pwc-archive/papers-with-abstracts huggingface dataset:
KERL: Knowledge-Enhanced Personalized Recipe Recommendation using Large Language Models
Authors: Fnu Mohbat, Mohammed J Zaki
Date: 2025-05-20 00:00:00
Tasks: Food recommendation, Knowledge Graphs, Recipe Generation, Recommendation Systems

Recent advances in large language models (LLMs) and the abundance of food data have resulted in studies to improve food understanding using LLMs. Despite several recommendation systems utilizing LLMs and Knowledge Graphs (KGs), there has been limited research on integrating food related KGs with LLMs. We introduce KERL, a unified system that leverages food KGs and LLMs to provide personalized food recommendations and generates recipes with associated micro-nutritional information. Given a natural language question, KERL extracts entities, retrieves subgraphs from the KG, which are then fed into the LLM as context to select the recipes that satisfy the constraints. Next, our system generates the cooking steps and nutritional information for each recipe. To evaluate our approach, we also develop a benchmark dataset by curating recipe related questions, combined with constraints and personal preferences. Through extensive experiments, we show that our proposed KG-augmented LLM significantly outperforms existing approaches, offering a complete and coherent solution for food recommendation, recipe generation, and nutritional analysis. Our code and benchmark datasets are publicly available at https://github.com/mohbattharani/KERL.
EmoGist: Efficient In-Context Learning for Visual Emotion Understanding
Authors: Ronald Seoh, Dan Goldwasser
Date: 2025-05-20 00:00:00
Tasks: Emotion Classification, In-Context Learning

In this paper, we introduce EmoGist, a training-free, in-context learning method for performing visual emotion classification with LVLMs. The key intuition of our approach is that context-dependent definition of emotion labels could allow more accurate predictions of emotions, as the ways in which emotions manifest within images are highly context dependent and nuanced. EmoGist pre-generates multiple explanations of emotion labels, by analyzing the clusters of example images belonging to each category. At test time, we retrieve a version of explanation based on embedding similarity, and feed it to a fast VLM for classification. Through our experiments, we show that EmoGist allows up to 13 points improvement in micro F1 scores with the multi-label Memotion dataset, and up to 8 points in macro F1 in the multi-class FI dataset.
Two Experts Are All You Need for Steering Thinking: Reinforcing Cognitive Effort in MoE Reasoning Models Without Additional Training
Authors: Mengru Wang, Xingyu Chen, Yue Wang, Zhiwei He, Jiahao Xu, Tian Liang, Qiuzhi Liu, Yunzhi Yao, Wenxuan Wang, Ruotian Ma, Haitao Mi, Ningyu Zhang, Zhaopeng Tu, Xiaolong Li, Dong Yu
Date: 2025-05-20 00:00:00
Tasks: All, Domain Generalization, Instruction Following, Mixture-of-Experts

Mixture-of-Experts (MoE) architectures within Large Reasoning Models (LRMs) have achieved impressive reasoning capabilities by selectively activating experts to facilitate structured cognitive processes. Despite notable advances, existing reasoning models often suffer from cognitive inefficiencies like overthinking and underthinking. To address these limitations, we introduce a novel inference-time steering methodology called Reinforcing Cognitive Experts (RICE), designed to improve reasoning performance without additional training or complex heuristics. Leveraging normalized Pointwise Mutual Information (nPMI), we systematically identify specialized experts, termed ''cognitive experts'' that orchestrate meta-level reasoning operations characterized by tokens like ''<think>''. Empirical evaluations with leading MoE-based LRMs (DeepSeek-R1 and Qwen3-235B) on rigorous quantitative and scientific reasoning benchmarks demonstrate noticeable and consistent improvements in reasoning accuracy, cognitive efficiency, and cross-domain generalization. Crucially, our lightweight approach substantially outperforms prevalent reasoning-steering techniques, such as prompt design and decoding constraints, while preserving the model's general instruction-following skills. These results highlight reinforcing cognitive experts as a promising, practical, and interpretable direction to enhance cognitive efficiency within advanced reasoning models.
ActiveSSF: An Active-Learning-Guided Self-Supervised Framework for Long-Tailed Megakaryocyte Classification
Authors: Linghao Zhuang, Ying Zhang, Gege Yuan, Xingyue Zhao, Zhiping Jiang
Date: 2025-02-12 00:00:00
Tasks: Active Learning, Clustering, Medical Image Analysis, Self-Supervised Learning

