pwc-archive/papers-with-abstracts huggingface dataset:
Investigating Gender Bias in Turkish Language Models
Authors: Orhun Caglidil, Malte Ostendorff, Georg Rehm
Date: 2024-04-17 00:00:00
Tasks: Attribute

Language models are trained mostly on Web data, which often contains social stereotypes and biases that the models can inherit. This has potentially negative consequences, as models can amplify these biases in downstream tasks or applications. However, prior research has primarily focused on the English language, especially in the context of gender bias. In particular, grammatically gender-neutral languages such as Turkish are underexplored despite representing different linguistic properties to language models with possibly different effects on biases. In this paper, we fill this research gap and investigate the significance of gender bias in Turkish language models. We build upon existing bias evaluation frameworks and extend them to the Turkish language by translating existing English tests and creating new ones designed to measure gender bias in the context of T\"urkiye. Specifically, we also evaluate Turkish language models for their embedded ethnic bias toward Kurdish people. Based on the experimental results, we attribute possible biases to different model characteristics such as the model size, their multilingualism, and the training corpora. We make the Turkish gender bias dataset publicly available.
How often are errors in natural language reasoning due to paraphrastic variability?
Authors: Neha Srikanth, Marine Carpuat, Rachel Rudinger
Date: 2024-04-17 00:00:00
Tasks: Natural Language Inference

Large language models have been shown to behave inconsistently in response to meaning-preserving paraphrastic inputs. At the same time, researchers evaluate the knowledge and reasoning abilities of these models with test evaluations that do not disaggregate the effect of paraphrastic variability on performance. We propose a metric for evaluating the paraphrastic consistency of natural language reasoning models based on the probability of a model achieving the same correctness on two paraphrases of the same problem. We mathematically connect this metric to the proportion of a model's variance in correctness attributable to paraphrasing. To estimate paraphrastic consistency, we collect ParaNLU, a dataset of 7,782 human-written and validated paraphrased reasoning problems constructed on top of existing benchmark datasets for defeasible and abductive natural language inference. Using ParaNLU, we measure the paraphrastic consistency of several model classes and show that consistency dramatically increases with pretraining but not finetuning. All models tested exhibited room for improvement in paraphrastic consistency.
Improvement in Semantic Address Matching using Natural Language Processing
Authors: Vansh Gupta, Mohit Gupta, Jai Garg, Nitesh Garg
Date: 2024-04-17 00:00:00
Tasks: Optical Character Recognition (OCR)

Address matching is an important task for many businesses especially delivery and take out companies which help them to take out a certain address from their data warehouse. Existing solution uses similarity of strings, and edit distance algorithms to find out the similar addresses from the address database, but these algorithms could not work effectively with redundant, unstructured, or incomplete address data. This paper discuss semantic Address matching technique, by which we can find out a particular address from a list of possible addresses. We have also reviewed existing practices and their shortcoming. Semantic address matching is an essentially NLP task in the field of deep learning. Through this technique We have the ability to triumph the drawbacks of existing methods like redundant or abbreviated data problems. The solution uses the OCR on invoices to extract the address and create the data pool of addresses. Then this data is fed to the algorithm BM-25 for scoring the best matching entries. Then to observe the best result, this will pass through BERT for giving the best possible result from the similar queries. Our investigation exhibits that our methodology enormously improves both accuracy and review of cutting-edge technology existing techniques.
How Well Can You Articulate that Idea? Insights from Automated Formative Assessment
Authors: Mahsa Sheikhi Karizaki, Dana Gnesdilow, Sadhana Puntambekar, Rebecca J. Passonneau
Date: 2024-04-17 00:00:00

