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Works137 from public data
- Text Matching as Image Recognition579
This model, which resembles the compositional hierarchies of patterns in image recognition, can successfully identify salient signals such as n-gram and n-term matchings and demonstrates its superiority against the baselines.
- A Deep Look into neural ranking models for information retrieval395
A deep look into the neural ranking models from different dimensions is taken to analyze their underlying assumptions, major design principles, and learning strategies to obtain a comprehensive empirical understanding of the existing techniques.
- A Deep Architecture for Semantic Matching with Multiple Positional Sentence Representations359
This work presents a new deep architecture to match two sentences with multiple positional sentence representations, generated by a bidirectional long short term memory (Bi-LSTM).
- Bias and Unfairness in Information Retrieval Systems: New Challenges in the LLM Era247
A comprehensive survey of existing works on emerging and pressing bias and unfairness issues in IR systems when the integration of LLMs is presented, and systematically delve into the specific bias and unfairness issues arising from three critical stages of LLMs integration into IR systems.
- SetRank169
A neural learning-to-rank model called SetRank is proposed which directly learns a permutation-invariant ranking model defined on document sets of any size and Experimental results showed that the SetRank significantly outperformed the baselines include the traditional learning- to-rank models and state-of-the-art Neural IR models.
- HalluciDoctor: Mitigating Hallucinatory Toxicity in Visual Instruction Data167
This work presents a novel hallucination detection and elimination framework, HalluciDoctor, based on the cross-checking paradigm, and uses this framework to identify and eliminate hallucinations in the training data automatically, and indicates that spurious correlations arising from long-tail object cooccurrences contribute to hallucinations.
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- ReCoSa: Detecting the Relevant Contexts with Self-Attention for Multi-turn Dialogue Generation129
Experimental results on both Chinese customer services dataset and English Ubuntu dialogue dataset show that ReCoSa significantly outperforms baseline models, in terms of both metric-based and human evaluations.
- Search-in-the-Chain: Interactively Enhancing Large Language Models with Search for Knowledge-intensive Tasks122
This paper proposes a novel framework named Search-in-the-Chain (SearChain) for the interaction between LLM and IR to solve the challenges and shows that SearChain outperforms state-of-the-art baselines on complex knowledge-intensive tasks including multi-hop Q&A, slot filling, fact checking, and long-form Q&A.
- Can Small Language Models be Good Reasoners for Sequential Recommendation?88
A novel Step-by-step knowLedge knowLedge fraMework for recommendation (SLIM) is proposed, paving a promising path for sequential recommenders to enjoy the exceptional reasoning capabilities of LLMs in a "slim" (i.e. resource-efficient) manner.
- LLMDet: A Third Party Large Language Models Generated Text Detection Tool88
LLMDet is a model-specific, secure, efficient, and extendable detection tool that can source text from specific LLMs, such as GPT-2, OPT, LLaMA, and others, that can effortlessly extend its detection capabilities to a new open-source model.
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- Beyond Factoid QA: Effective Methods for Non-factoid Answer Sentence Retrieval79
This work introduces the answer sentence retrieval task for non-factoid Web queries, and investigates how this task can be effectively solved under a learning to rank framework, and designs two types of features, namely semantic and context features, beyond traditional text matching features.
- Neural Retrievers are Biased Towards LLM-Generated Content76
A quantitative evaluation of IR models in scenarios where both human-written and LLM-generated texts are involved indicates that neural retrieval models tend to rank LLM-generated documents higher, and proposes a plug-and-play debiased constraint for the optimization objective to mitigate the source bias.
- Modeling Topical Relevance for Multi-Turn Dialogue Generation53
A new model, named STAR-BTM, is proposed, which significantly outperforms several state-of-the-art methods, in terms of both metric-based and human evaluations.
- Adaptive Information Seeking for Open-Domain Question Answering44
A novel adaptive information-seeking strategy for open-domain question answering, namely AISO is proposed, which outperforms all baseline methods with predefined strategies in terms of both retrieval and answer evaluations.
