pwc-archive/papers-with-abstracts huggingface dataset:
ClusterTalk: Corpus Exploration Framework using Multi-Dimensional Exploratory Search
Authors: Ashish Chouhan, Saifeldin Mandour, Michael Gertz
Date: 2024-12-19 00:00:00
Tasks: Clustering

Exploratory search of large text corpora is essential in domains like biomedical research, where large amounts of research literature are continuously generated. This paper presents ClusterTalk (The demo video and source code are available at: https://github.com/achouhan93/ClusterTalk), a framework for corpus exploration using multi-dimensional exploratory search. Our system integrates document clustering with faceted search, allowing users to interactively refine their exploration and ask corpus and document-level queries. Compared to traditional one-dimensional search approaches like keyword search or clustering, this system improves the discoverability of information by encouraging a deeper interaction with the corpus. We demonstrate the functionality of the ClusterTalk framework based on four million PubMed abstracts for the four-year time frame.
Multi-Level Optimal Transport for Universal Cross-Tokenizer Knowledge Distillation on Language Models
Authors: Xiao Cui, Mo Zhu, Yulei Qin, Liang Xie, Wengang Zhou, Houqiang Li
Date: 2024-12-19 00:00:00
Tasks: Knowledge Distillation

Knowledge distillation (KD) has become a prevalent technique for compressing large language models (LLMs). Existing KD methods are constrained by the need for identical tokenizers (i.e., vocabularies) between teacher and student models, limiting their versatility in handling LLMs of different architecture families. In this paper, we introduce the Multi-Level Optimal Transport (MultiLevelOT), a novel approach that advances the optimal transport for universal cross-tokenizer knowledge distillation. Our method aligns the logit distributions of the teacher and the student at both token and sequence levels using diverse cost matrices, eliminating the need for dimensional or token-by-token correspondence. At the token level, MultiLevelOT integrates both global and local information by jointly optimizing all tokens within a sequence to enhance robustness. At the sequence level, we efficiently capture complex distribution structures of logits via the Sinkhorn distance, which approximates the Wasserstein distance for divergence measures. Extensive experiments on tasks such as extractive QA, generative QA, and summarization demonstrate that the MultiLevelOT outperforms state-of-the-art cross-tokenizer KD methods under various settings. Our approach is robust to different student and teacher models across model families, architectures, and parameter sizes. Codes and models are available at https://github.com/2018cx/Multi-Level-OT.
Why We Build Local Large Language Models: An Observational Analysis from 35 Japanese and Multilingual LLMs
Authors: Koshiro Saito, Sakae Mizuki, Masanari Ohi, Taishi Nakamura, Taihei Shiotani, Koki Maeda, Youmi Ma, Kakeru Hattori, Kazuki Fujii, Takumi Okamoto, Shigeki Ishida, Hiroya Takamura, Rio Yokota, Naoaki Okazaki
Date: 2024-12-19 00:00:00
Tasks: Arithmetic Reasoning, Code Generation, Question Answering, Reading Comprehension

Why do we build local large language models (LLMs)? What should a local LLM learn from the target language? Which abilities can be transferred from other languages? Do language-specific scaling laws exist? To explore these research questions, we evaluated 35 Japanese, English, and multilingual LLMs on 19 evaluation benchmarks for Japanese and English, taking Japanese as a local language. Adopting an observational approach, we analyzed correlations of benchmark scores, and conducted principal component analysis (PCA) on the scores to derive \textit{ability factors} of local LLMs. We found that training on English text can improve the scores of academic subjects in Japanese (JMMLU). In addition, it is unnecessary to specifically train on Japanese text to enhance abilities for solving Japanese code generation, arithmetic reasoning, commonsense, and reading comprehension tasks. In contrast, training on Japanese text could improve question-answering tasks about Japanese knowledge and English-Japanese translation, which indicates that abilities for solving these two tasks can be regarded as \textit{Japanese abilities} for LLMs. Furthermore, we confirmed that the Japanese abilities scale with the computational budget for Japanese text.
Agent-SafetyBench: Evaluating the Safety of LLM Agents
Authors: Zhexin Zhang, Shiyao Cui, Yida Lu, Jingzhuo Zhou, Junxiao Yang, Hongning Wang, Minlie Huang
Date: 2024-12-19 00:00:00

