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Jian Shao

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6 papers

1 author row

NeurIPS Conference 2025 Conference Paper

Let LRMs Break Free from Overthinking via Self-Braking Tuning

Haoran Zhao
Yuchen Yan
Yongliang Shen
Haolei Xu
Wenqi Zhang
Kaitao Song
Jian Shao
Weiming Lu

Large reasoning models (LRMs), such as OpenAI o1 and DeepSeek-R1, have significantly enhanced their reasoning capabilities by generating longer chains of thought, demonstrating outstanding performance across a variety of tasks. However, this performance gain comes at the cost of a substantial increase in redundant reasoning during the generation process, leading to high computational overhead and exacerbating the issue of overthinking. Although numerous existing approaches aim to address the problem of overthinking, they often rely on external interventions. In this paper, we propose a novel framework, Self-Braking Tuning (SBT), which tackles overthinking from the perspective of allowing the model to regulate its own reasoning process, thus eliminating the reliance on external control mechanisms. We construct a set of overthinking identification metrics based on standard answers and design a systematic method to detect redundant reasoning. This method accurately identifies unnecessary steps within the reasoning trajectory and generates training signals for learning self-regulation behaviors. Building on this foundation, we develop a complete strategy for constructing data with adaptive reasoning lengths and introduce an innovative braking prompt mechanism that enables the model to naturally learn when to terminate reasoning at an appropriate point. Experiments across mathematical benchmarks (AIME, AMC, MATH500, GSM8K) demonstrate that our method reduces token consumption by up to 60\% while maintaining comparable accuracy to unconstrained models.

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AAAI Conference 2025 Conference Paper

S^3cMath: Spontaneous Step-Level Self-Correction Makes Large Language Models Better Mathematical Reasoners

Yuchen Yan
Jin Jiang
Yang Liu
Yixin Cao
Xin Xu
Mengdi Zhang
Xunliang Cai
Jian Shao

Self-correction is a novel method that can stimulate the potential reasoning abilities of large language models (LLMs). It involves detecting and correcting errors during the inference process when LLMs solve reasoning problems. However, recent works do not regard self-correction as a spontaneous and intrinsic capability of LLMs. Instead, such correction is achieved through post-hoc generation, external knowledge introduction, multi-model collaboration, and similar techniques. In this paper, we propose a series of mathematical LLMs called S^3cMath, which are able to perform Spontaneous Step-level Self-correction for Mathematical reasoning. This capability helps LLMs to recognize whether their ongoing inference tends to contain errors and simultaneously correct these errors to produce a more reliable response. We proposed a method, which employs a step-level sampling approach to construct step-wise self-correction data for achieving such ability. Additionally, we implement a training strategy that uses above constructed data to equip LLMs with spontaneous step-level self-correction capacities. Our data and methods have been demonstrated to be effective across various foundation LLMs, consistently showing significant progress in evaluations on GSM8K, MATH, and other mathematical benchmarks. To the best of our knowledge, we are the first to introduce the spontaneous step-level self-correction ability of LLMs in mathematical reasoning.

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NeurIPS Conference 2023 Conference Paper

Zero-shot Visual Relation Detection via Composite Visual Cues from Large Language Models

Lin Li
Jun Xiao
Guikun Chen
Jian Shao
Yueting Zhuang
Long Chen

Pretrained vision-language models, such as CLIP, have demonstrated strong generalization capabilities, making them promising tools in the realm of zero-shot visual recognition. Visual relation detection (VRD) is a typical task that identifies relationship (or interaction) types between object pairs within an image. However, naively utilizing CLIP with prevalent class-based prompts for zero-shot VRD has several weaknesses, e. g. , it struggles to distinguish between different fine-grained relation types and it neglects essential spatial information of two objects. To this end, we propose a novel method for zero-shot VRD: RECODE, which solves RElation detection via COmposite DEscription prompts. Specifically, RECODE first decomposes each predicate category into subject, object, and spatial components. Then, it leverages large language models (LLMs) to generate description-based prompts (or visual cues) for each component. Different visual cues enhance the discriminability of similar relation categories from different perspectives, which significantly boosts performance in VRD. To dynamically fuse different cues, we further introduce a chain-of-thought method that prompts LLMs to generate reasonable weights for different visual cues. Extensive experiments on four VRD benchmarks have demonstrated the effectiveness and interpretability of RECODE.

