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Ruochun Jin

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6 papers
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6

NeurIPS Conference 2025 Conference Paper

Elastic Robust Unlearning of Specific Knowledge in Large Language Models

  • Yize Sui
  • Jing Ren
  • Wenjing Yang
  • Ruochun Jin
  • Liyang Xu
  • Xiyao Liu
  • J Wang

LLM unlearning aims to remove sensitive or harmful information within the model, thus reducing the potential risk of generating unexpected information. However, existing Preference Optimization (PO)-based unlearning methods suffer two limitations. First, their rigid reward setting limits the effect of unlearning. Second, the lack of robustness causes unlearned information to reappear. To remedy these two weaknesses, we present a novel LLM unlearning optimization framework, namely Elastic Robust Unlearning (ERU), to efficiently and robustly remove specific knowledge from LLMs. We design the elastic reward setting instead of the rigid reward setting to enhance the unlearning performance. Meanwhile, we incorporate the refusal feature ablation into the unlearning process to trigger specific failure patterns for efficiently enhancing the robustness of the PO-based unlearning methods in multiple scenarios. Experimental results show that ERU can improve the unlearning effectiveness significantly while maintaining a high utility performance. Especially, on the WMDP-Bio benchmark, ERU shows a 9\% improvement over the second-best method, and maintains 83\% performance even under 1, 000 sample fine-tuned retraining attacks, significantly better than the baseline method.

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UAI Conference 2025 Conference Paper

Enhancing Uncertainty Quantification in Large Language Models through Semantic Graph Density

  • Zhaoye Li
  • Siyuan Shen
  • Wenjing Yang 0002
  • Ruochun Jin
  • Huan Chen
  • Ligong Cao
  • Jing Ren

Large Language Models (LLMs) excel in language understanding but are susceptible to "confabulation, " where they generate arbitrary, factually incorrect responses to uncertain questions. Detecting confabulation in question answering often relies on Uncertainty Quantification (UQ), which measures semantic entropy or consistency among sampled answers. While several methods have been proposed for UQ in LLMs, they suffer from key limitations, such as overlooking fine-grained semantic relationships among answers and neglecting answer probabilities. To address these issues, we propose Semantic Graph Density (SGD). SGD quantifies semantic consistency by evaluating the density of a semantic graph that captures fine-grained semantic relationships among answers. Additionally, it integrates answer probabilities to adjust the contribution of each edge to the overall uncertainty score. We theoretically prove that SGD generalizes the previous state-of-the-art method, Deg, and empirically demonstrate its superior performance across four LLMs and four free-form question-answering datasets. In particular, in experiments with Llama3. 1-8B, SGD outperformed the best baseline by 1. 52% in AUROC on the CoQA dataset and by 1. 22% in AUARC on the TriviaQA dataset.

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ICRA Conference 2025 Conference Paper

FutureNet-LoF: Joint Trajectory Prediction and Lane Occupancy Field Prediction with Future Context Encoding

  • Mingkun Wang
  • Xiaoguang Ren
  • Ruochun Jin
  • Minglong Li
  • Xiaochuan Zhang
  • Changqian Yu
  • Mingxu Wang
  • Wenjing Yang 0002

Most prior motion prediction endeavors in autonomous driving have inadequately encoded future scenarios, leading to predictions that may fail to accurately capture the diverse movements of agents (e. g. , vehicles or pedestrians). To address this, we propose FutureNet, which explicitly integrates initially predicted trajectories into the future scenario and further encodes these future contexts to enhance subsequent forecasting. Additionally, most previous motion forecasting works have focused on predicting independent futures for each agent. However, safe and smooth autonomous driving requires accurately predicting the diverse future behaviors of numerous surrounding agents jointly in complex dynamic environments. Given that all agents occupy certain potential travel spaces and possess lane driving priority, we propose Lane Occupancy Field (LOF), a new representation with lane semantics for motion forecasting in autonomous driving. LOF can simultaneously capture the joint probability distribution of all road participants' future spatial-temporal positions. Due to the high compatibility between lane occupancy field prediction and trajectory prediction, we propose a novel network for joint prediction of these two tasks. Our approach ranks 1st on two large-scale motion forecasting benchmarks: Argoverse 1 and Argoverse 2, while it is also the champion method of the CVPR 2024 Argoverse 2 motion forecasting challenge.