As large language models (LLMs) are increasingly deployed as agents, their integration into interactive environments and tool use introduce new safety challenges beyond those associated with the models themselves. However, the absence of comprehensive benchmarks for evaluating agent safety presents a significant barrier to effective assessment and further improvement. In this paper, we introduce Agent-SafetyBench, a comprehensive benchmark designed to evaluate the safety of LLM agents. Agent-SafetyBench encompasses 349 interaction environments and 2,000 test cases, evaluating 8 categories of safety risks and covering 10 common failure modes frequently encountered in unsafe interactions. Our evaluation of 16 popular LLM agents reveals a concerning result: none of the agents achieves a safety score above 60%. This highlights significant safety challenges in LLM agents and underscores the considerable need for improvement. Through failure mode and helpfulness analysis, we summarize two fundamental safety defects in current LLM agents: lack of robustness and lack of risk awareness. Furthermore, our findings suggest that reliance on defense prompts alone may be insufficient to address these safety issues, emphasizing the need for more advanced and robust strategies. To drive progress in this area, Agent-SafetyBench has been released at https://github.com/thu-coai/Agent-SafetyBench/ to facilitate further research in agent safety evaluation and improvement.
From Human Annotation to LLMs: SILICON Annotation Workflow for Management Research
Authors: Xiang Cheng, Raveesh Mayya, João Sedoc
Date: 2024-12-19 00:00:00
Tasks: Attribute, Management, Model Selection, Navigate, text annotation

Unstructured text data annotation and analysis are fundamental to management research, often relying on human annotators through crowdsourcing platforms. While Large Language Models (LLMs) promise to provide a cost-effective and efficient alternative to human annotation, there lacks a systematic workflow that evaluate when LLMs are suitable or how to proceed with LLM-based text annotation in a reproducible manner. This paper addresses this methodological gap by introducing the ``SILICON" (\textbf{S}ystematic \textbf{I}nference with \textbf{L}LMs for \textbf{I}nformation \textbf{C}lassificati\textbf{o}n and \textbf{N}otation) workflow. The workflow integrates established principles of human annotation with systematic prompt optimization and model selection, addressing challenges such as developing robust annotation guidelines, establishing high-quality human baselines, optimizing prompts, and ensuring reproducibility across LLMs. We validate the SILICON workflow through seven case studies covering common management research tasks, including business proposal evaluation, dialog intent and breakdown analysis, review attribute detection. Our findings highlight the importance of validating annotation guideline agreement, the superiority of expert-developed human baselines over crowdsourced ones, the iterative nature of prompt optimization, and the necessity of testing multiple LLMs. Notably, we propose a regression-based methodology to empirically compare LLM outputs across prompts and models. Our workflow advances management research by establishing reproducible processes for LLM-based annotation that maintain scientific rigor. We provide practical guidance for researchers to effectively navigate the evolving landscape of generative AI tools effectively while maintaining transparency and reproducibility.
Are Longer Prompts Always Better? Prompt Selection in Large Language Models for Recommendation Systems
Authors: Genki Kusano, Kosuke Akimoto, Kunihiro Takeoka
Date: 2024-12-19 00:00:00
Tasks: General Knowledge, Recommendation Systems

