Ning Ding

AAAI Conference 2026 Conference Paper

W2S-AlignTree: Weak-to-Strong Inference-Time Alignment for Large Language Models via Monte Carlo Tree Search

• Zhenyu Ding
• Yuhao Wang
• Tengyue Xiao
• Haoying Wang
• Guojun Ma
• Mingyang Wan
• Caigui Jiang
• Ning Ding

Large Language Models (LLMs) demonstrate impressive capabilities, yet their outputs often suffer from misalignment with human preferences due to the inadequacy of weak supervision and a lack of fine-grained control. Training-time alignment methods like Reinforcement Learning from Human Feedback (RLHF) face prohibitive costs in expert supervision and inherent scalability limitations, offering limited dynamic control during inference. Consequently, there is an urgent need for scalable and adaptable alignment mechanisms. To address this, we propose W2S-AlignTree, a pioneering plug-and-play inference-time alignment framework that synergistically combines Monte Carlo Tree Search (MCTS) with the Weak-to-Strong Generalization paradigm for the first time. W2S-AlignTree formulates LLM alignment as an optimal heuristic search problem within a generative search tree. By leveraging weak model's real-time, step-level signals as alignment proxies and introducing an Entropy-Aware exploration mechanism, W2S-AlignTree enables fine-grained guidance during strong model's generation without modifying its parameters. The approach dynamically balances exploration and exploitation in high-dimensional generation search trees. Experiments across controlled sentiment generation, summarization, and instruction-following show that W2S-AlignTree consistently outperforms strong baselines. Notably, W2S-AlignTree raises the performance of Llama3-8B from 1.89 to 2.19, a relative improvement of 15.9% on the summarization task.

EAAI Journal 2025 Journal Article

A module partition method for complex product based on the knowledge hypergraph

• Pengchao Wang
• Jianjie Chu
• Suihuai Yu
• Fangmin Cheng
• Ning Ding
• Yangfan Cong

NeurIPS Conference 2025 Conference Paper

DePass: Unified Feature Attributing by Simple Decomposed Forward Pass

• Xiangyu Hong
• Che Jiang
• Kai Tian
• Biqing Qi
• Youbang Sun
• Ning Ding
• Bowen Zhou

Attributing the behavior of Transformer models to internal computations is a central challenge in mechanistic interpretability. We introduce DePass, a unified framework for feature attribution based on a single decomposed forward pass. DePass decomposes hidden states into customized additive components, then propagates them with attention scores and MLP's activations fixed. It achieves faithful, fine-grained attribution without requiring auxiliary training. We validate DePass across token-level, model component-level, and subspace-level attribution tasks, demonstrating its effectiveness and fidelity. Our experiments highlight its potential to attribute information flow between arbitrary components of a Transformer model. We hope DePass serves as a foundational tool for broader applications in interpretability.

JBHI Journal 2025 Journal Article

Knowledge Guided Articulatory and Spectrum Information Fusion for Obstructive Sleep Apnea Severity Estimation

• Biao Xue
• Zhichao Wang
• Yanting Shao
• Xiaohua Zhu
• Heng Zhao
• Chang–Hong Fu
• Jing Xu
• Ning Ding

Numerous studies have demonstrated that speech analysis during wakefulness is a non-invasive and convenient method for Obstructive sleep apnea (OSA) screening. However, the inherent differences in upper airway structure and function between wakefulness and sleep limit the effectiveness of OSA assessments based on vowels and phonemes employed in existing studies. To address this challenge, we propose the design of controlled articulations that more accurately simulate upper airway obstruction during sleep, offering a more comprehensive reflection of the pathological changes in upper airway anatomy and function in individuals with suspected OSA. Specifically, we constructed a Mandarin Chinese controlled articulation dataset, consisting of speech recordings from 301 male adult participants who underwent polysomnography (PSG) monitoring at a sleep center. Drawing on domain knowledge, we thoroughly investigated articulations associated with upper airway collapse, including vowels, pharyngeals, and nasals, and identified interpretable optimal articulations using SHapley Additive Explanations (SHAP). Furthermore, we introduced a dual-stream fusion model, PTF-Net, which employs the Paralinguistic Acoustic Feature stream (PAF-Stream) to extract the physical attributes of speech and the Transfer Learning-based Spectrogram Feature stream (TLE-Stream) to capture the nonlinear features of upper airway dynamics. The Swin Transformer is utilized to integrate both local and global information from various articulations. Experimental results demonstrate that the knowledge-guided PTF-Net model outperforms existing methods in the assessment of OSA severity. The knowledge-guided PTF-Net model outperforms existing methods by 5. 1% in Area Under the Curve (AUC) and 5. 8% in Unweighted Average Recall (UAR) for OSA severity assessment. In addition, we revealed that the proposed deep embedding of controlled articulation could differentiate between different types of obstruction sites identified by drug-induced sleep endoscopy (DISE), suggesting its potential as a novel digital biomarker for upper airway assessment in OSA patients. This study enhances the understanding of speech-based OSA screening and paves the way for its broad clinical application.