- HAS-QA: Hierarchical Answer Spans Model for Open-Domain Question Answering43
A new probabilistic formulation of OpenQA is proposed, based on a three-level hierarchical structure, i.e.,~the question level, the paragraph level and the answer span level, and a Hierarchical Answer Spans Model (HAS-QA) is designed to capture each probability.
- LightPROF: A Lightweight Reasoning Framework for Large Language Model on Knowledge Graph39
A novel Lightweight and efficient Prompt learning-ReasOning Framework for KGQA (LightPROF), which leverages the full potential of LLMs to tackle complex reasoning tasks in a parameter-efficient manner and shows significant advantages in terms of input token count and reasoning time.
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- Transductive Learning for Unsupervised Text Style Transfer33
This paper proposes a novel transductive learning approach, based on a retrieval-based context-aware style representation, which involves top-K relevant sentences in the target style in the transfer process and outperforms several strong baselines.
- Transformation Driven Visual Reasoning32
A novel transformation driven visual reasoning task, where the target is to infer the corresponding single-step or multi-step transformation, represented as a triplet or a sequence of triplets, respectively, which will boost the development of machine visual reasoning.
- A Survey of Generative Search and Recommendation in the Era of Large Language Models31
This paper provides a comprehensive survey of the emerging paradigm in information systems and summarize the developments in generative search and recommendation from a unified perspective, and abstracts a unified framework for the generative paradigm.
- Match-Ignition28
The main idea is to plug the well-known PageRank algorithm into the Transformer, to identify and filter both sentence and word level noisy information in the matching process, and to show that Match-Ignition efficiently captures important sentences and words, to facilitate the long-form text matching process.
- Invisible Relevance Bias: Text-Image Retrieval Models Prefer AI-Generated Images27
The proposed debiasing method is applied to retroactively identify the causes of the invisible relevance bias, revealing that the AI-generated images induce the image encoder to embed additional information into their representation, which makes the retriever estimate a higher relevance score.
- SkillAttack: Automated Red Teaming of Agent Skills through Attack Path Refinement26
SkillAttack is introduced, a red-teaming framework that dynamically verifies skill vulnerability exploitability through adversarial prompting, and outperforms all baselines by a wide margin, revealing that even well-intended skills pose serious security risks under realistic agent interactions.
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- Rowen: Adaptive Retrieval-Augmented Generation for Hallucination Mitigation in LLMs25
This study presents Rowen, a novel framework that enhances LLMs with an adaptive retrieval augmentation process tailored to address hallucinated outputs, and surpasses the current state-of-the-art in both detecting and mitigating hallucinated content within the outputs of LLMs.
- Image matting in the perception granular deep learning24
It is shown that layered deep learning can be formally represented as a framework of a granular system defined by fuzzy logic, i.e., the convolution pyramids or hierarchical convolutional factor analysis in the deeplearning can be viewed as special cases of granular computing.
- LLM Latent Reasoning as Chain of Superposition22
Latent-SFT sets a new state of the art on GSM8k, matching explicit SFT performance while cutting reasoning chains by up to 4× and outperforming prior latent methods and lexical probability–based latent reasoning also clearly surpasses hidden-state–based approaches.
- Cross-Modal Safety Mechanism Transfer in Large Vision-Language Models21
A novel Text-Guided vision-language Alignment method (TGA) for LVLMs is proposed that not only transfers the safety mechanism for text in basic LLMs to vision in vision-language alignment for LVLMs without any safety fine-tuning on the visual modality but also maintains the general performance on various vision tasks.
- Match-Prompt20
Experimental results on eighteen public datasets show that Match-Prompt can improve multi- Task generalization capability of PLMs in text matching and yield better in-domain multi-task, out-of-domainmulti-task and new task adaptation performance than multi- task and task-specific models trained by previous fine-tuning paradigm.
- Sketch and Customize: A Counterfactual Story Generator20
A sketch-and-customize generation model guided by the causality implicated in the conditions and endings is proposed, which generates much better endings, as compared with the traditional sequence-to-sequence model.
- Do LLMs Play Dice? Exploring Probability Distribution Sampling in Large Language Models for Behavioral Simulation19
The analysis indicates that LLM agents can understand probabilities, but they struggle with probability sampling, which makes it difficult to simulate human behavior as agents.