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ICRA Conference 2023 Conference Paper

GANet: Goal Area Network for Motion Forecasting

  • Mingkun Wang
  • Xinge Zhu
  • Changqian Yu
  • Wei Li 0111
  • Yuexin Ma
  • Ruochun Jin
  • Xiaoguang Ren
  • Dongchun Ren

Predicting the future motion of road participants is crucial for autonomous driving but is extremely challenging due to staggering motion uncertainty. Recently, most motion forecasting methods resort to the goal-based strategy, i. e. , predicting endpoints of motion trajectories as conditions to regress the entire trajectories, so that the search space of solution can be reduced. However, accurate goal coordinates are hard to predict and evaluate. In addition, the point representation of the destination limits the utilization of a rich road context, leading to inaccurate prediction results in many cases. Goal area, i. e. , the possible destination area, rather than goal coordinate, could provide a more soft constraint for searching potential trajectories by involving more tolerance and guidance. In view of this, we propose a new goal area-based framework, named Goal Area Network (GANet), for motion forecasting, which models goal areas as preconditions for trajectory prediction, performing more robustly and accurately. Specifically, we propose a GoICrop (Goal Area of Interest) operator to effectively aggregate semantic lane features in goal areas and model actors' future interactions as feedback, which benefits a lot for future trajectory estimations. GANet ranks the 1st on the leaderboard of Argoverse Challenge among all public literature (till the paper submission). Code will be available at https://github.com/kingwmk/GANet.

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ICRA Conference 2023 Conference Paper

Memory-based Exploration-value Evaluation Model for Visual Navigation

  • Yongquan Feng
  • Liyang Xu
  • Minglong Li
  • Ruochun Jin
  • Da Huang
  • Shaowu Yang
  • Wenjing Yang 0002

We propose a hierarchical visual navigation solution, called Memory-based Exploration-value Evaluation Model (MEEM), to improve the agent's navigation performance. MEEM employs a hierarchical policy to tackle the challenge of sparse rewards, holds an episodic memory to store the historical information of the agent, and applies an Exploration-value Evaluation Model to calculate an exploration-value for action planning at each location in the observable area. We experimentally verify MEEM by navigation performance comparison on two datasets including the grid-map dataset and the 3D scenes Gibson dataset, where our approach achieves state-of-the-art performance on both. Specifically, the overall success rate of MEEM is 95% on the grid-map dataset while the best competitor reaches 68% only. As for the Gibson dataset, the success rate of ours and the best competitor SemExp are 69. 8% and 54. 4%, respectively. Ablation analysis on the tile-map dataset indicates that all three components of MEEM have positive effects.

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IJCAI Conference 2017 Conference Paper

Confusion Graph: Detecting Confusion Communities in Large Scale Image Classification

  • Ruochun Jin
  • Yong Dou
  • Yueqing Wang
  • Xin Niu

For deep CNN-based image classification models, we observe that confusions between classes with high visual similarity are much stronger than those where classes are visually dissimilar. With these unbalanced confusions, classes can be organized in communities, which is similar to cliques of people in the social network. Based on this, we propose a graph-based tool named "confusion graph" to quantify these confusions and further reveal the community structure inside the database. With this community structure, we can diagnose the model's weaknesses and improve the classification accuracy using specialized expert sub-nets, which is comparable to other state-of-the-art techniques. Utilizing this community information, we can also employ pre-trained models to automatically identify mislabeled images in the large scale database. With our method, researchers just need to manually check approximate 3% of the ILSVRC2012 classification database to locate almost all mislabeled samples.

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