NeurIPS Conference 2025 Conference Paper

Learning to Focus: Causal Attention Distillation via Gradient‐Guided Token Pruning

• Yiju Guo
• Wenkai Yang
• Zexu Sun
• Ning Ding
• Zhiyuan Liu
• Yankai Lin

Large language models (LLMs) have demonstrated significant improvements in contextual understanding. However, their ability to attend to truly critical information during long-context reasoning and generation still falls behind the pace. Specifically, our preliminary experiments reveal that certain distracting patterns can misdirect the model’s attention during inference, and removing these patterns substantially improves reasoning accuracy and generation quality. We attribute this phenomenon to spurious correlations in the training data, which obstruct the model’s capacity to infer authentic causal instruction–response relationships. This phenomenon may induce redundant reasoning processes, potentially resulting in significant inference overhead and, more critically, the generation of erroneous or suboptimal responses. To mitigate this, we introduce a two-stage framework called Learning to Focus (LeaF) leveraging intervention-based inference to disentangle confounding factors. In the first stage, LeaF employs gradient-based comparisons with an advanced teacher to automatically identify confounding tokens based on causal relationships in the training corpus. Then, in the second stage, it prunes these tokens during distillation to enact intervention, aligning the student’s attention with the teacher’s focus distribution on truly critical context tokens. Experimental results demonstrate that LeaF not only achieves an absolute improvement in various mathematical reasoning, code generation and multi-hop question answering benchmarks but also effectively suppresses attention to confounding tokens during inference, yielding a more interpretable and reliable reasoning model.

NeurIPS Conference 2025 Conference Paper

Predictable Scale (Part II) --- Farseer: A Refined Scaling Law in LLMs

• Houyi Li
• Wenzhen Zheng
• Qiufeng Wang
• Zhenyu Ding
• Haoying Wang
• Zili Wang
• Shijie Xuyang
• Ning Ding

Training Large Language Models (LLMs) is prohibitively expensive, creating a critical scaling gap where insights from small-scale experiments often fail to transfer to resource-intensive production systems, thereby hindering efficient innovation. To bridge this, we introduce Farseer, a novel and refined scaling law offering enhanced predictive accuracy across scales. By systematically constructing a model loss surface $L(N, D)$, Farseer achieves a significantly better fit to empirical data than prior laws (e. g. , \Chinchilla's law). Our methodology yields accurate, robust, and highly generalizable predictions, demonstrating excellent extrapolation capabilities, outperforming Chinchilla's law, whose extrapolation error is 433\% higher. This allows for the reliable evaluation of competing training strategies across all $(N, D)$ settings, enabling conclusions from small-scale ablation studies to be confidently extrapolated to predict large-scale performance. Furthermore, Farseer provides new insights into optimal compute allocation, better reflecting the nuanced demands of modern LLM training. To validate our approach, we trained an extensive suite of approximately 1, 000 LLMs across diverse scales and configurations, consuming roughly 3 million NVIDIA H100 GPU hours. To foster further research, we are comprehensively open-sourcing all code, data, results (https: //github. com/Farseer-Scaling-Law/Farseer), all training logs (https: //wandb. ai/billzid/Farseer? nw=nwuserbillzid), all models used in scaling law fitting (https: //huggingface. co/Farseer-Scaling-Law).

