pwc-archive/papers-with-abstracts huggingface dataset:
NormXLogit: The Head-on-Top Never Lies
Authors: Sina Abbasi, Mohammad Reza Modarres, Mohammad Taher Pilehvar
Date: 2024-11-25 00:00:00
Tasks: Word Embeddings

The Transformer architecture has emerged as the dominant choice for building large language models (LLMs). However, with new LLMs emerging on a frequent basis, it is important to consider the potential value of architecture-agnostic approaches that can provide interpretability across a variety of architectures. Despite recent successes in the interpretability of LLMs, many existing approaches rely on complex methods that are often tied to a specific model design and come with a significant computational cost. To address these limitations, we propose a novel technique, called NormXLogit, for assessing the significance of individual input tokens. This method operates based on the input and output representations associated with each token. First, we demonstrate that during the pre-training of LLMs, the norms of word embeddings capture the importance of input tokens. Second, we reveal a significant relationship between a token's importance and the extent to which its representation can resemble the model's final prediction. Through extensive analysis, we show that our approach consistently outperforms existing gradient-based methods in terms of faithfulness. Additionally, our method achieves better performance in layer-wise explanations compared to the most prominent architecture-specific methods.
Transparent Neighborhood Approximation for Text Classifier Explanation
Authors: Yi Cai, Arthur Zimek, Eirini Ntoutsi, Gerhard Wunder
Date: 2024-11-25 00:00:00

Recent literature highlights the critical role of neighborhood construction in deriving model-agnostic explanations, with a growing trend toward deploying generative models to improve synthetic instance quality, especially for explaining text classifiers. These approaches overcome the challenges in neighborhood construction posed by the unstructured nature of texts, thereby improving the quality of explanations. However, the deployed generators are usually implemented via neural networks and lack inherent explainability, sparking arguments over the transparency of the explanation process itself. To address this limitation while preserving neighborhood quality, this paper introduces a probability-based editing method as an alternative to black-box text generators. This approach generates neighboring texts by implementing manipulations based on in-text contexts. Substituting the generator-based construction process with recursive probability-based editing, the resultant explanation method, XPROB (explainer with probability-based editing), exhibits competitive performance according to the evaluation conducted on two real-world datasets. Additionally, XPROB's fully transparent and more controllable construction process leads to superior stability compared to the generator-based explainers.
DoubleCCA: Improving Foundation Model Group Robustness with Random Sentence Embeddings
Authors: Hong Liu, Yitong Lu
Date: 2024-11-25 00:00:00
Tasks: Sentence, Sentence Embedding, Sentence-Embedding, Sentence Embeddings

This paper presents a novel method to improve the robustness of foundation models to group-based biases. We propose a simple yet effective method, called DoubleCCA, that leverages random sentences and Canonical Correlation Analysis (CCA) to enrich the text embeddings of the foundation model. First, we generate various random sentences that augment the original prompts, which extends the original prompts with random words or character sequences. Second, we use an additional sentence embedding model to generate different text embeddings with respect to these random sentences. We then use CCA double twice to align the representations and reconstruct them back to the original representation space. We demonstrate the effectiveness of our method on a variety of tasks and datasets, showing that it outperforms existing methods in terms of both performance and robustness. Our method is simple to implement and can be easily integrated into existing models, making it a practical solution for improving the robustness of foundation models to group-based biases.
MH-MoE: Multi-Head Mixture-of-Experts
Authors: Shaohan Huang, Xun Wu, Shuming Ma, Furu Wei
Date: 2024-11-25 00:00:00
Tasks: Mixture-of-Experts

Multi-Head Mixture-of-Experts (MH-MoE) demonstrates superior performance by using the multi-head mechanism to collectively attend to information from various representation spaces within different experts. In this paper, we present a novel implementation of MH-MoE that maintains both FLOPs and parameter parity with sparse Mixture of Experts models. Experimental results on language models show that the new implementation yields quality improvements over both vanilla MoE and fine-grained MoE models. Additionally, our experiments demonstrate that MH-MoE is compatible with 1-bit Large Language Models (LLMs) such as BitNet.
SAGEval: The frontiers of Satisfactory Agent based NLG Evaluation for reference-free open-ended text
Authors: Reshmi Ghosh, Tianyi Yao, Lizzy Chen, Sadid Hasan, Tianwei Chen, Dario Bernal, Huitian Jiao, H M Sajjad Hossain
Date: 2024-11-25 00:00:00
Tasks: Language Modeling, Language Modelling, Large Language Model, Multiple-choice, nlg evaluation, Text Generation

