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06c7e2f | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 | """Coreference / pronoun resolution — elemental reading comprehension.
Tests the model's ability to resolve pronouns and other referring expressions
to their antecedents. This is a prerequisite for WinoGrande-style benchmarks
and general reading comprehension.
Categories:
- pronoun_simple: Unambiguous pronoun resolution (he/she/they)
- pronoun_hard: Winograd-style where world knowledge is needed
All cases are *unambiguous* — there is exactly one correct answer.
Format (ICL):
Sentence: "Alice told Bob that she would be late."
Who does "she" refer to?
Answer: Alice
"""
import random
from typing import Dict, List, Any
from tasks.base_task import BaseTask, TaskConfig
class CoreferenceTask(BaseTask):
"""Elemental coreference / pronoun resolution task."""
TASK_NAME = "coreference"
CATEGORY_DATA: Dict[str, List[Dict[str, str]]] = {
"pronoun_simple": [
{"input": 'Sentence: "Alice told Bob that she would be late."\nWho does "she" refer to?', "output": "Alice"},
{"input": 'Sentence: "David asked Sarah to help him with the project."\nWho does "him" refer to?', "output": "David"},
{"input": 'Sentence: "Maria finished her homework before dinner."\nWho does "her" refer to?', "output": "Maria"},
{"input": 'Sentence: "The teacher praised James because he scored the highest."\nWho does "he" refer to?', "output": "James"},
{"input": 'Sentence: "Emma called her mother and told her the good news."\nWho does the first "her" refer to?', "output": "Emma"},
{"input": 'Sentence: "Tom lent his car to his brother Mike."\nWho does "his" refer to?', "output": "Tom"},
{"input": 'Sentence: "When Lisa entered the room, she noticed the window was open."\nWho does "she" refer to?', "output": "Lisa"},
{"input": 'Sentence: "The doctor told the patient that he needed more rest."\nWho does "he" refer to?', "output": "the patient"},
{"input": 'Sentence: "Kevin and his dog went for a walk in the park."\nWho does "his" refer to?', "output": "Kevin"},
{"input": 'Sentence: "Rachel sent a letter to her grandmother."\nWho does "her" refer to?', "output": "Rachel"},
{"input": 'Sentence: "Mr. Johnson asked his secretary to reschedule the meeting."\nWho does "his" refer to?', "output": "Mr. Johnson"},
{"input": 'Sentence: "Sophie baked a cake and brought it to the party."\nWhat does "it" refer to?', "output": "the cake"},
{"input": 'Sentence: "After Mark finished his speech, he received a standing ovation."\nWho does "he" refer to?', "output": "Mark"},
{"input": 'Sentence: "Diana wrote a novel and published it last year."\nWhat does "it" refer to?', "output": "the novel"},
{"input": 'Sentence: "The manager told the employee that she had been promoted."\nWho does "she" refer to?', "output": "the employee"},
{"input": 'Sentence: "Paul took his children to the zoo on Saturday."\nWho does "his" refer to?', "output": "Paul"},
{"input": 'Sentence: "Olivia practiced the piano until she could play the piece perfectly."\nWho does "she" refer to?', "output": "Olivia"},
{"input": 'Sentence: "The chef prepared a special dish and served it to the guests."\nWhat does "it" refer to?', "output": "the dish"},
{"input": 'Sentence: "Laura thanked her friend for helping her move."\nWho does the first "her" refer to?', "output": "Laura"},
{"input": 'Sentence: "Ben fixed the car and drove it to work."\nWhat does "it" refer to?', "output": "the car"},
],
"pronoun_hard": [
{"input": 'Sentence: "The trophy didn\'t fit in the suitcase because it was too big."\nWhat was too big?', "output": "the trophy"},
{"input": 'Sentence: "The trophy didn\'t fit in the suitcase because it was too small."\nWhat was too small?', "output": "the suitcase"},
{"input": 'Sentence: "The city council refused the demonstrators a permit because they feared violence."\nWho feared violence?', "output": "the city council"},
{"input": 'Sentence: "The city council refused the demonstrators a permit because they advocated violence."\nWho advocated violence?', "output": "the demonstrators"},
{"input": 'Sentence: "Sam pulled the wagon and Chris pushed it, even though he was very tired."\nWho was tired?', "output": "Sam"},
{"input": 'Sentence: "The painting was sold to the collector because it was very valuable."\nWhat was valuable?', "output": "the painting"},
{"input": 'Sentence: "The plant didn\'t grow well in the pot because it was too dry."\nWhat was too dry?', "output": "the pot"},
{"input": 'Sentence: "The cat caught the mouse because it was too slow."\nWhat was too slow?', "output": "the mouse"},
{"input": 'Sentence: "The cat caught the mouse because it was very quick."\nWhat was quick?', "output": "the cat"},
{"input": 'Sentence: "Joan made sure to thank Susan for all the help she had given."\nWho gave help?', "output": "Susan"},
{"input": 'Sentence: "The table won\'t fit through the door because it is too wide."\nWhat is too wide?', "output": "the table"},