NeurIPS Conference 2025 Conference Paper

Scaling Physical Reasoning with the PHYSICS Dataset

• Shenghe Zheng
• Qianjia Cheng
• Junchi Yao
• Mengsong Wu
• Haonan He
• Ning Ding
• Yu Cheng
• Shuyue Hu

Large Language Models (LLMs) have achieved remarkable progress on advanced reasoning tasks such as mathematics and coding competitions. Meanwhile, physics, despite being both reasoning-intensive and essential to real-world understanding, received limited academic and industrial attention. This paper introduces PHYSICS, a dataset containing 16, 568 high-quality physics problems spanning subjects and difficulty levels, to facilitate this issue. Specifically, PHYSICS is curated with exercises from over 100 textbooks through a carefully designed pipeline for quality control. It covers five major physics domains: Mechanics, Electromagnetism, Thermodynamics, Optics, and Modern Physics. It also spans a wide range of difficulty levels, from high school to graduate-level physics courses. To utilize the data for improving and evaluating the model's physical reasoning capabilities, we split the dataset into training and test sets, and provide reasoning paths generated by powerful reasoning models for the training data to facilitate model training. In addition, for the evaluation part, we find that existing evaluation frameworks exhibit biases in aspects such as units, simplification, and precision in physics domain. To balance efficiency and accuracy, we introduce a Rule+Model evaluation framework tailored to physics problems. Our evaluations on current state-of-the-art open-source and proprietary models highlight the limitations of current models in handling physics-related tasks. We hope that our dataset and evaluation methodology will jointly advance the development of LLMs in the field of physics. The code and data can be found at: https: //github. com/Zhengsh123/PHYSICS.

NeurIPS Conference 2025 Conference Paper

The Overthinker's DIET: Cutting Token Calories with DIfficulty-AwarE Training

• Weize Chen
• Jiarui yuan
• Jin Tailin
• Ning Ding
• Huimin Chen
• Zhiyuan Liu
• Maosong Sun

Recent large language models (LLMs) exhibit impressive reasoning but often \textit{overthink}, generating excessively long responses that hinder efficiency. We introduce DIET (DIfficulty-AwarE Training), a framework that systematically cuts these "token calories" by integrating on-the-fly problem difficulty into the reinforcement learning (RL) process. DIET dynamically adapts token compression strategies by modulating token penalty strength and conditioning target lengths on estimated task difficulty, to optimize the performance-efficiency trade-off. We also theoretically analyze the pitfalls of naive reward weighting in group-normalized RL algorithms like GRPO, and propose \textit{Advantage Weighting} technique, which enables stable and effective implementation of these difficulty-aware objectives. Experimental results demonstrate that DIET significantly reduces token counts while simultaneously improving reasoning performance. Beyond raw token reduction, we show two crucial benefits largely overlooked by prior work: (1) DIET leads to superior \textbf{inference scaling}. By maintaining high per-sample quality with fewer tokens, it enables better scaling performance via majority voting under fixed computational budgets, an area where other methods falter. (2) DIET enhances the natural positive correlation between response length and problem difficulty, ensuring verbosity is appropriately allocated, unlike many existing compression methods that disrupt this relationship. Our analyses provide a principled and effective framework for developing more efficient, practical, and high-performing LLMs.

NeurIPS Conference 2025 Conference Paper

Towards Large-Scale In-Context Reinforcement Learning by Meta-Training in Randomized Worlds

• Fan Wang
• Pengtao Shao
• Yiming Zhang
• Bo Yu
• Shaoshan Liu
• Ning Ding
• Yang Cao
• Yu Kang

In-Context Reinforcement Learning (ICRL) enables agents to learn automatically and on-the-fly from their interactive experiences. However, a major challenge in scaling up ICRL is the lack of scalable task collections. To address this, we propose the procedurally generated tabular Markov Decision Processes, named AnyMDP. Through a carefully designed randomization process, AnyMDP is capable of generating high-quality tasks on a large scale while maintaining relatively low structural biases. To facilitate efficient meta-training at scale, we further introduce decoupled policy distillation and induce prior information in the ICRL framework. Our results demonstrate that, with a sufficiently large scale of AnyMDP tasks, the proposed model can generalize to tasks that were not considered in the training set through versatile in-context learning paradigms. The scalable task set provided by AnyMDP also enables a more thorough empirical investigation of the relationship between data distribution and ICRL performance. We further show that the generalization of ICRL potentially comes at the cost of increased task diversity and longer adaptation periods. This finding carries critical implications for scaling robust ICRL capabilities, highlighting the necessity of diverse and extensive task design, and prioritizing asymptotic performance over few-shot adaptation.