Precise classification of megakaryocytes is crucial for diagnosing myelodysplastic syndromes. Although self-supervised learning has shown promise in medical image analysis, its application to classifying megakaryocytes in stained slides faces three main challenges: (1) pervasive background noise that obscures cellular details, (2) a long-tailed distribution that limits data for rare subtypes, and (3) complex morphological variations leading to high intra-class variability. To address these issues, we propose the ActiveSSF framework, which integrates active learning with self-supervised pretraining. Specifically, our approach employs Gaussian filtering combined with K-means clustering and HSV analysis (augmented by clinical prior knowledge) for accurate region-of-interest extraction; an adaptive sample selection mechanism that dynamically adjusts similarity thresholds to mitigate class imbalance; and prototype clustering on labeled samples to overcome morphological complexity. Experimental results on clinical megakaryocyte datasets demonstrate that ActiveSSF not only achieves state-of-the-art performance but also significantly improves recognition accuracy for rare subtypes. Moreover, the integration of these advanced techniques further underscores the practical potential of ActiveSSF in clinical settings.
Uni4D: A Unified Self-Supervised Learning Framework for Point Cloud Videos
Authors: Zhi Zuo, Chenyi Zhuang, Pan Gao, Jie Qin, Hao Feng, Nicu Sebe
Date: 2025-04-07 00:00:00
Tasks: Action Segmentation, Representation Learning, Self-Supervised Learning

Self-supervised representation learning for point cloud videos remains a challenging problem with two key limitations: (1) existing methods rely on explicit knowledge to learn motion, resulting in suboptimal representations; (2) prior Masked AutoEncoder (MAE) frameworks struggle to bridge the gap between low-level geometry and high-level dynamics in 4D data. In this work, we propose a novel self-disentangled MAE for learning expressive, discriminative, and transferable 4D representations. To overcome the first limitation, we learn motion by aligning high-level semantics in the latent space \textit{without any explicit knowledge}. To tackle the second, we introduce a \textit{self-disentangled learning} strategy that incorporates the latent token with the geometry token within a shared decoder, effectively disentangling low-level geometry and high-level semantics. In addition to the reconstruction objective, we employ three alignment objectives to enhance temporal understanding, including frame-level motion and video-level global information. We show that our pre-trained encoder surprisingly discriminates spatio-temporal representation without further fine-tuning. Extensive experiments on MSR-Action3D, NTU-RGBD, HOI4D, NvGesture, and SHREC'17 demonstrate the superiority of our approach in both coarse-grained and fine-grained 4D downstream tasks. Notably, Uni4D improves action segmentation accuracy on HOI4D by $+3.8\%$.
Structure-Preserving Zero-Shot Image Editing via Stage-Wise Latent Injection in Diffusion Models
Authors: Dasol Jeong, Donggoo Kang, Jiwon Park, Hyebean Lee, Joonki Paik
Date: 2025-04-22 00:00:00
Tasks: Attribute

We propose a diffusion-based framework for zero-shot image editing that unifies text-guided and reference-guided approaches without requiring fine-tuning. Our method leverages diffusion inversion and timestep-specific null-text embeddings to preserve the structural integrity of the source image. By introducing a stage-wise latent injection strategy-shape injection in early steps and attribute injection in later steps-we enable precise, fine-grained modifications while maintaining global consistency. Cross-attention with reference latents facilitates semantic alignment between the source and reference. Extensive experiments across expression transfer, texture transformation, and style infusion demonstrate state-of-the-art performance, confirming the method's scalability and adaptability to diverse image editing scenarios.
Evaluating the Correctness of Inference Patterns Used by LLMs for Judgment
Authors: Lu Chen, Yuxuan Huang, Yixing Li, Dongrui Liu, Qihan Ren, Shuai Zhao, Kun Kuang, Zilong Zheng, Quanshi Zhang
Date: 2024-10-06 00:00:00
Tasks: Text Generation