{"input": 'Sentence: "The table won\'t fit through the door because it is too narrow."\nWhat is too narrow?', "output": "the door"},
{"input": 'Sentence: "Bob collapsed on the sidewalk, and Jim called an ambulance for him."\nWho needed the ambulance?', "output": "Bob"},
{"input": 'Sentence: "The doctor told the nurse that she had made an error in the chart."\nWho made an error?', "output": "the nurse"},
{"input": 'Sentence: "The truck hit the pole and it broke into pieces."\nWhat broke?', "output": "the pole"},
{"input": 'Sentence: "The actress used to be the director\'s favorite, but she replaced her."\nWho was replaced?', "output": "the actress"},
{"input": 'Sentence: "The ball broke the window because it was fragile."\nWhat was fragile?', "output": "the window"},
{"input": 'Sentence: "The ball broke the window because it was moving fast."\nWhat was moving fast?', "output": "the ball"},
{"input": 'Sentence: "The guests enjoyed the meal that the chef prepared because it was delicious."\nWhat was delicious?', "output": "the meal"},
{"input": 'Sentence: "The lock couldn\'t be opened by the key because it was rusty."\nWhat was rusty?', "output": "the lock"},
],
}
CATEGORY_DEMOS: Dict[str, List[str]] = {
"pronoun_simple": [
'Sentence: "John asked Mary if she could help."\nWho does "she" refer to?\nAnswer: Mary',
'Sentence: "Carlos lost his wallet at the store."\nWho does "his" refer to?\nAnswer: Carlos',
'Sentence: "The professor graded the papers and returned them to the students."\nWhat does "them" refer to?\nAnswer: the papers',
'Sentence: "Anna brought her dog to the vet."\nWho does "her" refer to?\nAnswer: Anna',
'Sentence: "The boy kicked the ball and it flew over the fence."\nWhat does "it" refer to?\nAnswer: the ball',
],
"pronoun_hard": [
'Sentence: "The bottle didn\'t fit in the bag because it was too large."\nWhat was too large?\nAnswer: the bottle',
'Sentence: "The town council denied the miners a parade permit because they worried about damage."\nWho worried?\nAnswer: the town council',
'Sentence: "The book fell off the shelf because it was uneven."\nWhat was uneven?\nAnswer: the shelf',
'Sentence: "Bill passed the salt to John because he needed it for his soup."\nWho needed the salt?\nAnswer: John',
'Sentence: "The vase broke when the mover dropped it because it was slippery."\nWhat was slippery?\nAnswer: the vase',
],
}
def __init__(self, config: TaskConfig):
super().__init__(config)
def _load_data(self):
import pandas as pd
if self.config.in_memory_data:
self.data = pd.DataFrame(self.config.in_memory_data)
return
rows: List[Dict[str, str]] = []
for category, examples in self.CATEGORY_DATA.items():
for ex in examples:
rows.append({
"input": ex["input"],
"output": ex["output"],
"category_name": category,
})
self.data = pd.DataFrame(rows)
def build_prompt(self, instance: Dict[str, Any], num_shots: int = 5) -> str:
category = instance.get("category_name", "pronoun_simple")
demos = self.CATEGORY_DEMOS.get(category, [])
inst_input = instance.get("input", "")
demos = [d for d in demos if inst_input not in d][:num_shots]
prompt = ""
for d in demos:
prompt += d + "\n\n"
prompt += f"{inst_input}\nAnswer:"
return prompt
def evaluate(self, predictions: List[str], split: str = "test", **kwargs) -> Dict[str, float]:
ground_truth = self.get_ground_truth(split)
task_data = self.get_split(split)
if len(predictions) != len(ground_truth):
raise ValueError(
f"Prediction count ({len(predictions)}) != ground truth ({len(ground_truth)})"
)
processed = [self.preprocess_prediction(p) for p in predictions]
correct = sum(
1 for p, g in zip(processed, ground_truth)
if p.lower().strip() == g.lower().strip()
)
results: Dict[str, Any] = {
"accuracy": correct / len(ground_truth),
"correct": correct,
"total": len(ground_truth),
}
cat_stats: Dict[str, Dict[str, int]] = {}
for p, g, item in zip(processed, ground_truth, task_data):
cat = item.get("category_name", "unknown")
cat_stats.setdefault(cat, {"correct": 0, "total": 0})
cat_stats[cat]["total"] += 1
if p.lower().strip() == g.lower().strip():
cat_stats[cat]["correct"] += 1
for cat, s in cat_stats.items():
results[f"accuracy_{cat}"] = s["correct"] / s["total"]
return results
def get_ground_truth(self, split: str = "test") -> List[str]:
rows = self.get_split(split)
return [str(r.get("output", "")) for r in rows]
def create_coreference_task(
category: str = None,
name: str = "coreference",
) -> CoreferenceTask:
"""Create a CoreferenceTask, optionally filtered to one category."""
data = None
if category and category in CoreferenceTask.CATEGORY_DATA:
data = [
{**ex, "category_name": category}
for ex in CoreferenceTask.CATEGORY_DATA[category]
]
name = f"coreference:{category}"
config = TaskConfig(
name=name,
description="Coreference / pronoun resolution",
data_format="memory",
in_memory_data=data,
input_column="input",
output_column="output",
evaluation_metrics=["accuracy"],
metadata={"task_type": "coreference", "category": category},
)
return CoreferenceTask(config)
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