NeurIPS Conference 2025 Conference Paper

TTRL: Test-Time Reinforcement Learning

• Yuxin Zuo
• Kaiyan Zhang
• Li Sheng
• Shang Qu
• Ganqu Cui
• Xuekai Zhu
• Haozhan Li
• Yuchen Zhang

This paper investigates Reinforcement Learning (RL) on data without explicit labels for reasoning tasks in Large Language Models (LLMs). The core challenge of the problem is reward estimation during inference while not having access to ground-truth information. While this setting appears elusive, we find that common practices in Test-Time Scaling (TTS), such as majority voting, yield surprisingly effective rewards suitable for driving RL training. In this work, we introduce Test-Time Reinforcement Learning (TTRL), a novel method for training LLMs using RL on unlabeled data. TTRL enables self-evolution of LLMs by utilizing the priors in the pre-trained models. Our experiments demonstrate that TTRL consistently improves performance across a variety of tasks and models. Notably, TTRL boosts the pass@1 performance of Qwen-2. 5-Math-7B by approximately 211% on the AIME 2024 with only unlabeled test data. Furthermore, although TTRL is only supervised by the Maj@N metric, TTRL has demonstrated performance to consistently surpass the upper limit of the initial model, and approach the performance of models trained directly on test data with ground-truth labels. Our experimental findings validate the general effectiveness of TTRL across various tasks and highlight TTRL's potential for broader tasks and domains.

ICRA Conference 2025 Conference Paper

Underwater Motions Analysis and Control of a Coupling-Tiltable Unmanned Aerial-Aquatic Vehicle

• Dongyue Huang
• Minghao Dou
• Xuchen Liu 0001
• Tao Sun
• Jianguo Zhang
• Ning Ding
• Xinlei Chen
• Ben M. Chen

Coupling-Tiltable Unmanned Aerial-Aquatic Vehicles (UAAVs) have gained increasing importance, yet lack comprehensive analysis and suitable controllers. This paper analyzes the underwater motion characteristics of a self-designed UAAV, Mirs-Alioth, and designs a controller for it. The effectiveness of the controller is validated through experiments. The singularities of Mirs-Alioth are derived as Singular Thrust Tilt Angle (STTA), which serve as an essential tool for an analysis of its underwater motion characteristics. The analysis reveals several key factors for designing the controller. These include the need for logic switching, using a Nussbaum function to compensate control direction uncertainty in the auxiliary channel, and employing an auxiliary controller to mitigate coupling effects. Based on these key points, a control scheme is designed. It consists of a controller that regulates the thrust tilt angle to the singular value, an auxiliary controller incorporating a Saturated Nussbaum function, and a logic switch. Eventually, two sets of experiments are conducted to validate the effectiveness of the controller and demonstrate the necessity of the Nussbaum function.

ICML Conference 2024 Conference Paper

Memory-Space Visual Prompting for Efficient Vision-Language Fine-Tuning

• Shibo Jie
• Yehui Tang 0001
• Ning Ding
• Zhi-Hong Deng 0001
• Kai Han 0002
• Yunhe Wang 0001

Current solutions for efficiently constructing large vision-language (VL) models follow a two-step paradigm: projecting the output of pre-trained vision encoders to the input space of pre-trained language models as visual prompts; and then transferring the models to downstream VL tasks via end-to-end parameter-efficient fine-tuning (PEFT). However, this paradigm still exhibits inefficiency since it significantly increases the input length of the language models. In this paper, in contrast to integrating visual prompts into inputs, we regard visual prompts as additional knowledge that facilitates language models in addressing tasks associated with visual information. Motivated by the finding that Feed-Forward Network (FFN) of language models acts as "key-value memory", we introduce a novel approach termed memory-space visual prompting (MemVP), wherein visual prompts are concatenated with the weights of FFN for visual knowledge injection. Experimental results across various VL tasks and language models reveal that MemVP significantly reduces the training time and inference latency of the finetuned VL models and surpasses the performance of previous PEFT methods.