Automated methods are becoming increasingly integrated into studies of formative feedback on students' science explanation writing. Most of this work, however, addresses students' responses to short answer questions. We investigate automated feedback on students' science explanation essays, where students must articulate multiple ideas. Feedback is based on a rubric that identifies the main ideas students are prompted to include in explanatory essays about the physics of energy and mass, given their experiments with a simulated roller coaster. We have found that students generally improve on revised versions of their essays. Here, however, we focus on two factors that affect the accuracy of the automated feedback. First, we find that the main ideas in the rubric differ with respect to how much freedom they afford in explanations of the idea, thus explanation of a natural law is relatively constrained. Students have more freedom in how they explain complex relations they observe in their roller coasters, such as transfer of different forms of energy. Second, by tracing the automated decision process, we can diagnose when a student's statement lacks sufficient clarity for the automated tool to associate it more strongly with one of the main ideas above all others. This in turn provides an opportunity for teachers and peers to help students reflect on how to state their ideas more clearly.
MemLLM: Finetuning LLMs to Use An Explicit Read-Write Memory
Authors: Ali Modarressi, Abdullatif Köksal, Ayyoob Imani, Mohsen Fayyaz, Hinrich Schütze
Date: 2024-04-17 00:00:00
Tasks: Hallucination, Language Modeling, Language Modelling, Memorization, Model Editing, RAG

While current large language models (LLMs) perform well on many knowledge-related tasks, they are limited by relying on their parameters as an implicit storage mechanism. As a result, they struggle with memorizing rare events and with updating their memory as facts change over time. In addition, the uninterpretable nature of parametric memory makes it challenging to prevent hallucination. Model editing and augmenting LLMs with parameters specialized for memory are only partial solutions. In this paper, we introduce MemLLM, a novel method of enhancing LLMs by integrating a structured and explicit read-and-write memory module. MemLLM tackles the aforementioned challenges by enabling dynamic interaction with the memory and improving the LLM's capabilities in using stored knowledge. Our experiments indicate that MemLLM enhances the LLM's performance and interpretability, in language modeling in general and knowledge-intensive tasks in particular. We see MemLLM as an important step towards making LLMs more grounded and factual through memory augmentation. The project repository is publicly available at https://github.com/amodaresi/MemLLM
How Population Diversity Influences the Efficiency of Crossover
Authors: Sacha Cerf, Johannes Lengler
Date: 2024-04-18 00:00:00
Tasks: Diversity

Our theoretical understanding of crossover is limited by our ability to analyze how population diversity evolves. In this study, we provide one of the first rigorous analyses of population diversity and optimization time in a setting where large diversity and large population sizes are required to speed up progress. We give a formal and general criterion which amount of diversity is necessary and sufficient to speed up the $(\mu+1)$ Genetic Algorithm on LeadingOnes. We show that the naturally evolving diversity falls short of giving a substantial speed-up for any $\mu=O(\sqrt{n}/\log^2 n)$. On the other hand, we show that even for $\mu=2$, if we simply break ties in favor of diversity then this increases diversity so much that optimization is accelerated by a constant factor.
Faster Optimization Through Genetic Drift
Authors: Cella Florescu, Marc Kaufmann, Johannes Lengler, Ulysse Schaller
Date: 2024-04-18 00:00:00

The compact Genetic Algorithm (cGA), parameterized by its hypothetical population size $K$, offers a low-memory alternative to evolving a large offspring population of solutions. It evolves a probability distribution, biasing it towards promising samples. For the classical benchmark OneMax, the cGA has to two different modes of operation: a conservative one with small step sizes $\Theta(1/(\sqrt{n}\log n))$, which is slow but prevents genetic drift, and an aggressive one with large step sizes $\Theta(1/\log n)$, in which genetic drift leads to wrong decisions, but those are corrected efficiently. On OneMax, an easy hill-climbing problem, both modes lead to optimization times of $\Theta(n\log n)$ and are thus equally efficient. In this paper we study how both regimes change when we replace OneMax by the harder hill-climbing problem DynamicBinVal. It turns out that the aggressive mode is not affected and still yields quasi-linear runtime $O(n\cdot polylog (n))$. However, the conservative mode becomes substantially slower, yielding a runtime of $\Omega(n^2)$, since genetic drift can only be avoided with smaller step sizes of $O(1/n)$. We complement our theoretical results with simulations.
Self-Adjusting Evolutionary Algorithms Are Slow on Multimodal Landscapes
Authors: Johannes Lengler, Konstantin Sturm
Date: 2024-04-18 00:00:00
Tasks: Evolutionary Algorithms

