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import dataclasses
from typing import Dict, Optional, Union
from lm_eval.tasks.ifeval import instructions_registry
from lm_eval.utils import eval_logger
@dataclasses.dataclass
class InputExample:
key: int
instruction_id_list: list[str]
prompt: str
kwargs: list[Dict[str, Optional[Union[str, int]]]]
@dataclasses.dataclass
class OutputExample:
instruction_id_list: list[str]
prompt: str
response: str
follow_all_instructions: bool
follow_instruction_list: list[bool]
def test_instruction_following_strict(
inp,
response,
):
"""Tests response to see if instructions are followed."""
instruction_list = inp.instruction_id_list
is_following_list = []
for index, instruction_id in enumerate(instruction_list):
instruction_cls = instructions_registry.INSTRUCTION_DICT[instruction_id]
instruction = instruction_cls(instruction_id)
# Remove None values from kwargs to avoid unexpected keyword argument errors in build_description method.
kwargs = {k: v for k, v in inp.kwargs[index].items() if v}
instruction.build_description(**kwargs)
args = instruction.get_instruction_args()
if args and "prompt" in args:
instruction.build_description(prompt=inp.prompt)
if response.strip() and instruction.check_following(response):
is_following_list.append(True)
else:
is_following_list.append(False)
return OutputExample(
instruction_id_list=inp.instruction_id_list,
prompt=inp.prompt,
response=response,
follow_all_instructions=all(is_following_list),
follow_instruction_list=is_following_list,
)
def test_instruction_following_loose(
inp,
response,
):
"""Tests response for an upper bound for following instructions."""
r = response.split("\n")
response_remove_first = "\n".join(r[1:]).strip()
response_remove_last = "\n".join(r[:-1]).strip()
response_remove_both = "\n".join(r[1:-1]).strip()
revised_response = response.replace("*", "")
revised_response_remove_first = response_remove_first.replace("*", "")
revised_response_remove_last = response_remove_last.replace("*", "")
revised_response_remove_both = response_remove_both.replace("*", "")
all_responses = [
response,
revised_response,
response_remove_first,
response_remove_last,
response_remove_both,
revised_response_remove_first,
revised_response_remove_last,
revised_response_remove_both,
]
instruction_list = inp.instruction_id_list
is_following_list = []
for index, instruction_id in enumerate(instruction_list):
instruction_cls = instructions_registry.INSTRUCTION_DICT[instruction_id]
instruction = instruction_cls(instruction_id)
# Remove None values from kwargs to avoid unexpected keyword argument errors in build_description method.
kwargs = {k: v for k, v in inp.kwargs[index].items() if v}
instruction.build_description(**kwargs)
args = instruction.get_instruction_args()
if args and "prompt" in args:
instruction.build_description(prompt=inp.prompt)
is_following = False
for r in all_responses:
if r.strip() and instruction.check_following(r):
is_following = True
break
is_following_list.append(is_following)
return OutputExample(
instruction_id_list=inp.instruction_id_list,
prompt=inp.prompt,
response=response,
follow_all_instructions=all(is_following_list),
follow_instruction_list=is_following_list,
)
def process_results(doc, results):
eval_logger.warning(
"This task is meant for chat-finetuned models, and may not give meaningful results for models other than `openai` or `anthropic` if `doc_to_text` in its YAML is not wrapped in the appropriate chat template string. This warning will be removed when chat templating support is added natively to local models"
)
inp = InputExample(
key=doc["key"],
instruction_id_list=doc["instruction_id_list"],
prompt=doc["prompt"],
kwargs=doc["kwargs"],
)
response = results[0]
out_strict = test_instruction_following_strict(inp, response)
out_loose = test_instruction_following_loose(inp, response)
return {
"prompt_level_strict_acc": out_strict.follow_all_instructions,
"inst_level_strict_acc": out_strict.follow_instruction_list,
"prompt_level_loose_acc": out_loose.follow_all_instructions,
"inst_level_loose_acc": out_loose.follow_instruction_list,
}
def agg_inst_level_acc(items):
flat_items = [item for sublist in items for item in sublist]
inst_level_acc = sum(flat_items) / len(flat_items)
return inst_level_acc
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