Large Language Model (LLM) integrations into applications like Microsoft365 suite and Google Workspace for creating/processing documents, emails, presentations, etc. has led to considerable enhancements in productivity and time savings. But as these integrations become more more complex, it is paramount to ensure that the quality of output from the LLM-integrated applications are relevant and appropriate for use. Identifying the need to develop robust evaluation approaches for natural language generation, wherein references/ground labels doesn't exist or isn't amply available, this paper introduces a novel framework called "SAGEval" which utilizes a critiquing Agent to provide feedback on scores generated by LLM evaluators. We show that the critiquing Agent is able to rectify scores from LLM evaluators, in absence of references/ground-truth labels, thereby reducing the need for labeled data even for complex NLG evaluation scenarios, like the generation of JSON-structured forms/surveys with responses in different styles like multiple choice, likert ratings, single choice questions, etc.
TransCompressor: LLM-Powered Multimodal Data Compression for Smart Transportation
Authors: Huanqi Yang, Rucheng Wu, Weitao Xu
Date: 2024-11-25 00:00:00
Tasks: Data Compression, Management, Retrieval

The incorporation of Large Language Models (LLMs) into smart transportation systems has paved the way for improving data management and operational efficiency. This study introduces TransCompressor, a novel framework that leverages LLMs for efficient compression and decompression of multimodal transportation sensor data. TransCompressor has undergone thorough evaluation with diverse sensor data types, including barometer, speed, and altitude measurements, across various transportation modes like buses, taxis, and MTRs. Comprehensive evaluation illustrates the effectiveness of TransCompressor in reconstructing transportation sensor data at different compression ratios. The results highlight that, with well-crafted prompts, LLMs can utilize their vast knowledge base to contribute to data compression processes, enhancing data storage, analysis, and retrieval in smart transportation settings.
Exploring Performance Contrasts in TableQA: Step-by-Step Reasoning Boosts Bigger Language Models, Limits Smaller Language Models
Authors: Haoyan Yang, YiXuan Wang, Keyue Tong, Hongjin Zhu, Yuanxin Zhang
Date: 2024-11-24 00:00:00

This paper proposes a detailed prompting flow, termed Table-Logic, to investigate the performance contrasts between bigger and smaller language models (LMs) utilizing step-by-step reasoning methods in the TableQA task. The method processes tasks by sequentially identifying critical columns and rows given question and table with its structure, determining necessary aggregations, calculations, or comparisons, and finally inferring the results to generate a precise prediction. By deploying this method, we observe a 7.8% accuracy improvement in bigger LMs like Llama-3-70B compared to the vanilla on HybridQA, while smaller LMs like Llama-2-7B shows an 11% performance decline. We empirically investigate the potential causes of performance contrasts by exploring the capabilities of bigger and smaller LMs from various dimensions in TableQA task. Our findings highlight the limitations of the step-by-step reasoning method in small models and provide potential insights for making improvements.
Multi-ToM: Evaluating Multilingual Theory of Mind Capabilities in Large Language Models
Authors: Jayanta Sadhu, Ayan Antik Khan, Noshin Nawal, Sanju Basak, Abhik Bhattacharjee, Rifat Shahriyar
Date: 2024-11-24 00:00:00
Tasks: Attribute, Diversity