The one-fifth rule and its generalizations are a classical parameter control mechanism in discrete domains. They have also been transferred to control the offspring population size of the $(1, \lambda)$-EA. This has been shown to work very well for hill-climbing, and combined with a restart mechanism it was recently shown by Hevia Fajardo and Sudholt to improve performance on the multi-modal problem Cliff drastically. In this work we show that the positive results do not extend to other types of local optima. On the distorted OneMax benchmark, the self-adjusting $(1, \lambda)$-EA is slowed down just as elitist algorithms because self-adaptation prevents the algorithm from escaping from local optima. This makes the self-adaptive algorithm considerably worse than good static parameter choices, which do allow to escape from local optima efficiently. We show this theoretically and complement the result with empirical runtime results.
Analysis of Evolutionary Diversity Optimisation for the Maximum Matching Problem
Authors: Jonathan Gadea Harder, Aneta Neumann, Frank Neumann
Date: 2024-04-17 00:00:00
Tasks: Diversity, Evolutionary Algorithms

This paper explores the enhancement of solution diversity in evolutionary algorithms (EAs) for the maximum matching problem, concentrating on complete bipartite graphs and paths. We adopt binary string encoding for matchings and use Hamming distance to measure diversity, aiming for its maximization. Our study centers on the $(\mu+1)$-EA and $2P-EA_D$, which are applied to optimize diversity. We provide a rigorous theoretical and empirical analysis of these algorithms. For complete bipartite graphs, our runtime analysis shows that, with a reasonably small $\mu$, the $(\mu+1)$-EA achieves maximal diversity with an expected runtime of $O(\mu^2 m^4 \log(m))$ for the small gap case (where the population size $\mu$ is less than the difference in the sizes of the bipartite partitions) and $O(\mu^2 m^2 \log(m))$ otherwise. For paths, we establish an upper runtime bound of $O(\mu^3 m^3)$. The $2P-EA_D$ displays stronger performance, with bounds of $O(\mu^2 m^2 \log(m))$ for the small gap case, $O(\mu^2 n^2 \log(n))$ otherwise, and $O(\mu^3 m^2)$ for paths. Here, $n$ represents the total number of vertices and $m$ the number of edges. Our empirical studies, which examine the scaling behavior with respect to $m$ and $\mu$, complement these theoretical insights and suggest potential for further refinement of the runtime bounds.
Shotit: compute-efficient image-to-video search engine for the cloud
Authors: Leslie Wong
Date: 2024-04-18 00:00:00
Tasks: Image Retrieval

With the rapid growth of information technology, users are exposed to a massive amount of data online, including image, music, and video. This has led to strong needs to provide effective corresponsive search services such as image, music, and video search services. Most of them are operated based on keywords, namely using keywords to find related image, music, and video. Additionally, there are image-to-image search services that enable users to find similar images using one input image. Given that videos are essentially composed of image frames, then similar videos can be searched by one input image or screenshot. We want to target this scenario and provide an efficient method and implementation in this paper. We present Shotit, a cloud-native image-to-video search engine that tailors this search scenario in a compute-efficient approach. One main limitation faced in this scenario is the scale of its dataset. A typical image-to-image search engine only handles one-to-one relationships, colloquially, one image corresponds to another single image. But image-to-video proliferates. Take a 24-min length video as an example, it will generate roughly 20,000 image frames. As the number of videos grows, the scale of the dataset explodes exponentially. In this case, a compute-efficient approach ought to be considered, and the system design should cater to the cloud-native trend. Choosing an emerging technology - vector database as its backbone, Shotit fits these two metrics performantly. Experiments for two different datasets, a 50 thousand-scale Blender Open Movie dataset, and a 50 million-scale proprietary TV genre dataset at a 4 Core 32GB RAM Intel Xeon Gold 6271C cloud machine with object storage reveal the effectiveness of Shotit. A demo regarding the Blender Open Movie dataset is illustrated within this paper.
SIGformer: Sign-aware Graph Transformer for Recommendation
Authors: Sirui Chen, Jiawei Chen, Sheng Zhou, Bohao Wang, Shen Han, Chanfei Su, Yuqing Yuan, Can Wang
Date: 2024-04-18 00:00:00
Tasks: Recommendation Systems