- L2R²: Leveraging Ranking for Abductive Reasoning18
A novel L2R2 approach is proposed under the learning-to-rank framework to evaluate the abductive reasoning ability of a learning system by switching to a ranking perspective that sorts the hypotheses in order of their plausibilities.
- Perception granular computing in visual haze-free task18
A novel learning approach, which combines fuzzy logical designing with machine learning, is proposed in this paper to construct a GrC system to explore a novel direction for deep learning.
- List-aware Reranking-Truncation Joint Model for Search and Retrieval-augmented Generation17
A Reranking-Truncation joint model (GenRT) that can perform the two tasks concurrently via a generative paradigm based on an encoder-decoder architecture with novel loss functions for joint optimization to learn both tasks.
- Stable Knowledge Editing in Large Language Models17
StableKE surpasses other knowledge editing methods, demonstrating stability both edited knowledge and multi-hop knowledge, while also preserving unrelated knowledge and general abilities.
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- A Study of Implicit Ranking Unfairness in Large Language Models16
The need for the community to identify and mitigate the implicit unfairness in Large Language Models, aiming to avert the potential deterioration in the reinforced human-LLMs ecosystem deterioration, is emphasized.
- Knowledge Augmented Dialogue Generation with Divergent Facts Selection16
Comprehensive experiments on a newly released knowledge-grounded conversation dataset Wizard-of-Wikipedia have verified the superiority of the KADG model than previous baselines and shown that the method can refer to the knowledge properly and generate diverse and informative responses.
- Exploring the Escalation of Source Bias in User, Data, and Recommender System Feedback Loop15
A debiasing method based on L1-loss optimization is proposed to maintain long-term content ecosystem balance with AIGC generated by mainstream LLMs in a real-world environment and ensures a balance between AIGC and human-generated content in the ecosystem.
- MLaKE: Multilingual Knowledge Editing Benchmark for Large Language Models14
This study introduces MLaKE (Multilingual Language Knowledge Editing), a novel benchmark comprising 4072 multi-hop and 5360 single-hop questions designed to evaluate the adaptability of knowledge editing methods across five languages: English, Chinese, Japanese, French, and German.
- De-fine: De composing and Re fin ing Visual Programs with Auto-Feedback14
Inspired by benders decomposition, this work introduces De-fine, a training-free framework that automatically decomposes complex tasks into simpler subtasks and refines programs through auto-feedback, which can improve logical reasoning performance by integrating the strengths of multiple models.
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- Information-Controllable Graph Contrastive Learning for Recommendation13
The Information-Controllable Graph Contrastive Learning framework is proposed, a novel approach that focuses on optimizing the shared information between views to include as much relevant information for the recommendation task as possible while maintaining an appropriate level of control.
- Improving Video Corpus Moment Retrieval with Partial Relevance Enhancement13
A multi-modal collaborative video retriever, generating different query representations for the two modalities by modality-specific pooling, ensuring a more effective match and introducing relevant content-enhanced training methods for both retriever and localizer to enhance the ability of model to capture relevant content.
- METNet: A Mutual Enhanced Transformation Network for Aspect-based Sentiment Analysis13
A mutual enhanced transformation network (METNet) for the ABSA task, where the aspect enhancement module in METNet improves the representation learning of the aspect with contextual semantic features, which gives the aspect more abundant information.
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- Fact :Teaching MLLMs with Faithful, Concise and Transferable Rationales12
Fact is introduced, a novel paradigm designed to generate multimodal rationales that are faithful, concise, and transferable for teaching MLLMs and filter rationales that can be transferred to end-to-end paradigms from programming paradigms to guarantee transferability.
- Wasserstein Distance Regularized Sequence Representation for Text Matching in Asymmetrical Domains12
WD-Match is a novel match method tailored for text matching in asymmetrical domains, called WD-Match, which enforces the feature projection function to generate vectors such that those correspond to different domains cannot be easily discriminated.
- Unifying Bias and Unfairness in Information Retrieval: New Challenges in the LLM Era11
This tutorial first unifies bias and unfairness issues as problems of distribution mismatch and further categorizes the mitigation strategies under the umbrella of distribution alignment and summarizes several types of bias and unfairness issues emerging from three critical stages of LLM integration into IR systems.
