[step] for step in steps])
@@ -281,7 +281,7 @@ def bbh_logical_deduction_three_objects(line, task_name: str = None):
 def bbh_movie_recommendation(line, task_name: str = None):
     if line["target"] == "Monsters, Inc":  # this line is not correctly formatted
         logger.warning(
-            "One sample removed from task bbh:movie_recommentation because its line is incorrectly formatted."
+            "One sample removed from task bbh:movie_recommendation because its line is incorrectly formatted."
         )
         return []
     instruction = "Recommend movies similar to the given list of movies.\n\n"
@@ -500,7 +500,7 @@ def civil_comments(line, task_name: str = None):
 def cnn_dm(line, task_name: str = None):
     return Doc(
         task_name=task_name,
-        query=f"###\nArticle:{line['article']}\n\nSummarize the above article in 3 sentence.\n",
+        query=f"###\nArticle:{line['article']}\n\nSummarize the above article in 3 sentences.\n",
         choices=[str(line["summary"])],
         gold_index=0,
         specific={"text": line["article"]},
@@ -730,7 +730,7 @@ def gpqa(line, task_name: str = None):
 
 
 def gsm8k(line, task_name: str = None):
-    # Has special analysis in metric for number decomposiition
+    # Has special analysis in metric for number decomposition
     return Doc(
         task_name=task_name,
         query=f"Question: {line['question']}\nAnswer:",
@@ -2076,7 +2076,7 @@ def rte(line, task_name: str = None):
     return Doc(
         task_name=task_name,
         query=f"{line['sentence1']}\nQuestion: {line['sentence2']} True or False?\nAnswer:",
-        choices=[" True", " False"],  # 0 = entailement, 1 = not entailment
+        choices=[" True", " False"],  # 0 = entailment, 1 = not entailment
         gold_index=int(line["label"]),
         # "metric": "choices_loglikelihood",
     )
diff --git a/src/lighteval/tasks/extended/mix_eval/main.py b/src/lighteval/tasks/extended/mix_eval/main.py
index 8684e910c..eaa58f2a5 100644
--- a/src/lighteval/tasks/extended/mix_eval/main.py
+++ b/src/lighteval/tasks/extended/mix_eval/main.py
@@ -20,6 +20,7 @@
 # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
 # SOFTWARE.
 
+import logging
 import re
 
 import numpy as np
@@ -37,6 +38,9 @@
 from lighteval.tasks.requests import Doc
 
 
+logger = logging.getLogger(__name__)
+
+
 def mixeval_freeform_prompt(line, task_name: str = ""):
     prompt = construct_prompt_freeform(line)
     return Doc(
@@ -71,19 +75,30 @@ def mixeval_multichoice_prompt(line, task_name: str = ""):
 
 
 def process_judge_response(x):
-    search = re.search(r"<score>\s(\d)\s</score>", x)
-    return int(search.group(1)) if search else 0
+    try:
+        search = re.search(r"<score>\s(\d)\s</score>", x)
+        return int(search.group(1)) if search else 0
+    except Exception as e:
+        logger.warning(f"Error processing judge response for flow: {e}")
+        return 0
 
 
 def process_judge_response_multichoice_gpt(x):
-    search = re.search(r"\[\[([01])\]\]", x)
-    return int(search.group(1)) if search else 0
+    try:
+        search = re.search(r"\[\[([01])\]\]", x)
+        return int(search.group(1)) if search else 0
+    except Exception as e:
+        logger.warning(f"Error processing judge response for multichoice GPT: {e}")
+        return 0
 
 
 def process_judge_response_freeform_gpt(x):
-    search = re.search(r"\[\[(\d.\d)\]\]", x)
-    answer = float(search.group(1) if search else 0)
-    return answer
+    try:
+        search = re.search(r"\[\[(\d.\d)\]\]", x)
+        return float(search.group(1)) if search else 0
+    except Exception as e:
+        logger.warning(f"Error processing judge response for freeform GPT: {e}")
+        return 0
 
 
 llm_judge_mixeval_multichoice_flow_judge = SampleLevelMetricGrouping(
diff --git a/src/lighteval/tasks/lighteval_task.py b/src/lighteval/tasks/lighteval_task.py
index 09886e4db..c187f653f 100644
--- a/src/lighteval/tasks/lighteval_task.py
+++ b/src/lighteval/tasks/lighteval_task.py
@@ -621,7 +621,7 @@ def create_requests_from_tasks(  # noqa: C901
         n_samples = min(max_samples, len(task_docs)) if max_samples else len(task_docs)
         evaluation_tracker.task_config_logger.log_num_docs(task_name, len(task_docs), n_samples)
 
-        # logs out the diferent versions of the tasks for every few shot
+        # logs out the different versions of the tasks for every few shot
         for num_fewshot, _ in fewshot_dict[task_name]:
             cur_task_name = f"{task_name}|{num_fewshot}"
             evaluation_tracker.versions_logger.log(cur_task_name, task.version)
@@ -633,7 +633,7 @@ def create_requests_from_tasks(  # noqa: C901
         prompt_manager = PromptManager(lm=lm, task=task)
         seeds = prompt_manager.few_shot_sampler.get_fewshot_seeds(num_fewshot_seeds)
 