In recommender systems, most graph-based methods focus on positive user feedback, while overlooking the valuable negative feedback. Integrating both positive and negative feedback to form a signed graph can lead to a more comprehensive understanding of user preferences. However, the existing efforts to incorporate both types of feedback are sparse and face two main limitations: 1) They process positive and negative feedback separately, which fails to holistically leverage the collaborative information within the signed graph; 2) They rely on MLPs or GNNs for information extraction from negative feedback, which may not be effective. To overcome these limitations, we introduce SIGformer, a new method that employs the transformer architecture to sign-aware graph-based recommendation. SIGformer incorporates two innovative positional encodings that capture the spectral properties and path patterns of the signed graph, enabling the full exploitation of the entire graph. Our extensive experiments across five real-world datasets demonstrate the superiority of SIGformer over state-of-the-art methods. The code is available at https://github.com/StupidThree/SIGformer.
A Fast Maximum Clique Algorithm Based on Network Decomposition for Large Sparse Networks
Authors: Tianlong Fan, Wenjun Jiang, Yi-Cheng Zhang, Linyuan Lü
Date: 2024-04-18 00:00:00

Finding maximum cliques in large networks is a challenging combinatorial problem with many real-world applications. We present a fast algorithm to achieve the exact solution for the maximum clique problem in large sparse networks based on efficient graph decomposition. A bunch of effective techniques is being used to greatly prune the graph and a novel concept called Complete-Upper-Bound-Induced Subgraph (CUBIS) is proposed to ensure that the structures with the potential to form the maximum clique are retained in the process of graph decomposition. Our algorithm first pre-prunes peripheral nodes, subsequently, one or two small-scale CUBISs are constructed guided by the core number and current maximum clique size. Bron-Kerbosch search is performed on each CUBIS to find the maximum clique. Experiments on 50 empirical networks with a scale of up to 20 million show the CUBIS scales are largely independent of the original network scale. This enables an approximately linear runtime, making our algorithm amenable for large networks. Our work provides a new framework for effectively solving maximum clique problems on massive sparse graphs, which not only makes the graph scale no longer the bottleneck but also shows some light on solving other clique-related problems.
Automated Similarity Metric Generation for Recommendation
Authors: Liang Qu, Yun Lin, Wei Yuan, Xiaojun Wan, Yuhui Shi, Hongzhi Yin
Date: 2024-04-18 00:00:00
Tasks: Recommendation Systems

