Daniil

contest/extract.py

@@ -1,4 +1,5 @@
 import json
+from tqdm import tqdm
 from transformers import AutoTokenizer
 from dataclasses import asdict
 
@@ -8,7 +9,7 @@
 
 with open("Meta-Llama-3.1-70B-Instruct-Turbo.json", "r") as f:
     data = json.load(f)
-    for _id, row in data.items():
+    for _id, row in tqdm(data.items()):
         claims = extract_and_align_claims(
             text=row["output"],
             tokens=row["greedy_tokens"],

src/reclaim/__init__.py

@@ -1,10 +1,15 @@
 """ReClaim core package."""
 
 from importlib import metadata
-from typing import List
+from typing import List, Optional
 
 from .decompose import doc2sentences
-from .extract_claims import Claim, ClaimModel, ClaimsExtractor
+from .extract_claims import (
+    Claim,
+    ClaimModel,
+    ClaimPostprocessingConfig,
+    ClaimsExtractor,
+)
 from .annotate_claims import ClaimsAnnotator
 from .openai_client import OpenAIChat
 
@@ -20,15 +25,55 @@
     "batch_extract_and_align_claims",
     "doc2sentences",
     "Claim",
+    "ClaimPostprocessingConfig",
 ]
 
 
-def extract_claims(text: str, model: str = "gpt-4o") -> List[Claim]:
+def _default_postprocess_config(
+    override: Optional[ClaimPostprocessingConfig],
+    enable_defaults: bool,
+) -> Optional[ClaimPostprocessingConfig]:
+    """
+    Resolve a postprocessing config:
+    - honor explicit override;
+    - if allowed, provide a sensible default bundle;
+    - otherwise, return None to disable all extras.
+    """
+    if override is not None:
+        return override
+    if not enable_defaults:
+        return None
+    return ClaimPostprocessingConfig(
+        rewrite_pronouns=True,
+        sanitize_with_llm=True,
+        split_non_atomic=True,
+        dedupe_with_encoder=True,
+        dedupe_with_cosine=True,
+    )
+
+
+def extract_claims(
+    text: str,
+    model: str = "gpt-4.1",
+    postprocess_config: Optional[ClaimPostprocessingConfig] = None,
+    enable_default_postprocessing: bool = True,
+) -> List[Claim]:
     """
     Extract atomic claims from plain text.
+
+    By default, enables post-processing (pronoun rewrite, LLM sanitization,
+    encoder + BoW dedupe). Pass a custom ClaimPostprocessingConfig or set
+    enable_default_postprocessing=False to skip these steps.
     """
     result = doc2sentences(doc=text, mode="atomic_claims", model=model, schema=ClaimModel)
     claim_texts = result.claims if isinstance(result, ClaimModel) else result
+    chat = OpenAIChat(openai_model=model)
+    config = _default_postprocess_config(postprocess_config, enable_default_postprocessing)
+    extractor = ClaimsExtractor(
+        openai_chat=chat,
+        postprocess_config=config,
+    )
+    claim_texts = extractor.postprocess_claims(claim_texts, text)
     return [
         Claim(
             claim_text=claim_text,
@@ -44,17 +89,25 @@ def extract_and_align_claims(
     text,
     tokens,
     tokenizer,
-    openai_model: str = "gpt-4o",
+    openai_model: str = "gpt-4.1",
     progress_bar: bool = True,
     n_threads: int = 1,
+    postprocess_config: Optional[ClaimPostprocessingConfig] = None,
+    enable_default_postprocessing: bool = True,
 ):
     """
     Extract and align claims with token-level provenance from model output tokens.
+
+    By default, enables post-processing (pronoun rewrite, LLM sanitization,
+    encoder + BoW dedupe). Pass a custom ClaimPostprocessingConfig or set
+    enable_default_postprocessing=False to skip these steps.
     """
+    config = _default_postprocess_config(postprocess_config, enable_default_postprocessing)
     extractor = ClaimsExtractor(
         openai_chat=OpenAIChat(openai_model=openai_model),
         progress_bar=progress_bar,
         n_threads=n_threads,
+        postprocess_config=config,
     )
     return extractor.claims_from_text(text, tokens, tokenizer)
 
@@ -63,17 +116,25 @@ def batch_extract_and_align_claims(
     texts: List[str],
     tokens: List[List[int]],
     tokenizer,
-    openai_model: str = "gpt-4o",
+    openai_model: str = "gpt-4.1",
     progress_bar: bool = True,
     n_threads: int = 1,
+    postprocess_config: Optional[ClaimPostprocessingConfig] = None,
+    enable_default_postprocessing: bool = True,
 ) -> List[List[Claim]]:
     """
     Batch extract and align claims with token-level provenance from model output tokens.
+
+    By default, enables post-processing (pronoun rewrite, LLM sanitization,
+    encoder + BoW dedupe). Pass a custom ClaimPostprocessingConfig or set
+    enable_default_postprocessing=False to skip these steps.
     """
+    config = _default_postprocess_config(postprocess_config, enable_default_postprocessing)
     extractor = ClaimsExtractor(
         openai_chat=OpenAIChat(openai_model=openai_model),
         progress_bar=progress_bar,
         n_threads=n_threads,
+        postprocess_config=config,
     )
 
     return extractor.batch_claims_from_texts(texts, tokens, tokenizer)
@@ -82,7 +143,7 @@ def batch_extract_and_align_claims(
 def annotate_claims(
     claims: List[str],
     contexts: List[str],
-    openai_model: str = "gpt-4o",
+    openai_model: str = "gpt-4.1",
     progress_bar: bool = True,
     n_threads: int = 1,
 ):

src/reclaim/claim_level_prompts.py

@@ -2,7 +2,13 @@
 
 # Lightweight prompt dictionaries keyed by language to mirror original structure.
 CLAIM_EXTRACTION_PROMPTS = {
-    "en": "List all atomic claims from the following sentence. Return each claim on a new line starting with '- '. Sentence: {sent}",
+    "en": (
+        "List all atomic, decontextualized claims from the following sentence.\n"
+        "- One fact per claim (no conjunctions/enumerations).\n"
+        "- Replace pronouns or vague references with the specific entity so the claim stands alone.\n"
+        "Return JSON exactly as {\"claims\": [\"...\"]} and nothing else.\n"
+        "Sentence: {sent}"
+    ),
 }
 
 MATCHING_PROMPTS = {

src/reclaim/decompose.py

@@ -14,7 +14,7 @@
 def doc2sentences(
     doc: str,
     mode: str = "independent_sentences",
-    model: str = "gpt-4o",
+    model: str = "gpt-4.1",
     system_role: str = "You are good at decomposing and decontextualizing text.",
     num_retries: int = 5,
     schema: Optional[BaseModel] = None,