In large language models (LLM)-based recommendation systems (LLM-RSs), accurately predicting user preferences by leveraging the general knowledge of LLMs is possible without requiring extensive training data. By converting recommendation tasks into natural language inputs called prompts, LLM-RSs can efficiently solve issues that have been difficult to address due to data scarcity but are crucial in applications such as cold-start and cross-domain problems. However, when applying this in practice, selecting the prompt that matches tasks and data is essential. Although numerous prompts have been proposed in LLM-RSs and representing the target user in prompts significantly impacts recommendation accuracy, there are still no clear guidelines for selecting specific prompts. In this paper, we categorize and analyze prompts from previous research to establish practical prompt selection guidelines. Through 450 experiments with 90 prompts and five real-world datasets, we examined the relationship between prompts and dataset characteristics in recommendation accuracy. We found that no single prompt consistently outperforms others; thus, selecting prompts on the basis of dataset characteristics is crucial. Here, we propose a prompt selection method that achieves higher accuracy with minimal validation data. Because increasing the number of prompts to explore raises costs, we also introduce a cost-efficient strategy using high-performance and cost-efficient LLMs, significantly reducing exploration costs while maintaining high prediction accuracy. Our work offers valuable insights into the prompt selection, advancing accurate and efficient LLM-RSs.
In-Group Love, Out-Group Hate: A Framework to Measure Affective Polarization via Contentious Online Discussions
Authors: Buddhika Nettasinghe, Ashwin Rao, Bohan Jiang, Allon Percus, Kristina Lerman
Date: 2024-12-18 00:00:00
Tasks: Decision Making

Affective polarization, the emotional divide between ideological groups marked by in-group love and out-group hate, has intensified in the United States, driving contentious issues like masking and lockdowns during the COVID-19 pandemic. Despite its societal impact, existing models of opinion change fail to account for emotional dynamics nor offer methods to quantify affective polarization robustly and in real-time. In this paper, we introduce a discrete choice model that captures decision-making within affectively polarized social networks and propose a statistical inference method estimate key parameters -- in-group love and out-group hate -- from social media data. Through empirical validation from online discussions about the COVID-19 pandemic, we demonstrate that our approach accurately captures real-world polarization dynamics and explains the rapid emergence of a partisan gap in attitudes towards masking and lockdowns. This framework allows for tracking affective polarization across contentious issues has broad implications for fostering constructive online dialogues in digital spaces.
ECG-Byte: A Tokenizer for End-to-End Generative Electrocardiogram Language Modeling
Authors: William Han, Chaojing Duan, Michael A. Rosenberg, Emerson Liu, Ding Zhao
Date: 2024-12-18 00:00:00
Tasks: Language Modeling, Language Modelling, Self-Supervised Learning, Text Generation

Large Language Models (LLMs) have shown remarkable adaptability across domains beyond text, specifically electrocardiograms (ECGs). More specifically, there is a growing body of work exploring the task of generating text from a multi-channeled ECG and corresponding textual prompt. Current approaches typically involve pretraining an ECG-specific encoder with a self-supervised learning (SSL) objective and using the features output by the pretrained encoder to finetune a LLM for natural language generation (NLG). However, these methods are limited by 1) inefficiency from two-stage training and 2) interpretability challenges with encoder-generated features. To address these limitations, we introduce ECG-Byte, an adapted byte pair encoding (BPE) tokenizer pipeline for autoregressive language modeling of ECGs. This approach compresses and encodes ECG signals into tokens, enabling end-to-end LLM training by combining ECG and text tokens directly, while being much more interpretable since the ECG tokens can be directly mapped back to the original signal. Using ECG-Byte, we achieve competitive performance in NLG tasks in only half the time and ~48% of the data required by two-stage approaches.
Memorization Over Reasoning? Exposing and Mitigating Verbatim Memorization in Large Language Models' Character Understanding Evaluation
Authors: YuXuan Jiang, Francis Ferraro
Date: 2024-12-18 00:00:00
Tasks: Memorization

Recently, Large Language Models (LLMs) have shown impressive performance in character understanding tasks, such as analyzing the roles, personalities, and relationships of fictional characters. However, the extensive pre-training corpora used by LLMs raise concerns that they may rely on memorizing popular fictional works rather than genuinely understanding and reasoning about them. In this work, we argue that 'gist memory'-capturing essential meaning - should be the primary mechanism for character understanding tasks, as opposed to 'verbatim memory' - exact match of a string. We introduce a simple yet effective method to mitigate mechanized memorization in character understanding evaluations while preserving the essential implicit cues needed for comprehension and reasoning. Our approach reduces memorization-driven performance on popular fictional works from 96% accuracy to 72% and results in up to an 18% drop in accuracy across various character understanding tasks. These findings underscore the issue of data contamination in existing benchmarks, which often measure memorization rather than true character understanding.
State Space Models are Strong Text Rerankers
Authors: Zhichao Xu, Jinghua Yan, Ashim Gupta, Vivek Srikumar
Date: 2024-12-18 00:00:00
Tasks: Long-Context Understanding, Mamba, Reranking, State Space Models, Text Reranking