The embedding-based architecture has become the dominant approach in modern recommender systems, mapping users and items into a compact vector space. It then employs predefined similarity metrics, such as the inner product, to calculate similarity scores between user and item embeddings, thereby guiding the recommendation of items that align closely with a user's preferences. Given the critical role of similarity metrics in recommender systems, existing methods mainly employ handcrafted similarity metrics to capture the complex characteristics of user-item interactions. Yet, handcrafted metrics may not fully capture the diverse range of similarity patterns that can significantly vary across different domains. To address this issue, we propose an Automated Similarity Metric Generation method for recommendations, named AutoSMG, which can generate tailored similarity metrics for various domains and datasets. Specifically, we first construct a similarity metric space by sampling from a set of basic embedding operators, which are then integrated into computational graphs to represent metrics. We employ an evolutionary algorithm to search for the optimal metrics within this metric space iteratively. To improve search efficiency, we utilize an early stopping strategy and a surrogate model to approximate the performance of candidate metrics instead of fully training models. Notably, our proposed method is model-agnostic, which can seamlessly plugin into different recommendation model architectures. The proposed method is validated on three public recommendation datasets across various domains in the Top-K recommendation task, and experimental results demonstrate that AutoSMG outperforms both commonly used handcrafted metrics and those generated by other search strategies.
Consolidating Ranking and Relevance Predictions of Large Language Models through Post-Processing
Authors: Le Yan, Zhen Qin, Honglei Zhuang, Rolf Jagerman, Xuanhui Wang, Michael Bendersky, Harrie Oosterhuis
Date: 2024-04-17 00:00:00

The powerful generative abilities of large language models (LLMs) show potential in generating relevance labels for search applications. Previous work has found that directly asking about relevancy, such as ``How relevant is document A to query Q?", results in sub-optimal ranking. Instead, the pairwise ranking prompting (PRP) approach produces promising ranking performance through asking about pairwise comparisons, e.g., ``Is document A more relevant than document B to query Q?". Thus, while LLMs are effective at their ranking ability, this is not reflected in their relevance label generation. In this work, we propose a post-processing method to consolidate the relevance labels generated by an LLM with its powerful ranking abilities. Our method takes both LLM generated relevance labels and pairwise preferences. The labels are then altered to satisfy the pairwise preferences of the LLM, while staying as close to the original values as possible. Our experimental results indicate that our approach effectively balances label accuracy and ranking performance. Thereby, our work shows it is possible to combine both the ranking and labeling abilities of LLMs through post-processing.
A Learning-to-Rank Formulation of Clustering-Based Approximate Nearest Neighbor Search
Authors: Thomas Vecchiato, Claudio Lucchese, Franco Maria Nardini, Sebastian Bruch
Date: 2024-04-17 00:00:00
Tasks: Clustering, Information Retrieval, Learning-To-Rank

A critical piece of the modern information retrieval puzzle is approximate nearest neighbor search. Its objective is to return a set of $k$ data points that are closest to a query point, with its accuracy measured by the proportion of exact nearest neighbors captured in the returned set. One popular approach to this question is clustering: The indexing algorithm partitions data points into non-overlapping subsets and represents each partition by a point such as its centroid. The query processing algorithm first identifies the nearest clusters -- a process known as routing -- then performs a nearest neighbor search over those clusters only. In this work, we make a simple observation: The routing function solves a ranking problem. Its quality can therefore be assessed with a ranking metric, making the function amenable to learning-to-rank. Interestingly, ground-truth is often freely available: Given a query distribution in a top-$k$ configuration, the ground-truth is the set of clusters that contain the exact top-$k$ vectors. We develop this insight and apply it to Maximum Inner Product Search (MIPS). As we demonstrate empirically on various datasets, learning a simple linear function consistently improves the accuracy of clustering-based MIPS.
Internet sentiment exacerbates intraday overtrading, evidence from A-Share market
Authors: Peng Yifeng
Date: 2024-04-18 00:00:00

Market fluctuations caused by overtrading are important components of systemic market risk. This study examines the effect of investor sentiment on intraday overtrading activities in the Chinese A-share market. Employing high-frequency sentiment indices inferred from social media posts on the Eastmoney forum Guba, the research focuses on constituents of the CSI 300 and CSI 500 indices over a period from 01/01/2018, to 12/30/2022. The empirical analysis indicates that investor sentiment exerts a significantly positive impact on intraday overtrading, with the influence being more pronounced among institutional investors relative to individual traders. Moreover, sentiment-driven overtrading is found to be more prevalent during bull markets as opposed to bear markets. Additionally, the effect of sentiment on overtrading is observed to be more pronounced among individual investors in large-cap stocks compared to small- and mid-cap stocks.