- Dual-Channel Localization Networks for Moment Retrieval with Natural Language11
An intuitive and efficient Dual-Channel Localization Network (DCLN) is designed to balance computational cost and retrieval performance and learn discriminative features to distinguish the matching degree between natural language query and video moments.
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- Learning Visual Features from Snapshots for Web Search11
This work proposes a novel visual perception model inspired by human's visual search behaviors on page viewing to extract the visual features of Web pages and shows that such visual features can be efficiently acquired in the online setting with an extended inverted indexing scheme.
- Latent-GRPO: Group Relative Policy Optimization for Latent Reasoning10
Latent-GRPO is proposed, which combines invalid-sample advantage masking, one-sided noise sampling, and optimal correct-path first-token selection and achieves stronger pass@k$ performance under Gumbel sampling, and is established as an effective approach for stable and efficient latent reasoning.
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- AdaComp: Extractive Context Compression with Adaptive Predictor for Retrieval-Augmented Large Language Models10
AdaComp is introduced, a low-cost extractive context compression method that adaptively determines the compression rate based on both query complexity and retrieval quality that significantly reduces inference costs while maintaining performance nearly identical to uncompressed models.
- A Disentangled-Attention Based Framework with Persona-Aware Prompt Learning for Dialogue Generation10
This work proposes a disentangled-attention based pre-training architecture, which incorporates persona-aware prompt learning to bridge the connection between the selected persona and response generation and can outperform strong baselines and deliver more consistent and engaging responses on the PERSONA-CHAT dataset.
- RLKD: Distilling LLMs’ Reasoning via Reinforcement Learning9
RLKD, a reinforcement learning (RL)-based distillation framework guided by a novel Generative Structure Reward Model (GSRM), enables the student LLM to internalize the teacher’s implicit multi-branch structure in authentic reasoning, rather than merely mimicking fixed teacher's output paths.
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- SCTc-TE: A Comprehensive Formulation and Benchmark for Temporal Event Forecasting8
A novel formulation for Structured, Complex, and Time-complete Temporal Event (SCTc-TE) is introduced, and a novel model that leverages both Lo cal and G l o bal contexts for SCTc-TE forecasting, named LoGo is proposed.
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- PEDNet: A Persona Enhanced Dual Alternating Learning Network for Conversational Response Generation8
Both automatic and human evaluations on a newly released dialogue dataset Persona-chat demonstrate that the proposed PEDNet method could deliver more personalized responses than baseline methods.
- Evaluating Natural Language Generation via Unbalanced Optimal Transport8
Inspired by the optimal transportation theory, this paper proposes a family of new evaluation metrics, namely Lazy Earth Mover's Distances, based on the more general unbalanced optimal transport problem, and proves that these metrics correspond to the optimal transport problem with different hard marginal constraints.
- Visual Reasoning: From State to Transformation7
This work proposes a novel transformation driven visual reasoning (TVR) task, inspired by human reasoning, and proposes a three-staged reasoning framework called TranNet, including observing, analyzing, and concluding, to test how recent advanced techniques perform on TVR.
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- NIR-Prompt: A Multi-task Generalized Neural Information Retrieval Training Framework7
Experiments under in-domain multi-task, out-of-domain multi-task, and new task adaptation settings show that NIR-Prompt can improve the generalization of PLMs in NIR for both retrieval and reranking stages compared with baselines.
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- NExT-Search: Rebuilding User Feedback Ecosystem for Generative AI Search6
NExT-Search is envisioned, a next-generation paradigm designed to reintroduce fine-grained, process-level feedback into generative AI search, and offers a promising direction for building feedback-rich AI search systems that can evolve continuously alongside human feedback.
- Everything is Editable: Extend Knowledge Editing to Unstructured Data in Large Language Models6
A novel unstructured knowledge editing method, namely UnKE, which extends previous assumptions in the layer dimension and token dimension, and effectively represents and edits complex and comprehensive unstructured knowledge, leveraging the potential of both the MLP and attention layers.