NeurIPS Conference 2024 Conference Paper

MemoryFormer : Minimize Transformer Computation by Removing Fully-Connected Layers

• Ning Ding
• Yehui Tang
• Haochen Qin
• Zhenli Zhou
• Chao Xu
• Lin Li
• Kai Han
• Heng Liao

In order to reduce the computational complexity of large language models, great efforts have been made to to improve the efficiency of transformer models such as linear attention and flash-attention. However, the model size and corresponding computational complexity are constantly scaled up in pursuit of higher performance. In this work, we present MemoryFormer, a novel transformer architecture which significantly reduces the computational complexity (FLOPs) from a new perspective. We eliminate nearly all the computations of the transformer model except for the necessary computation required by the multi-head attention operation. This is made possible by utilizing an alternative method for feature transformation to replace the linear projection of fully-connected layers. Specifically, we first construct a group of in-memory lookup tables that store a large amount of discrete vectors to replace the weight matrix used in linear projection. We then use a hash algorithm to retrieve a correlated subset of vectors dynamically based on the input embedding. The retrieved vectors combined together will form the output embedding, which provides an estimation of the result of matrix multiplication operation in a fully-connected layer. Compared to conducting matrix multiplication, retrieving data blocks from memory is a much cheaper operation which requires little computations. We train MemoryFormer from scratch and conduct extensive experiments on various benchmarks to demonstrate the effectiveness of the proposed model.

NeurIPS Conference 2024 Conference Paper

UltraMedical: Building Specialized Generalists in Biomedicine

• Kaiyan Zhang
• Sihang Zeng
• Ermo Hua
• Ning Ding
• Zhang-Ren Chen
• Zhiyuan Ma
• Haoxin Li
• Ganqu Cui

Large Language Models (LLMs) have demonstrated remarkable capabilities across various domains and are moving towards more specialized areas. Recent advanced proprietary models such as GPT-4 and Gemini have achieved significant advancements in biomedicine, which have also raised privacy and security challenges. The construction of specialized generalists hinges largely on high-quality datasets, enhanced by techniques like supervised fine-tuning and reinforcement learning from human or AI feedback, and direct preference optimization. However, these leading technologies (e. g. , preference learning) are still significantly limited in the open source community due to the scarcity of specialized data. In this paper, we present the UltraMedical collections, which consist of high-quality manual and synthetic datasets in the biomedicine domain, featuring preference annotations across multiple advanced LLMs. By utilizing these datasets, we fine-tune a suite of specialized medical models based on Llama-3 series, demonstrating breathtaking capabilities across various medical benchmarks. Moreover, we develop powerful reward models skilled in biomedical and general reward benchmark, enhancing further online preference learning within the biomedical LLM community.

NeurIPS Conference 2022 Conference Paper

Sparse Structure Search for Delta Tuning

• Shengding Hu
• Zhen Zhang
• Ning Ding
• Yadao Wang
• Yasheng Wang
• Zhiyuan Liu
• Maosong Sun

Adapting large pre-trained models (PTMs) through fine-tuning imposes prohibitive computational and storage burdens. Recent studies of delta tuning (DT), i. e. , parameter-efficient tuning, find that only optimizing a small portion of parameters conditioned on PTMs could yield on-par performance compared to conventional fine-tuning. Generally, DT methods exquisitely design delta modules (DT modules) which could be applied to arbitrary fine-grained positions inside PTMs. However, the effectiveness of these fine-grained positions largely relies on sophisticated manual designation, thereby usually producing sub-optimal results. In contrast to the manual designation, we explore constructing DT modules in an automatic manner. We automatically \textbf{S}earch for the \textbf{S}parse \textbf{S}tructure of \textbf{Delta} Tuning (S$^3$Delta). Based on a unified framework of various DT methods, S$^3$Delta conducts the differentiable DT structure search through bi-level optimization and proposes shifted global sigmoid method to explicitly control the number of trainable parameters. Extensive experiments show that S$^3$Delta surpasses manual and random structures with less trainable parameters. The searched structures preserve more than 99\% fine-tuning performance with 0. 01\% trainable parameters. Moreover, the advantage of S$^3$Delta is amplified with extremely low trainable parameters budgets (0. 0009\%$\sim$0. 01\%). The searched structures are transferable and explainable, providing suggestions and guidance for the future design of DT methods. Our codes are publicly available at \url{https: //github. com/thunlp/S3Delta}.