Transformers dominate NLP and IR; but their inference inefficiencies and challenges in extrapolating to longer contexts have sparked interest in alternative model architectures. Among these, state space models (SSMs) like Mamba offer promising advantages, particularly $O(1)$ time complexity in inference. Despite their potential, SSMs' effectiveness at text reranking -- a task requiring fine-grained query-document interaction and long-context understanding -- remains underexplored. This study benchmarks SSM-based architectures (specifically, Mamba-1 and Mamba-2) against transformer-based models across various scales, architectures, and pre-training objectives, focusing on performance and efficiency in text reranking tasks. We find that (1) Mamba architectures achieve competitive text ranking performance, comparable to transformer-based models of similar size; (2) they are less efficient in training and inference compared to transformers with flash attention; and (3) Mamba-2 outperforms Mamba-1 in both performance and efficiency. These results underscore the potential of state space models as a transformer alternative and highlight areas for improvement in future IR applications.
Event-based backpropagation on the neuromorphic platform SpiNNaker2
Authors: Gabriel Béna, Timo Wunderlich, Mahmoud Akl, Bernhard Vogginger, Christian Mayr, Hector Andres Gonzalez
Date: 2024-12-19 00:00:00

Neuromorphic computing aims to replicate the brain's capabilities for energy efficient and parallel information processing, promising a solution to the increasing demand for faster and more efficient computational systems. Efficient training of neural networks on neuromorphic hardware requires the development of training algorithms that retain the sparsity of spike-based communication during training. Here, we report on the first implementation of event-based backpropagation on the SpiNNaker2 neuromorphic hardware platform. We use EventProp, an algorithm for event-based backpropagation in spiking neural networks (SNNs), to compute exact gradients using sparse communication of error signals between neurons. Our implementation computes multi-layer networks of leaky integrate-and-fire neurons using discretized versions of the differential equations and their adjoints, and uses event packets to transmit spikes and error signals between network layers. We demonstrate a proof-of-concept of batch-parallelized, on-chip training of SNNs using the Yin Yang dataset, and provide an off-chip implementation for efficient prototyping, hyper-parameter search, and hybrid training methods.
Graph-neural-network predictions of solid-state NMR parameters from spherical tensor decomposition
Authors: Chiheb Ben Mahmoud, Louise A. M. Rosset, Jonathan R. Yates, Volker L. Deringer
Date: 2024-12-19 00:00:00
Tasks: Graph Neural Network, Tensor Decomposition

Nuclear magnetic resonance (NMR) is a powerful spectroscopic technique that is sensitive to the local atomic structure of matter. Computational predictions of NMR parameters can help to interpret experimental data and validate structural models, and machine learning (ML) has emerged as an efficient route to making such predictions. Here, we systematically study graph-neural-network approaches to representing and learning tensor quantities for solid-state NMR -- specifically, the anisotropic magnetic shielding and the electric field gradient. We assess how the numerical accuracy of different ML models translates into prediction quality for experimentally relevant NMR properties: chemical shifts, quadrupolar coupling constants, tensor orientations, and even static 1D spectra. We apply these ML models to a structurally diverse dataset of amorphous SiO$_2$ configurations, spanning a wide range of density and local order, to larger configurations beyond the reach of traditional first-principles methods, and to the dynamics of the $\alpha\unicode{x2013}\beta$ inversion in cristobalite. Our work marks a step toward streamlining ML-driven NMR predictions for both static and dynamic behavior of complex materials, and toward bridging the gap between first-principles modeling and real-world experimental data.
Joint Models for Handling Non-Ignorable Missing Data using Bayesian Additive Regression Trees: Application to Leaf Photosynthetic Traits Data
Authors: Yong Chen Goh, Wuu Kuang Soh, Andrew C. Parnell, Keefe Murphy
Date: 2024-12-19 00:00:00
Tasks: regression