This paper presents a method to analyze the inference patterns used by Large Language Models (LLMs) for judgment in a case study on legal LLMs, so as to identify potential incorrect representations of the LLM, according to human domain knowledge. Unlike traditional evaluations on language generation results, we propose to evaluate the correctness of the detailed inference patterns of an LLM behind its seemingly correct outputs. To this end, we quantify the interactions between input phrases used by the LLM as primitive inference patterns, because recent theoretical achievements have proven several mathematical guarantees of the faithfulness of the interaction-based explanation. We design a set of metrics to evaluate the detailed inference patterns of LLMs. Experiments show that even when the language generation results appear correct, a significant portion of the inference patterns used by the LLM for the legal judgment may represent misleading or irrelevant logic.
MedEIR: A Specialized Medical Embedding Model for Enhanced Information Retrieval
Authors: Anand Selvadurai, Jasheen Shaik, Girish Chandrasekar, ShriRadhaKrishnan Balamurugan, Eswara Reddy
Date: 2025-05-12 00:00:00
Tasks: Information Retrieval, RAG, Re-Ranking, Retrieval, Retrieval-augmented Generation

Embedding models have become essential for retrieval-augmented generation (RAG) tasks, semantic clustering, and text re-ranking. But despite their growing use, many of these come with notable limitations. For example, Jina fails to capture the semantic content of medical documents, while models such as MiniLM often perform poorly on long-form documents. Domain-adapted models, while specialized, often underperform in general-purpose tasks, reducing their overall applicability. General-domain tokenizers often misinterpret medical vocabulary. The limitations of current embedding models, whether in tokenization accuracy, domain comprehension, or handling long sequences, highlight the need for more versatile solutions. In this work, we present MedEIR, a novel embedding model and tokenizer jointly optimized for both medical and general NLP tasks, incorporating ALiBi-based long-context processing to support sequences of up to 8,192 tokens. MedEIR was pre-trained on only 6 billion tokens, significantly fewer than Jina's, followed by fine-tuning on 3 million sentence pairs. MedEIR consistently outperforms Jina V2 and MiniLM across MTEB benchmarks, achieving top scores on ArguAna (55.24), NFCorpus (38.44), MedicalQARetrieval (74.25), SciFact (72.04), and TRECCOVID (79.56). These results highlight the potential of MedEIR as a highly effective embedding model, demonstrating strong performance across both general-purpose and domain-specific tasks and outperforming existing models on multiple benchmarks.
Beyond Retrieval: Joint Supervision and Multimodal Document Ranking for Textbook Question Answering
Authors: Hessa Alawwad, Usman Naseem, Areej Alhothali, Ali Alkhathlan, Amani Jamal
Date: 2025-05-17 00:00:00
Tasks: Document Ranking, Large Language Model, Multimodal Large Language Model, Question Answering, Retrieval, Retrieval-augmented Generation

Textbook question answering (TQA) is a complex task, requiring the interpretation of complex multimodal context. Although recent advances have improved overall performance, they often encounter difficulties in educational settings where accurate semantic alignment and task-specific document retrieval are essential. In this paper, we propose a novel approach to multimodal textbook question answering by introducing a mechanism for enhancing semantic representations through multi-objective joint training. Our model, Joint Embedding Training With Ranking Supervision for Textbook Question Answering (JETRTQA), is a multimodal learning framework built on a retriever--generator architecture that uses a retrieval-augmented generation setup, in which a multimodal large language model generates answers. JETRTQA is designed to improve the relevance of retrieved documents in complex educational contexts. Unlike traditional direct scoring approaches, JETRTQA learns to refine the semantic representations of questions and documents through a supervised signal that combines pairwise ranking and implicit supervision derived from answers. We evaluate our method on the CK12-QA dataset and demonstrate that it significantly improves the discrimination between informative and irrelevant documents, even when they are long, complex, and multimodal. JETRTQA outperforms the previous state of the art, achieving a 2.4\% gain in accuracy on the validation set and 11.1\% on the test set.
Geography-Aware Large Language Models for Next POI Recommendation
Authors: Zhao Liu, Wei Liu, Huajie Zhu, Jianxing Yu, Jian Yin, Wang-Chien Lee, Shun Wang
Date: 2025-05-18 00:00:00
Tasks: Large Language Model