Theory of Mind (ToM) refers to the cognitive ability to infer and attribute mental states to oneself and others. As large language models (LLMs) are increasingly evaluated for social and cognitive capabilities, it remains unclear to what extent these models demonstrate ToM across diverse languages and cultural contexts. In this paper, we introduce a comprehensive study of multilingual ToM capabilities aimed at addressing this gap. Our approach includes two key components: (1) We translate existing ToM datasets into multiple languages, effectively creating a multilingual ToM dataset and (2) We enrich these translations with culturally specific elements to reflect the social and cognitive scenarios relevant to diverse populations. We conduct extensive evaluations of six state-of-the-art LLMs to measure their ToM performance across both the translated and culturally adapted datasets. The results highlight the influence of linguistic and cultural diversity on the models' ability to exhibit ToM, and questions their social reasoning capabilities. This work lays the groundwork for future research into enhancing LLMs' cross-cultural social cognition and contributes to the development of more culturally aware and socially intelligent AI systems. All our data and code are publicly available.
Investigating Factuality in Long-Form Text Generation: The Roles of Self-Known and Self-Unknown
Authors: Lifu Tu, Rui Meng, Shafiq Joty, Yingbo Zhou, Semih Yavuz
Date: 2024-11-24 00:00:00
Tasks: Form, Text Generation

Large language models (LLMs) have demonstrated strong capabilities in text understanding and generation. However, they often lack factuality, producing a mixture of true and false information, especially in long-form generation. In this work, we investigates the factuality of long-form text generation across various large language models (LLMs), including GPT-4, Gemini-1.5-Pro, Claude-3-Opus, Llama-3-70B, and Mistral. Our analysis reveals that factuality scores tend to decline in later sentences of the generated text, accompanied by a rise in the number of unsupported claims. Furthermore, we explore the effectiveness of different evaluation settings to assess whether LLMs can accurately judge the correctness of their own outputs: Self-Known (the percentage of supported atomic claims, decomposed from LLM outputs, that the corresponding LLMs judge as correct) and Self-Unknown (the percentage of unsupported atomic claims that the corresponding LLMs judge as incorrect). The results indicate that even advanced models like GPT-4 and Gemini-1.5-Pro fail to achieve perfect Self-Known scores, while their Self-Unknown scores remain notably above zero, reflecting ongoing uncertainty in their self-assessments. Moreover, we find a correlation between higher Self-Known scores and improved factuality, while higher Self-Unknown scores are associated with lower factuality. Interestingly, even without significant changes in the models' self-judgment (Self-Known and Self-Unknown), the number of unsupported claims can increases, likely as an artifact of long-form generation. These findings show the limitations of current LLMs in long-form generation, and provide valuable insights for improving factuality in long-form text generation.
Kleene algebra with commutativity conditions is undecidable
Authors: Arthur Azevedo de Amorim, Cheng Zhang, Marco Gaboardi
Date: 2024-11-24 00:00:00

We prove that the equational theory of Kleene algebra with commutativity conditions on primitives (or atomic terms) is undecidable, thereby settling a longstanding open question in the theory of Kleene algebra. While this question has also been recently solved independently by Kuznetsov, our results hold even for weaker theories that do not support the induction axioms of Kleene algebra.
Evaluating Large Language Models for Causal Modeling
Authors: Houssam Razouk, Leonie Benischke, Georg Niess, Roman Kern
Date: 2024-11-24 00:00:00

In this paper, we consider the process of transforming causal domain knowledge into a representation that aligns more closely with guidelines from causal data science. To this end, we introduce two novel tasks related to distilling causal domain knowledge into causal variables and detecting interaction entities using LLMs. We have determined that contemporary LLMs are helpful tools for conducting causal modeling tasks in collaboration with human experts, as they can provide a wider perspective. Specifically, LLMs, such as GPT-4-turbo and Llama3-70b, perform better in distilling causal domain knowledge into causal variables compared to sparse expert models, such as Mixtral-8x22b. On the contrary, sparse expert models such as Mixtral-8x22b stand out as the most effective in identifying interaction entities. Finally, we highlight the dependency between the domain where the entities are generated and the performance of the chosen LLM for causal modeling.
A Method for Building Large Language Models with Predefined KV Cache Capacity
Authors: Zhonghua Yi, Ge Niu, Lei Wang, Wei Tang, Liqiu Zhang
Date: 2024-11-24 00:00:00