IJCAI Conference 2020 Conference Paper

Infobox-to-text Generation with Tree-like Planning based Attention Network

• Yang Bai
• Ziran Li
• Ning Ding
• Ying Shen
• Hai-Tao Zheng

We study the problem of infobox-to-text generation that aims to generate a textual description from a key-value table. Representing the input infobox as a sequence, previous neural methods using end-to-end models without order-planning suffer from the problems of incoherence and inadaptability to disordered input. Recent planning-based models only implement static order-planning to guide the generation, which may cause error propagation between planning and generation. To address these issues, we propose a Tree-like PLanning based Attention Network (Tree-PLAN) which leverages both static order-planning and dynamic tuning to guide the generation. A novel tree-like tuning encoder is designed to dynamically tune the static order-plan for better planning by merging the most relevant attributes together layer by layer. Experiments conducted on two datasets show that our model outperforms previous methods on both automatic and human evaluation, and demonstrate that our model has better adaptability to disordered input.

AAAI Conference 2020 Conference Paper

Integrating Linguistic Knowledge to Sentence Paraphrase Generation

• Zibo Lin
• Ziran Li
• Ning Ding
• Hai-Tao Zheng
• Ying Shen
• Wei Wang
• Cong-Zhi Zhao

Paraphrase generation aims to rewrite a text with different words while keeping the same meaning. Previous work performs the task based solely on the given dataset while ignoring the availability of external linguistic knowledge. However, it is intuitive that a model can generate more expressive and diverse paraphrase with the help of such knowledge. To fill this gap, we propose Knowledge-Enhanced Paraphrase Network (KEPN), a transformer-based framework that can leverage external linguistic knowledge to facilitate paraphrase generation. (1) The model integrates synonym information from the external linguistic knowledge into the paraphrase generator, which is used to guide the decision on whether to generate a new word or replace it with a synonym. (2) To locate the synonym pairs more accurately, we adopt an incremental encoding scheme to incorporate position information of each synonym. Besides, a multi-task architecture is designed to help the framework jointly learn the selection of synonym pairs and the generation of expressive paraphrase. Experimental results on both English and Chinese datasets show that our method significantly outperforms the state-ofthe-art approaches in terms of both automatic and human evaluation.

IJCAI Conference 2020 Conference Paper

Triple-to-Text Generation with an Anchor-to-Prototype Framework

• Ziran Li
• Zibo Lin
• Ning Ding
• Hai-Tao Zheng
• Ying Shen

Generating a textual description from a set of RDF triplets is a challenging task in natural language generation. Recent neural methods have become the mainstream for this task, which often generate sentences from scratch. However, due to the huge gap between the structured input and the unstructured output, the input triples alone are insufficient to decide an expressive and specific description. In this paper, we propose a novel anchor-to-prototype framework to bridge the gap between structured RDF triples and natural text. The model retrieves a set of prototype descriptions from the training data and extracts writing patterns from them to guide the generation process. Furthermore, to make a more precise use of the retrieved prototypes, we employ a triple anchor that aligns the input triples into groups so as to better match the prototypes. Experimental results on both English and Chinese datasets show that our method significantly outperforms the state-of-the-art baselines in terms of both automatic and manual evaluation, demonstrating the benefit of learning guidance from retrieved prototypes to facilitate triple-to-text generation.

AAAI Conference 2014 Conference Paper

On Computing Optimal Strategies in Open List Proportional Representation: The Two Parties Case

• Ning Ding
• Fangzhen Lin

Open list proportional representation is an election mechanism used in many elections, including the 2012 Hong Kong Legislative Council Geographical Constituencies election. In this paper, we assume that there are just two parties in the election, and that the number of votes that a list would get is the sum of the numbers of votes that the candidates in the list would get if each of them would go alone in the election. Under these assumptions, we formulate the election as a mostly zero-sum game, and show that while the game always has a pure Nash equilibrium, it is NP-hard to compute it.

AAMAS Conference 2013 Conference Paper

Voting with Partial Information: What Questions to Ask?

• Ning Ding
• Fangzhen Lin

Voting is a way to aggregate individual voters’ preferences. Traditionally a voter’s preference is represented by a total order on the set of candidates. However, sometimes one may not have complete information about a voter’s preference, and in this case, can only model a voter’s preference as a partial order. Given this framework, there has been work on computing the possible and necessary winners of a (partial) profile. In this paper, we take a step further, look at sets of questions to ask in order to determine the outcome of such a partial profile. Specifically, we call a set of questions a deciding set for a candidate if the outcome of the vote for the candidate is determined no matter how the questions are answered by the voters, and a possible winning (losing) set if there is a way to answer these questions to make the candidate a winner (loser) of the vote. We discuss some interesting properties about these sets of queries, prove some complexity results about them under some well-known voting rules such as plurality and Borda, and consider their application in vote elicitation.