The next Point-of-Interest (POI) recommendation task aims to predict users' next destinations based on their historical movement data and plays a key role in location-based services and personalized applications. Accurate next POI recommendation depends on effectively modeling geographic information and POI transition relations, which are crucial for capturing spatial dependencies and user movement patterns. While Large Language Models (LLMs) exhibit strong capabilities in semantic understanding and contextual reasoning, applying them to spatial tasks like next POI recommendation remains challenging. First, the infrequent nature of specific GPS coordinates makes it difficult for LLMs to model precise spatial contexts. Second, the lack of knowledge about POI transitions limits their ability to capture potential POI-POI relationships. To address these issues, we propose GA-LLM (Geography-Aware Large Language Model), a novel framework that enhances LLMs with two specialized components. The Geographic Coordinate Injection Module (GCIM) transforms GPS coordinates into spatial representations using hierarchical and Fourier-based positional encoding, enabling the model to understand geographic features from multiple perspectives. The POI Alignment Module (PAM) incorporates POI transition relations into the LLM's semantic space, allowing it to infer global POI relationships and generalize to unseen POIs. Experiments on three real-world datasets demonstrate the state-of-the-art performance of GA-LLM.
LLM-Based User Simulation for Low-Knowledge Shilling Attacks on Recommender Systems
Authors: Shengkang Gu, Jiahao Liu, Dongsheng Li, Guangping Zhang, Mingzhe Han, Hansu Gu, Peng Zhang, Ning Gu, Li Shang, Tun Lu
Date: 2025-05-18 00:00:00
Tasks: Language Modeling, Language Modelling, Large Language Model, Recommendation Systems, Review Generation, User Simulation

Recommender systems (RS) are increasingly vulnerable to shilling attacks, where adversaries inject fake user profiles to manipulate system outputs. Traditional attack strategies often rely on simplistic heuristics, require access to internal RS data, and overlook the manipulation potential of textual reviews. In this work, we introduce Agent4SR, a novel framework that leverages Large Language Model (LLM)-based agents to perform low-knowledge, high-impact shilling attacks through both rating and review generation. Agent4SR simulates realistic user behavior by orchestrating adversarial interactions, selecting items, assigning ratings, and crafting reviews, while maintaining behavioral plausibility. Our design includes targeted profile construction, hybrid memory retrieval, and a review attack strategy that propagates target item features across unrelated reviews to amplify manipulation. Extensive experiments on multiple datasets and RS architectures demonstrate that Agent4SR outperforms existing low-knowledge baselines in both effectiveness and stealth. Our findings reveal a new class of emergent threats posed by LLM-driven agents, underscoring the urgent need for enhanced defenses in modern recommender systems.
Information Extraction from Visually Rich Documents using LLM-based Organization of Documents into Independent Textual Segments
Authors: Aniket Bhattacharyya, Anurag Tripathi, Ujjal Das, Archan Karmakar, Amit Pathak, Maneesh Gupta
Date: 2025-05-18 00:00:00

Information extraction (IE) from Visually Rich Documents (VRDs) containing layout features along with text is a critical and well-studied task. Specialized non-LLM NLP-based solutions typically involve training models using both textual and geometric information to label sequences/tokens as named entities or answers to specific questions. However, these approaches lack reasoning, are not able to infer values not explicitly present in documents, and do not generalize well to new formats. Generative LLM-based approaches proposed recently are capable of reasoning, but struggle to comprehend clues from document layout especially in previously unseen document formats, and do not show competitive performance in heterogeneous VRD benchmark datasets. In this paper, we propose BLOCKIE, a novel LLM-based approach that organizes VRDs into localized, reusable semantic textual segments called $\textit{semantic blocks}$, which are processed independently. Through focused and more generalizable reasoning,our approach outperforms the state-of-the-art on public VRD benchmarks by 1-3% in F1 scores, is resilient to document formats previously not encountered and shows abilities to correctly extract information not explicitly present in documents.
RAGXplain: From Explainable Evaluation to Actionable Guidance of RAG Pipelines
Authors: Dvir Cohen, Lin Burg, Gilad Barkan
Date: 2025-05-18 00:00:00
Tasks: Decision Making, Question Answering, RAG, Retrieval-augmented Generation