- LoL6
The Loss-over-Loss (LoL) framework is proposed to compare the reformulation losses between different revisions of the same query during training, and a differentiable query reformulation method is presented to implement this framework.
- Beyond Probability Ranking Principle: Modeling the Dependencies among Documents6
This tutorial aims to give a comprehensive survey on recently developed ranking models that go beyond the PRP principle, and tries to categorize these models based on their intrinsic assumptions: assuming that the documents are independent, sequentially dependent, or globally dependent.
- RI-Match: Integrating Both Representations and Interactions for Deep Semantic Matching6
This paper proposes a new deep learning architecture to combine the merits of both deep matching approaches, and shows that this model outperforms traditional state-of-the art baselines significantly.
- Learning to Retrieve from Agent Trajectories5
This work proposes LRAT, a simple yet effective framework that mines high-quality retrieval supervision from agent trajectories and incorporates relevance intensity through weighted optimization and demonstrates that retrievers trained with LRAT consistently improve evidence recall, end-to-end task success, and execution efficiency across diverse agent architectures and scales.
- Circular Reasoning: Understanding Self-Reinforcing Loops in Large Reasoning Models5
This paper identifies circular reasoning as a state collapse exhibiting distinct boundaries, where semantic repetition precedes textual repetition and reveals that reasoning impasses trigger the loop onset, which subsequently persists as an inescapable cycle driven by a self-reinforcing V-shaped attention mechanism.
- Bridging Jensen Gap for Max-Min Group Fairness Optimization in Recommendation5
An efficient and effective algorithm named FairDual is presented, which utilizes a dual optimization technique to minimize the Jensen gap and can achieve a sub-linear convergence rate to the globally optimal solution and the Jensen gap can be well bounded under a mini-batch sampling strategy with random shuffle.
- Visual Transformation Telling5
Experimental results reveal that even state-of-the-art models still face challenges in VTT, highlighting substantial areas for improvement.
- Beyond Language: Learning Commonsense from Images for Reasoning5
This paper proposes a novel approach to learn commonsense from images, instead of limited raw texts or costly constructed knowledge bases, for the commonsense reasoning problem in NLP, and demonstrates that Loire outperforms traditional language-based methods.
- Mitigating Source Bias with LLM Alignment4
LLM-SBM, a novel LLM alignment framework for source bias mitigation, is introduced and extensive experiments demonstrate that LLMs aligned with LLM-SBM successfully reduce source bias while preserving their general capabilities.
- Unveiling Knowledge Boundary of Large Language Models for Trustworthy Information Access4
This tutorial will explore the taxonomy of knowledge boundary in LLMs, addressing their handling of uncertainty, response calibration, and mitigation of unintended behaviors that can arise during interaction with users.
- Knowledge Boundary and Persona Dynamic Shape A Better Social Media Agent4
This work constructs the social media agent based on personalized knowledge and dynamic persona information, and designs five basic modules for it: persona, planning, action, memory and reflection.
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- DIVA-GRPO: Enhancing Multimodal Reasoning through Difficulty-Adaptive Variant Advantage3
DIVA-GRPO is proposed, a difficulty-adaptive variant advantage method that adjusts variant difficulty distributions from a global perspective that alleviates reward sparsity and advantage vanishing while improving training stability.
- Cross-Model Comparative Loss for Enhancing Neuronal Utility in Language Understanding3
The universal effectiveness of comparative loss is demonstrated through extensive experiments on 14 datasets from three distinct NLU tasks based on five widely used pre-trained language models and find it particularly superior for models with few parameters or long input.
- A Theory for Token-Level Harmonization in Retrieval-Augmented Generation3
This paper takes the first step in theoretically giving the essential explanation of benefit and detriment in RAG by decoupling and formalizing them from RAG prediction, approximating the gap between their values by representation similarity and establishing the trade-off mechanism between them, to make them explainable, quantifiable, and comparable.
- PMHR: Path-Based Multi-Hop Reasoning Incorporating Rule-Enhanced Reinforcement Learning and KG Embeddings3
A novel approach that combines KG embeddings and RL strategies for multi-hop reasoning called path-based multi-hop reasoning (PMHR), which provides not only improved reasoning accuracy and explainability but also optimized computational efficiency, thereby offering a robust solution for multi-hop reasoning.