This paper introduces a novel approach, the Bounded-Cache Transformer (BCT), for building large language models with a predefined Key-Value (KV) cache capacity. The BCT addresses the excessive memory consumption issue in traditional KV caches by implementing a bounded-length KV cache, which is particularly suitable for the attention layers in Transformer decode-only architectures. By dynamically updating the key-value vector sequences, the BCT achieves efficient inference within limited cache capacity, significantly reducing memory usage while maintaining model performance and system throughput. Experimental results demonstrate that the BCT significantly reduces memory usage while maintaining the model's inference quality, offering a new solution for efficient inference in large language models.
Detecting Turkish Synonyms Used in Different Time Periods
Authors: Umur Togay Yazar, Mucahid Kutlu
Date: 2024-11-24 00:00:00

Dynamic structure of languages poses significant challenges in applying natural language processing models on historical texts, causing decreased performance in various downstream tasks. Turkish is a prominent example of rapid linguistic transformation due to the language reform in the 20th century. In this paper, we propose two methods for detecting synonyms used in different time periods, focusing on Turkish. In our first method, we use Orthogonal Procrustes method to align the embedding spaces created using documents written in the corresponding time periods. In our second method, we extend the first one by incorporating Spearman's correlation between frequencies of words throughout the years. In our experiments, we show that our proposed methods outperform the baseline method. Furthermore, we observe that the efficacy of our methods remains consistent when the target time period shifts from the 1960s to the 1980s. However, their performance slightly decreases for subsequent time periods.
BiomedCoOp: Learning to Prompt for Biomedical Vision-Language Models
Authors: Taha Koleilat, Hojat Asgariandehkordi, Hassan Rivaz, Yiming Xiao
Date: 2024-11-21 00:00:00
Proceeding: CVPR 2025 1
Tasks: image-classification, Image Classification, Knowledge Distillation, Prompt Engineering, Prompt Learning, Representation Learning

Recent advancements in vision-language models (VLMs), such as CLIP, have demonstrated substantial success in self-supervised representation learning for vision tasks. However, effectively adapting VLMs to downstream applications remains challenging, as their accuracy often depends on time-intensive and expertise-demanding prompt engineering, while full model fine-tuning is costly. This is particularly true for biomedical images, which, unlike natural images, typically suffer from limited annotated datasets, unintuitive image contrasts, and nuanced visual features. Recent prompt learning techniques, such as Context Optimization (CoOp) intend to tackle these issues, but still fall short in generalizability. Meanwhile, explorations in prompt learning for biomedical image analysis are still highly limited. In this work, we propose BiomedCoOp, a novel prompt learning framework that enables efficient adaptation of BiomedCLIP for accurate and highly generalizable few-shot biomedical image classification. Our approach achieves effective prompt context learning by leveraging semantic consistency with average prompt ensembles from Large Language Models (LLMs) and knowledge distillation with a statistics-based prompt selection strategy. We conducted comprehensive validation of our proposed framework on 11 medical datasets across 9 modalities and 10 organs against existing state-of-the-art methods, demonstrating significant improvements in both accuracy and generalizability. The code will be publicly available at https://github.com/HealthX-Lab/BiomedCoOp.
Uni-Mlip: Unified Self-supervision for Medical Vision Language Pre-training
Authors: Ameera Bawazir, Kebin Wu, Wenbin Li
Date: 2024-11-20 00:00:00
Tasks: Contrastive Learning, image-classification, Image Classification, Image-text Retrieval, Question Answering, Retrieval, Text Retrieval, Visual Question Answering, Visual Question Answering (VQA)

Recent advancements in vision-language pre-training via contrastive learning have significantly improved performance across computer vision tasks. However, in the medical domain, obtaining multimodal data is often costly and challenging due to privacy, sensitivity, and annotation complexity. To mitigate data scarcity while boosting model performance, we introduce \textbf{Uni-Mlip}, a unified self-supervision framework specifically designed to enhance medical vision-language pre-training. Uni-Mlip seamlessly integrates cross-modality, uni-modality, and fused-modality self-supervision techniques at the data-level and the feature-level. Additionally, Uni-Mlip tailors uni-modal image self-supervision to accommodate the unique characteristics of medical images. Our experiments across datasets of varying scales demonstrate that Uni-Mlip significantly surpasses current state-of-the-art methods in three key downstream tasks: image-text retrieval, image classification, and visual question answering (VQA).
Multimodal large language model for wheat breeding: a new exploration of smart breeding
Authors: Guofeng Yang, Yu Li, Yong He, Zhenjiang Zhou, Lingzhen Ye, Hui Fang, Yiqi Luo, Xuping Feng
Date: 2024-11-20 00:00:00
Tasks: Language Modeling, Language Modelling, Large Language Model, Multimodal Large Language Model, RAG, Retrieval-augmented Generation