Dealing with missing data poses significant challenges in predictive analysis, often leading to biased conclusions when oversimplified assumptions about the missing data process are made. In cases where the data are missing not at random (MNAR), jointly modeling the data and missing data indicators is essential. Motivated by a real data application with partially missing multivariate outcomes related to leaf photosynthetic traits and several environmental covariates, we propose two methods under a selection model framework for handling data with missingness in the response variables suitable for recovering various missingness mechanisms. Both approaches use a multivariate extension of Bayesian additive regression trees (BART) to flexibly model the outcomes. The first approach simultaneously uses a probit regression model to jointly model the missingness. In scenarios where the relationship between the missingness and the data is more complex or non-linear, we propose a second approach using a probit BART model to characterize the missing data process, thereby employing two BART models simultaneously. Both models also effectively handle ignorable covariate missingness. The efficacy of both models compared to existing missing data approaches is demonstrated through extensive simulations, in both univariate and multivariate settings, and through the aforementioned application to the leaf photosynthetic trait data.
Comparing noisy neural population dynamics using optimal transport distances
Authors: Amin Nejatbakhsh, Victor Geadah, Alex H. Williams, David Lipshutz
Date: 2024-12-19 00:00:00
Tasks: Gaussian Processes, Image Generation

Biological and artificial neural systems form high-dimensional neural representations that underpin their computational capabilities. Methods for quantifying geometric similarity in neural representations have become a popular tool for identifying computational principles that are potentially shared across neural systems. These methods generally assume that neural responses are deterministic and static. However, responses of biological systems, and some artificial systems, are noisy and dynamically unfold over time. Furthermore, these characteristics can have substantial influence on a system's computational capabilities. Here, we demonstrate that existing metrics can fail to capture key differences between neural systems with noisy dynamic responses. We then propose a metric for comparing the geometry of noisy neural trajectories, which can be derived as an optimal transport distance between Gaussian processes. We use the metric to compare models of neural responses in different regions of the motor system and to compare the dynamics of latent diffusion models for text-to-image synthesis.
Randomization Tests for Conditional Group Symmetry
Authors: Kenny Chiu, Alex Sharp, Benjamin Bloem-Reddy
Date: 2024-12-18 00:00:00

Symmetry plays a central role in the sciences, machine learning, and statistics. While statistical tests for the presence of distributional invariance with respect to groups have a long history, tests for conditional symmetry in the form of equivariance or conditional invariance are absent from the literature. This work initiates the study of nonparametric randomization tests for symmetry (invariance or equivariance) of a conditional distribution under the action of a specified locally compact group. We develop a general framework for randomization tests with finite-sample Type I error control and, using kernel methods, implement tests with finite-sample power lower bounds. We also describe and implement approximate versions of the tests, which are asymptotically consistent. We study their properties empirically on synthetic examples, and on applications to testing for symmetry in two problems from high-energy particle physics.
Long-time accuracy of ensemble Kalman filters for chaotic and machine-learned dynamical systems
Authors: Daniel Sanz-Alonso, Nathan Waniorek
Date: 2024-12-18 00:00:00

Filtering is concerned with online estimation of the state of a dynamical system from partial and noisy observations. In applications where the state is high dimensional, ensemble Kalman filters are often the method of choice. This paper establishes long-time accuracy of ensemble Kalman filters. We introduce conditions on the dynamics and the observations under which the estimation error remains small in the long-time horizon. Our theory covers a wide class of partially-observed chaotic dynamical systems, which includes the Navier-Stokes equations and Lorenz models. In addition, we prove long-time accuracy of ensemble Kalman filters with surrogate dynamics, thus validating the use of machine-learned forecast models in ensemble data assimilation.