-        # We can do several round of fewshots sampling to get some variance informations
+        # We can do several round of fewshots sampling to get some variance information
         for seed in seeds:
             for doc_id in range(n_samples):
                 doc_id_seed = f"{doc_id}_{seed}"  # if we do several rounds of few shot sampling we have several seeds
diff --git a/src/lighteval/tasks/prompt_manager.py b/src/lighteval/tasks/prompt_manager.py
index af55c3184..6d066c921 100644
--- a/src/lighteval/tasks/prompt_manager.py
+++ b/src/lighteval/tasks/prompt_manager.py
@@ -132,7 +132,7 @@ def _multi_turn_contexts(self, doc: Doc, use_chat_template: bool, system_prompt:
         Multi turn tasks need use chat templating.
 
         Args:
-            doc (Doc): Formated document.
+            doc (Doc): Formatted document.
             use_chat_template (bool): wether or not to use chat template. Will fail if false.
             system_prompt (Optional[str]): The system prompt to use
             tokenizer (PreTrainedTokenizer): The tokenizer used for the chat template
diff --git a/src/lighteval/tasks/registry.py b/src/lighteval/tasks/registry.py
index 834e81706..174a98d33 100644
--- a/src/lighteval/tasks/registry.py
+++ b/src/lighteval/tasks/registry.py
@@ -166,7 +166,7 @@ def _task_superset_dict(self):
                 "lighteval|mmlu" -> ["lighteval|mmlu:abstract_algebra", "lighteval|mmlu:college_biology", ...]
             }
         """
-        # Note: sorted before groupby is imporant as the python implementation of groupby does not
+        # Note: sorted before groupby is important as the python implementation of groupby does not
         # behave like sql groupby. For more info see the docs of itertools.groupby
         superset_dict = {k: list(v) for k, v in groupby(sorted(self.task_registry.keys()), lambda x: x.split(":")[0])}
         # Only consider supersets with more than one task
diff --git a/src/lighteval/tasks/templates/continuation.py b/src/lighteval/tasks/templates/continuation.py
index 6435fc8f2..c9cd5d1bc 100644
--- a/src/lighteval/tasks/templates/continuation.py
+++ b/src/lighteval/tasks/templates/continuation.py
@@ -112,7 +112,7 @@ def get_continuation_prompt_function(
     C. Continuation 3
     Answer: A/B/C
 
-    This template is very similar to the `Multiple Choice` template, except that it only takes context/continuations as input and don't use the anchor labels (Question/Answer)
+    This template is very similar to the `Multiple Choice` template, except that it only takes context/continuations as input and doesn't use the anchor labels (Question/Answer)
 
     Args:
         language (Language): The language of the Continuation task.
diff --git a/src/lighteval/tasks/templates/copa.py b/src/lighteval/tasks/templates/copa.py
index 2129332f8..a4d82c4de 100644
--- a/src/lighteval/tasks/templates/copa.py
+++ b/src/lighteval/tasks/templates/copa.py
@@ -86,17 +86,17 @@ def get_copa_prompt_function(
 
     Format:
     *CF*
-    Context Premise thefore/cause | (Continuation 1, Continuation 2, Continuation 3)
+    Context Premise therefore/cause | (Continuation 1, Continuation 2, Continuation 3)
 
     *Hybrid*
-    Context Premise thefore/cause
+    Context Premise therefore/cause
     A. Continuation 1
     B. Continuation 2
     C. Continuation 3
     Answer: | Continuation 1/Continuation 2/Continuation 3
 
     *MCF*
-    Context Premise thefore/cause
+    Context Premise therefore/cause
     A. Continuation 1
     B. Continuation 2
     C. Continuation 3
diff --git a/src/lighteval/tasks/templates/hellaswag.py b/src/lighteval/tasks/templates/hellaswag.py
index 43a3061b6..f5c4ba3db 100644
--- a/src/lighteval/tasks/templates/hellaswag.py
+++ b/src/lighteval/tasks/templates/hellaswag.py
@@ -70,7 +70,7 @@ def get_hellaswag_prompt_function(
     Create a templated prompt function for a Hellaswag task.
 
     Format:
-    Context Premise thefore/cause | (Continuation 1, Continuation 2, Continuation 3)
+    Context Premise therefore/cause | (Continuation 1, Continuation 2, Continuation 3)
 
     Args:
         language (Language): The language of the Hellaswag task.
@@ -126,7 +126,7 @@ def hellaswag_prompt(
         if ctx_b:
             ctx_a = join_ctxs(ctx_a, ctx_b)
 
-        # Removoal of the [header] can happen and we need the first letter to be capital afterwards
+        # Removal of the [header] can happen and we need the first letter to be capital afterwards
         full_context = HELLASWAG_QUERY.format(activity_label=activity_label, ctx=ctx_a)
         choices = [
             hellaswag_preprocess(
diff --git a/src/lighteval/tasks/templates/nli.py b/src/lighteval/tasks/templates/nli.py
index 842460306..e8809e17b 100644
--- a/src/lighteval/tasks/templates/nli.py
+++ b/src/lighteval/tasks/templates/nli.py
@@ -228,7 +228,7 @@ def prompt_fn(line: dict, task_name: str):
         if input_data is None:
             return None
 