UAV remote sensing technology has become a key technology in crop breeding, which can achieve high-throughput and non-destructive collection of crop phenotyping data. However, the multidisciplinary nature of breeding has brought technical barriers and efficiency challenges to knowledge mining. Therefore, it is important to develop a smart breeding goal tool to mine cross-domain multimodal data. Based on different pre-trained open-source multimodal large language models (MLLMs) (e.g., Qwen-VL, InternVL, Deepseek-VL), this study used supervised fine-tuning (SFT), retrieval-augmented generation (RAG), and reinforcement learning from human feedback (RLHF) technologies to inject cross-domain knowledge into MLLMs, thereby constructing multiple multimodal large language models for wheat breeding (WBLMs). The above WBLMs were evaluated using the newly created evaluation benchmark in this study. The results showed that the WBLM constructed using SFT, RAG and RLHF technologies and InternVL2-8B has leading performance. Then, subsequent experiments were conducted using the WBLM. Ablation experiments indicated that the combination of SFT, RAG, and RLHF technologies can improve the overall generation performance, enhance the generated quality, balance the timeliness and adaptability of the generated answer, and reduce hallucinations and biases. The WBLM performed best in wheat yield prediction using cross-domain data (remote sensing, phenotyping, weather, germplasm) simultaneously, with R2 and RMSE of 0.821 and 489.254 kg/ha, respectively. Furthermore, the WBLM can generate professional decision support answers for phenotyping estimation, environmental stress assessment, target germplasm screening, cultivation technique recommendation, and seed price query tasks.
Graph Neural Network-Based Entity Extraction and Relationship Reasoning in Complex Knowledge Graphs
Authors: Junliang Du, Guiran Liu, Jia Gao, Xiaoxuan Liao, Jiacheng Hu, Linxiao Wu
Date: 2024-11-19 00:00:00
Tasks: Graph Attention, Graph Neural Network, Knowledge Graphs

This study proposed a knowledge graph entity extraction and relationship reasoning algorithm based on a graph neural network, using a graph convolutional network and graph attention network to model the complex structure in the knowledge graph. By building an end-to-end joint model, this paper achieves efficient recognition and reasoning of entities and relationships. In the experiment, this paper compared the model with a variety of deep learning algorithms and verified its superiority through indicators such as AUC, recall rate, precision rate, and F1 value. The experimental results show that the model proposed in this paper performs well in all indicators, especially in complex knowledge graphs, it has stronger generalization ability and stability. This provides strong support for further research on knowledge graphs and also demonstrates the application potential of graph neural networks in entity extraction and relationship reasoning.
Guiding Word Equation Solving using Graph Neural Networks (Extended Technical Report)
Authors: Parosh Aziz Abdulla, Mohamed Faouzi Atig, Julie Cailler, Chencheng Liang, Philipp Rümmer
Date: 2024-11-19 00:00:00
Tasks: Decision Making, Graph Neural Network

This paper proposes a Graph Neural Network-guided algorithm for solving word equations, based on the well-known Nielsen transformation for splitting equations. The algorithm iteratively rewrites the first terms of each side of an equation, giving rise to a tree-like search space. The choice of path at each split point of the tree significantly impacts solving time, motivating the use of Graph Neural Networks (GNNs) for efficient split decision-making. Split decisions are encoded as multi-classification tasks, and five graph representations of word equations are introduced to encode their structural information for GNNs. The algorithm is implemented as a solver named DragonLi. Experiments are conducted on artificial and real-world benchmarks. The algorithm performs particularly well on satisfiable problems. For single word \mbox{equations}, DragonLi can solve significantly more problems than well-established string solvers. For the conjunction of multiple word equations, DragonLi is competitive with state-of-the-art string solvers.