TypedCSIP: Typed Counterfactual Pretraining for Chinese Legislative Conflict Classification

• URL: http://arxiv.org/abs/2605.25474v1
• Date: Mon, 25 May 2026 06:26:46 GMT
• Title: TypedCSIP: Typed Counterfactual Pretraining for Chinese Legislative Conflict Classification
• Abstract summary: TypedCSIP is a typed counterfactual pretraining method for the conflict-classification task of the LCR-CN benchmark.<n>We exploit LCR-CN's expert-written minimal revisions as training-time counterfactual supervision.
• Score: 3.5137191090796054
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: TypedCSIP is a typed counterfactual pretraining method for the conflict-classification task of the LCR-CN benchmark (Zhao et al., 2026): given a (superior, subordinate) provision pair, predict whether the pair conflicts and which of four legal-doctrine types (Responsibility, Condition, Sanction, Definition) describes the inconsistency. We exploit LCR-CN's expert-written minimal revisions as training-time counterfactual supervision; at test time the classifier reads only the original pair. Stage 1 pretrains a shared encoder with a typed Counterfactual Selective Intervention Pretraining objective on (superior, subordinate, expert-revised) triplets, treating the expert revision as a counterfactual that the typed factor head must classify as carrying no conflict evidence. Stage 2 transfers the encoder to a five-way classification head. The confirmatory test was registered on the Open Science Framework before observing v6 measurements: 18 seeds, locked rule requiring mean per-seed difference at least 0.8 pp with both seed-bootstrap and Student-t 95% lower bounds above zero. On the 696-record test split, the v2 variant improves macro-F1 over the strongest single-model baseline by +0.916 pp on chinese-roberta-wwm-ext and +1.288 pp on the SAILER cross-backbone replication; both cells pass the rule. A cold-start stratified result on the 244 Unseen-gB records keeps the gain positive on both backbones. A cross-task diagnostic shows the Stage-2 encoder is classification-specialized and does not transfer to LCR-CN's superior-law retrieval task, so we scope the contribution to conflict classification. We release code, 72 pre-registered prediction files, matched-seed and MLM-control auxiliaries, and the OSF pre-registration record.

Related papers

• The Order Is the Guarantee: Verifier-Budgeted Code Deletion with Static-First Learned Proposals [12.095189927690988]
  We study the inverse problem-how an Al system should remove code when execution-verification capacity is finite.<n>We formulate redundant-code reduction as proposal scheduling.<n>We show that scheduling governs search while the test suite alone governs what "preserving behavior" means.
  arXiv  Detail & Related papers  (2026-08-05T09:16:29Z)
• Steering Instruction Hierarchies at Inference Time [12.819290639427445]
  We introduce V-Steer, a training-free inference time method that restores privileged influence by editing cached value vectors at prompt positions.<n>V-Steer identifies heads where lower priority spans dominate privileged ones, then boosts privileged spans and suppresses conflicting lower priority spans.<n>Across models from 7B to 70B, this intervention raises primary constraint accuracy from under 18% up to 92% on controlled role conflict benchmarks.
  arXiv  Detail & Related papers  (2026-07-28T20:06:31Z)
• Compliance-Scored Best-of-N Guardrail Orchestration for Multimodal Document Generation in Payments Dispute Defense [0.0]
  High-stakes enterprise document generation includes financial dispute narratives, compliance notices, and audit summaries.<n>Prior to a unified guardrail layer, production systems often stitched together separate PII redaction, content moderation, and format validation steps.<n>We present a guardrail orchestration layer for text and image inputs that couples multi-candidate generation with an explicit compliance score used for early exit.
  arXiv  Detail & Related papers  (2026-06-01T00:24:30Z)
• ClaHF: A Human Feedback-inspired Reinforcement Learning Framework for Improving Classification Tasks [11.138973570127206]
  We propose ClaHF, a human feedback-inspired reinforcement learning framework for text classification.<n>ClaHF integrates preference modeling and RL optimization into the classification pipeline without requiring additional human annotations.<n>Results demonstrate that ClaHF consistently improves both classification performance and confidence calibration across diverse language models.
  arXiv  Detail & Related papers  (2026-05-17T14:00:01Z)
• Reinforcement Learning with Verifiable yet Noisy Rewards under Imperfect Verifiers [90.50039419576807]
  Reinforcement Learning with Verifiable Rewards (RLVR) trains policies against automated verifiers to avoid costly human labeling.<n>To reduce vulnerability to verifier hacking, many RLVR systems collapse rewards to binary $0,1$ during training.<n>This choice carries a cost: it introduces textitfalse negatives (rejecting correct answers, FNs) and textitfalse positives (accepting incorrect ones, FPs)
  arXiv  Detail & Related papers  (2025-10-01T13:56:44Z)
• Dual-Head Reasoning Distillation: Improving Classifier Accuracy with Train-Time-Only Reasoning [4.1343183246697475]
  We introduce Dual-Head Reasoning Distillation (DHRD), a simple training method for decoder-only language models (LMs)<n>On seven SuperGLUE tasks, DHRD yields relative gains of 0.65-5.47% over pooled baselines, with notably larger gains on entailment/causal tasks.<n>Since we disable the reasoning head at test time, inference throughput matches pooled classifiers and exceeds CoT decoding on the same backbones by 96-142 times in QPS.
  arXiv  Detail & Related papers  (2025-09-25T19:47:19Z)
• Balanced Classification: A Unified Framework for Long-Tailed Object Detection [74.94216414011326]
  Conventional detectors suffer from performance degradation when dealing with long-tailed data due to a classification bias towards the majority head categories. We introduce a unified framework called BAlanced CLassification (BACL), which enables adaptive rectification of inequalities caused by disparities in category distribution. BACL consistently achieves performance improvements across various datasets with different backbones and architectures.
  arXiv  Detail & Related papers  (2023-08-04T09:11:07Z)
• Semi-DETR: Semi-Supervised Object Detection with Detection Transformers [105.45018934087076]
  We analyze the DETR-based framework on semi-supervised object detection (SSOD) We present Semi-DETR, the first transformer-based end-to-end semi-supervised object detector. Our method outperforms all state-of-the-art methods by clear margins.
  arXiv  Detail & Related papers  (2023-07-16T16:32:14Z)
• When Does Confidence-Based Cascade Deferral Suffice? [69.28314307469381]
  Cascades are a classical strategy to enable inference cost to vary adaptively across samples. A deferral rule determines whether to invoke the next classifier in the sequence, or to terminate prediction. Despite being oblivious to the structure of the cascade, confidence-based deferral often works remarkably well in practice.
  arXiv  Detail & Related papers  (2023-07-06T04:13:57Z)
• Towards Understanding Why Mask-Reconstruction Pretraining Helps in Downstream Tasks [129.1080795985234]
  Mask-reconstruction pretraining (MRP) approaches randomly mask input patches and then reconstruct pixels or semantic features of these masked patches via an auto-encoder. For a downstream task, supervised fine-tuning the pretrained encoder remarkably surpasses the conventional supervised learning (SL) trained from scratch.
  arXiv  Detail & Related papers  (2022-06-08T11:49:26Z)
• Pre-training Is (Almost) All You Need: An Application to Commonsense Reasoning [61.32992639292889]
  Fine-tuning of pre-trained transformer models has become the standard approach for solving common NLP tasks. We introduce a new scoring method that casts a plausibility ranking task in a full-text format. We show that our method provides a much more stable training phase across random restarts.
  arXiv  Detail & Related papers  (2020-04-29T10:54:40Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.