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AAAI Conference 2021 Conference Paper

Boundary Proposal Network for Two-stage Natural Language Video Localization

Shaoning Xiao
Long Chen
Songyang Zhang
Wei Ji
Jian Shao
Lu Ye
Jun Xiao

We aim to address the problem of Natural Language Video Localization (NLVL) — localizing the video segment corresponding to a natural language description in a long and untrimmed video. State-of-the-art NLVL methods are almost in one-stage fashion, which can be typically grouped into two categories: 1) anchor-based approach: it first pre-defines a series of video segment candidates (e. g. , by sliding window), and then does classification for each candidate; 2) anchor-free approach: it directly predicts the probabilities for each video frame1 as a boundary or intermediate frame inside the positive segment. However, both kinds of one-stage approaches have inherent drawbacks: the anchor-based approach is susceptible to the heuristic rules, further limiting the capability of handling videos with variant length. While the anchorfree approach fails to exploit the segment-level interaction thus achieving inferior results. In this paper, we propose a novel Boundary Proposal Network (BPNet), a universal twostage framework that gets rid of the issues mentioned above. Specifically, in the first stage, BPNet utilizes an anchor-free model to generate a group of high-quality candidate video segments with their boundaries. In the second stage, a visuallanguage fusion layer is proposed to jointly model the multimodal interaction between the candidate and the language query, followed by a matching score rating layer that outputs the alignment score for each candidate. We evaluate our BP- Net on three challenging NLVL benchmarks (i. e. , Charades- STA, TACoS and ActivityNet-Captions). Extensive experiments and ablative studies on these datasets demonstrate that the BPNet outperforms the state-of-the-art methods.

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AAAI Conference 2021 Conference Paper

Empower Distantly Supervised Relation Extraction with Collaborative Adversarial Training

Tao Chen
Haochen Shi
Liyuan Liu
Siliang Tang
Jian Shao
Zhigang Chen
Yueting Zhuang

With recent advances in distantly supervised (DS) relation extraction (RE), considerable attention is attracted to leverage multi-instance learning (MIL) to distill high-quality supervision from the noisy DS. Here, we go beyond label noise and identify the key bottleneck of DS-MIL to be its low data utilization: as high-quality supervision being refined by MIL, MIL abandons a large amount of training instances, which leads to a low data utilization and hinders model training from having abundant supervision. In this paper, we propose collaborative adversarial training to improve the data utilization, which coordinates virtual adversarial training (VAT) and adversarial training (AT) at different levels. Specifically, since VAT is label-free, we employ the instance-level VAT to recycle instances abandoned by MIL. Besides, we deploy AT at the bag-level to unleash the full potential of the high-quality supervision got by MIL. Our proposed method brings consistent improvements (∼ 5 absolute AUC score) to the previous state of the art, which verifies the importance of the data utilization issue and the effectiveness of our method.

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IJCAI Conference 2020 Conference Paper

Alleviate Dataset Shift Problem in Fine-grained Entity Typing with Virtual Adversarial Training

Haochen Shi
Siliang Tang
Xiaotao Gu
Bo Chen
Zhigang Chen
Jian Shao
Xiang Ren

The recent success of Distant Supervision (DS) brings abundant labeled data for the task of fine-grained entity typing (FET) without human annotation. However, the heuristically generated labels inevitably bring a significant distribution gap, namely dataset shift, between the distantly labeled training set and the manually curated test set. Considerable efforts have been made to alleviate this problem from the label perspective by either intelligently denoising the training labels, or designing noise-aware loss functions. Despite their progress, the dataset shift can hardly be eliminated completely. In this work, complementary to the label perspective, we reconsider this problem from the model perspective: Can we learn a more robust typing model with the existence of dataset shift? To this end, we propose a novel regularization module based on virtual adversarial training (VAT). The proposed approach first uses a self-paced sample selection function to select suitable samples for VAT, then constructs virtual adversarial perturbations based on the selected samples, and finally regularizes the model to be robust to such perturbations. Experiments on two benchmarks demonstrate the effectiveness of the proposed method, with an average 3. 8%, 2. 5%, and 3. 2% improvement in accuracy, Macro F1 and Micro F1 respectively compared to the next best method.

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