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- Bursty Event Detection via Multichannel Feature Alignment3
This paper proposes to combine the data from daily news and social media channels to construct an efficient and effective bursty event detection system and shows that the framework outperforms baselines.
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- DxDirector: an agentic large language model driving the full-process clinical diagnosis2
DxDirector-7B is presented, an agentic LLM designed to navigate the full diagnostic process through advanced slow thinking capabilities, offering a scalable solution to enhance diagnostic efficiency and accessibility.
- Generative Ghost: Investigating Ranking Bias Hidden in AI-Generated Videos2
Unlike the preference observed in image modalities, it is found that video retrieval bias arises from both unseen visual and temporal information, making the root causes of video bias a complex interplay of these two factors.
- A Factuality and Diversity Reconciled Decoding Method for Knowledge-Grounded Dialogue Generation2
To discover a solution for advancing creativity without relying on questionable randomness and to subtly reconcile the factuality and diversity within the source-grounded paradigm, a novel method named DoGe is proposed.
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- Dynamic Rollout Editing for Reducing Overthinking in RL-Trained Reasoning Models1
Dynamic Rollout Editing (DRE), a training-time intervention for successful trajectories that continue thinking after answer emergence, is introduced, weakening the preference signal for unnecessary thinking without penalizing the reasoning needed to reach the answer.
- ActiveMem: Distributed Active Memory for Long-Horizon LLM Reasoning1
This work proposes ActiveMem, a heterogeneous framework that decouples agent memory from the core reasoning process, and shows that ActiveMem achieves state-of-the-art accuracy with significantly reduced overhead, demonstrating the effectiveness of distributed active memory for long-horizon reasoning.
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- The 1st Workshop on LLM Agents for Social Simulation1
This workshop on LLM Agent for Social Simulation (LASS) aims to bring together researchers and practitioners from diverse backgrounds to foster interdisciplinary collaboration, address key challenges, explore new technologies, and chart promising future directions in this rapidly evolving field.
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- Multi-video Moment Ranking with Multimodal Clue1
MINUTE uses Shared Normalization during both training and inference to rank candidate moments from multiple videos to solve moment predict bias, making it more efficient to predict target moment.
- SPAN: Understanding a Question with Its Support Answers1
This paper proposes a novel deep architecture namely SPAN to tackle the problem of the information lack in question's descriptions and the lexical gap between questions and answers, and introduces support answers to help understand the question.
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- A Review of Neural Question Generation: Approaches, Challenges, and Future Directions–
This survey provides a comprehensive review of developments in NQG, spanning traditional neural approaches to the latest paradigms driven by large language models (LLMs) and multimodal large language models (MLLMs).
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- ACE-Align: Attribute Causal Effect Alignment for Cultural Values under Varying Persona Granularities–
This work proposes ACE-Align (Atribute Causal Effect Alignment), a causally inspired framework based on controlled persona edits that aligns how specific demographic attributes shift different cultural values, rather than treating each culture as a homogeneous group.
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- Can Synthetic Query Rewrites Capture User Intent Better than Humans in Retrieval-Augmented Generation?–
Experiments on TopiOCQA and QRECC datasets show that SynRewrite consistently outperforms human rewrites in both retrieval and generation tasks, demonstrating that synthetic rewrites can serve as a scalable and effective alternative to human annotations.
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- LoL: A Comparative Regularization Loss over Query Reformulation Losses for Pseudo-Relevance Feedback–
- Perception Granular Computing in Visual Cognition Task–
The purpose of the book is to advance in the understanding of brain functions by defining a general framework for representation based on granular computing theory, setting the basis for a theory of image understanding and learning of visual tasks.
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- Hybrid Designing of a Neural System by Combining Fuzzy Logical Framework and PSVM for Visual Haze-Free Task–
A novel approach by combining fuzzy logical designing with Proximal Support Vector Machine Classifier learning in the designing of large scale neural networks is developed, and the experimental results show that texture features extracted by fuzzy logic can effectively increase the texture information entropy and improve the effect of haze-removing in some degree.
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Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-12. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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