Retrieval-Augmented Generation (RAG) systems show promise by coupling large language models with external knowledge, yet traditional RAG evaluation methods primarily report quantitative scores while offering limited actionable guidance for refining these complex pipelines. In this paper, we introduce RAGXplain, an evaluation framework that quantifies RAG performance and translates these assessments into clear insights that clarify the workings of its complex, multi-stage pipeline and offer actionable recommendations. Using LLM reasoning, RAGXplain converts raw scores into coherent narratives identifying performance gaps and suggesting targeted improvements. By providing transparent explanations for AI decision-making, our framework fosters user trust-a key challenge in AI adoption. Our LLM-based metric assessments show strong alignment with human judgments, and experiments on public question-answering datasets confirm that applying RAGXplain's actionable recommendations measurably improves system performance. RAGXplain thus bridges quantitative evaluation and practical optimization, empowering users to understand, trust, and enhance their AI systems.
Know Or Not: a library for evaluating out-of-knowledge base robustness
Authors: Jessica Foo, Pradyumna Shyama Prasad, Shaun Khoo
Date: 2025-05-19 00:00:00
Tasks: Hallucination, RAG, Retrieval-augmented Generation

While the capabilities of large language models (LLMs) have progressed significantly, their use in high-stakes applications have been limited due to risks of hallucination. One key approach in reducing hallucination is retrieval-augmented generation (RAG), but even in such setups, LLMs may still hallucinate when presented with questions outside of the knowledge base. Such behavior is unacceptable in high-stake applications where LLMs are expected to abstain from answering queries it does not have sufficient context on. In this work, we present a novel methodology for systematically evaluating out-of-knowledge base (OOKB) robustness of LLMs (whether LLMs know or do not know) in the RAG setting, without the need for manual annotation of gold standard answers. We implement our methodology in knowornot, an open-source library that enables users to develop their own customized evaluation data and pipelines for OOKB robustness. knowornot comprises four main features. Firstly, it provides a unified, high-level API that streamlines the process of setting up and running robustness benchmarks. Secondly, its modular architecture emphasizes extensibility and flexibility, allowing users to easily integrate their own LLM clients and RAG settings. Thirdly, its rigorous data modeling design ensures experiment reproducibility, reliability and traceability. Lastly, it implements a comprehensive suite of tools for users to customize their pipelines. We demonstrate the utility of knowornot by developing a challenging benchmark, PolicyBench, which spans four Question-Answer (QA) chatbots on government policies, and analyze its OOKB robustness. The source code of knowornot is available https://github.com/govtech-responsibleai/KnowOrNot.
JIR-Arena: The First Benchmark Dataset for Just-in-time Information Recommendation
Authors: Ke Yang, Kevin Ros, Shankar Kumar Senthil Kumar, ChengXiang Zhai
Date: 2025-05-19 00:00:00
Tasks: Information Retrieval, Retrieval

Just-in-time Information Recommendation (JIR) is a service designed to deliver the most relevant information precisely when users need it, , addressing their knowledge gaps with minimal effort and boosting decision-making and efficiency in daily life. Advances in device-efficient deployment of foundation models and the growing use of intelligent wearable devices have made always-on JIR assistants feasible. However, there has been no systematic effort to formally define JIR tasks or establish evaluation frameworks. To bridge this gap, we present the first mathematical definition of JIR tasks and associated evaluation metrics. Additionally, we introduce JIR-Arena, a multimodal benchmark dataset featuring diverse, information-request-intensive scenarios to evaluate JIR systems across critical dimensions: i) accurately inferring user information needs, ii) delivering timely and relevant recommendations, and iii) avoiding irrelevant content that may distract users. Developing a JIR benchmark dataset poses challenges due to subjectivity in estimating user information needs and uncontrollable system variables affecting reproducibility. To address these, JIR-Arena: i) combines input from multiple humans and large AI models to approximate information need distributions; ii) assesses JIR quality through information retrieval outcomes using static knowledge base snapshots; and iii) employs a multi-turn, multi-entity validation framework to improve objectivity and generality. Furthermore, we implement a baseline JIR system capable of processing real-time information streams aligned with user inputs. Our evaluation of this baseline system on JIR-Arena indicates that while foundation model-based JIR systems simulate user needs with reasonable precision, they face challenges in recall and effective content retrieval. To support future research in this new area, we fully release our code and data.