-        # Template based on dicussion here: https://github.com/EleutherAI/lm-evaluation-harness/issues/450
+        # Template based on discussion here: https://github.com/EleutherAI/lm-evaluation-harness/issues/450
         labels = [capitalize(get_relation_label(label, translation_literals)) for label in relations]
 
         premise, hypothesis, gold_idx = input_data["premise"], input_data["hypothesis"], input_data["gold_idx"]
@@ -236,15 +236,15 @@ def prompt_fn(line: dict, task_name: str):
         hypothesis = input_data["hypothesis"]
         if isinstance(formulation, HybridFormulation):
             # If we have the neither option move it to the end to be consistent with standard NLI evaluation
-            rearanged_labales = labels
+            rearranged_labels = labels
             if "neutral" in relations:
                 neutral_idx = relations.index("neutral")
-                rearanged_labales = labels[:neutral_idx] + labels[neutral_idx + 1 :] + [labels[neutral_idx]]
+                rearranged_labels = labels[:neutral_idx] + labels[neutral_idx + 1 :] + [labels[neutral_idx]]
 
-            choices_str = f"{translation_literals.comma}{translation_literals.word_space}".join(rearanged_labales[:-1])
-            hypothesis = f"{hypothesis.rstrip(PUNCT)}{translation_literals.sentence_space}{choices_str}{translation_literals.word_space}{translation_literals.or_word}{translation_literals.word_space}{rearanged_labales[-1]}{translation_literals.question_mark}"
+            choices_str = f"{translation_literals.comma}{translation_literals.word_space}".join(rearranged_labels[:-1])
+            hypothesis = f"{hypothesis.rstrip(PUNCT)}{translation_literals.sentence_space}{choices_str}{translation_literals.word_space}{translation_literals.or_word}{translation_literals.word_space}{rearranged_labels[-1]}{translation_literals.question_mark}"
 
-        # (hynky1999): Ideally we would not compute logprobs of the Yes/No/Also in CF fomulation. However as of right now lighteval doesn't allow to
+        # (hynky1999): Ideally we would not compute logprobs of the Yes/No/Also in CF formulation. However as of right now lighteval doesn't allow to
         # use multi-context.
         row = {
             "instruction": input_data.get("instruction", ""),
diff --git a/src/lighteval/tasks/templates/utils/translation_literals.py b/src/lighteval/tasks/templates/utils/translation_literals.py
index 51daf7198..756285e62 100644
--- a/src/lighteval/tasks/templates/utils/translation_literals.py
+++ b/src/lighteval/tasks/templates/utils/translation_literals.py
@@ -178,7 +178,29 @@ def __getattribute__(self, name: str) -> str:
     Language.BRETON: TranslationLiterals(language=Language.BRETON),
     Language.BULGARIAN: TranslationLiterals(language=Language.BULGARIAN),
     Language.BURMESE: TranslationLiterals(language=Language.BURMESE),
-    Language.CATALAN: TranslationLiterals(language=Language.CATALAN),
+    Language.CATALAN: TranslationLiterals(
+        language=Language.CATALAN,
+        question_word="pregunta",
+        answer="resposta",
+        confirmation_word="cert",
+        yes="sí",
+        no="no",
+        also="també",
+        cause_word="perquè",
+        effect_word="per tant",
+        or_word="o",
+        true="veritable",
+        false="fals",
+        neither="cap",
+        full_stop=".",
+        comma=",",
+        question_mark="?",
+        exclamation_mark="!",
+        word_space=" ",
+        sentence_space=" ",
+        colon=":",
+        semicolon=";",
+    ),
     Language.CEBUANO: TranslationLiterals(language=Language.CEBUANO),
     Language.CHINESE: TranslationLiterals(
         language=Language.CHINESE,
@@ -348,7 +370,29 @@ def __getattribute__(self, name: str) -> str:
         sentence_space=" ",
         colon=":",
     ),
-    Language.GALICIAN: TranslationLiterals(language=Language.GALICIAN),
+    Language.GALICIAN: TranslationLiterals(
+        language=Language.GALICIAN,
+        question_word="pregunta",
+        answer="resposta",
+        confirmation_word="certo",
+        yes="si",
+        no="non",
+        also="tamén",
+        cause_word="porque",
+        effect_word="polo tanto",
+        or_word="ou",
+        true="verdadeiro",
+        false="falso",
+        neither="ningún",
+        full_stop=".",
+        comma=",",
+        question_mark="?",
+        exclamation_mark="!",
+        word_space=" ",
+        sentence_space=" ",
+        colon=":",
+        semicolon=";",
+    ),
     Language.GEORGIAN: TranslationLiterals(language=Language.GEORGIAN),
     Language.GERMAN: TranslationLiterals(
         language=Language.GERMAN,