pwc-archive/papers-with-abstracts huggingface dataset:
Any Other Thoughts, Hedgehog? Linking Deliberation Chains in Collaborative Dialogues
Authors: Abhijnan Nath, Videep Venkatesha, Mariah Bradford, Avyakta Chelle, Austin Youngren, Carlos Mabrey, Nathaniel Blanchard, Nikhil Krishnaswamy
Date: 2024-10-25 00:00:00

Question-asking in collaborative dialogue has long been established as key to knowledge construction, both in internal and collaborative problem solving. In this work, we examine probing questions in collaborative dialogues: questions that explicitly elicit responses from the speaker's interlocutors. Specifically, we focus on modeling the causal relations that lead directly from utterances earlier in the dialogue to the emergence of the probing question. We model these relations using a novel graph-based framework of deliberation chains, and reframe the problem of constructing such chains as a coreference-style clustering problem. Our framework jointly models probing and causal utterances and the links between them, and we evaluate on two challenging collaborative task datasets: the Weights Task and DeliData. Our results demonstrate the effectiveness of our theoretically-grounded approach compared to both baselines and stronger coreference approaches, and establish a standard of performance in this novel task.
Fictitious Synthetic Data Can Improve LLM Factuality via Prerequisite Learning
Authors: Yujian Liu, Shiyu Chang, Tommi Jaakkola, Yang Zhang
Date: 2024-10-25 00:00:00

Recent studies have identified one aggravating factor of LLM hallucinations as the knowledge inconsistency between pre-training and fine-tuning, where unfamiliar fine-tuning data mislead the LLM to fabricate plausible but wrong outputs. In this paper, we propose a novel fine-tuning strategy called Prereq-Tune to address this knowledge inconsistency and reduce hallucinations. Fundamentally, Prereq-Tune disentangles the learning of skills and knowledge, so the model learns only the task skills without being impacted by the knowledge inconsistency. To achieve this, Prereq-Tune introduces an additional prerequisite learning stage to learn the necessary knowledge for SFT, allowing subsequent SFT to focus only on task skills. Prereq-Tune can also be combined with fictitious synthetic data to enhance the grounding of LLM outputs to their internal knowledge. Experiments show that Prereq-Tune outperforms existing baselines in improving LLM's factuality across short QA and long-form generation tasks. It also opens new possibilities for knowledge-controlled generation in LLMs. Our code is available at https://github.com/UCSB-NLP-Chang/Prereq_tune.git.
Have LLMs Reopened the Pandora's Box of AI-Generated Fake News?
Authors: Xinyu Wang, Wenbo Zhang, Sai Koneru, Hangzhi Guo, Bonam Mingole, S. Shyam Sundar, Sarah Rajtmajer, Amulya Yadav
Date: 2024-10-25 00:00:00
Tasks: Fake News Detection

With the rise of AI-generated content spewed at scale from large language models (LLMs), genuine concerns about the spread of fake news have intensified. The perceived ability of LLMs to produce convincing fake news at scale poses new challenges for both human and automated fake news detection systems. To address this gap, this paper presents the findings from a university-level competition that aimed to explore how LLMs can be used by humans to create fake news, and to assess the ability of human annotators and AI models to detect it. A total of 110 participants used LLMs to create 252 unique fake news stories, and 84 annotators participated in the detection tasks. Our findings indicate that LLMs are ~68% more effective at detecting real news than humans. However, for fake news detection, the performance of LLMs and humans remains comparable (~60% accuracy). Additionally, we examine the impact of visual elements (e.g., pictures) in news on the accuracy of detecting fake news stories. Finally, we also examine various strategies used by fake news creators to enhance the credibility of their AI-generated content. This work highlights the increasing complexity of detecting AI-generated fake news, particularly in collaborative human-AI settings.
Making Social Platforms Accessible: Emotion-Aware Speech Generation with Integrated Text Analysis
Authors: Suparna De, Ionut Bostan, Nishanth Sastry
Date: 2024-10-24 00:00:00
Tasks: Speech Synthesis, text-to-speech, Text to Speech

Recent studies have outlined the accessibility challenges faced by blind or visually impaired, and less-literate people, in interacting with social networks, in-spite of facilitating technologies such as monotone text-to-speech (TTS) screen readers and audio narration of visual elements such as emojis. Emotional speech generation traditionally relies on human input of the expected emotion together with the text to synthesise, with additional challenges around data simplification (causing information loss) and duration inaccuracy, leading to lack of expressive emotional rendering. In real-life communications, the duration of phonemes can vary since the same sentence might be spoken in a variety of ways depending on the speakers' emotional states or accents (referred to as the one-to-many problem of text to speech generation). As a result, an advanced voice synthesis system is required to account for this unpredictability. We propose an end-to-end context-aware Text-to-Speech (TTS) synthesis system that derives the conveyed emotion from text input and synthesises audio that focuses on emotions and speaker features for natural and expressive speech, integrating advanced natural language processing (NLP) and speech synthesis techniques for real-time applications. Our system also showcases competitive inference time performance when benchmarked against the state-of-the-art TTS models, making it suitable for real-time accessibility applications.
Label Set Optimization via Activation Distribution Kurtosis for Zero-shot Classification with Generative Models
Authors: Yue Li, Zhixue Zhao, Carolina Scarton
Date: 2024-10-24 00:00:00
Tasks: Classification, In-Context Learning, Stance Classification, zero-shot-classification, Zero-Shot Learning

In-context learning (ICL) performance is known to be sensitive to the prompt design, yet the impact of class label options in zero-shot classification has been largely overlooked. This study presents the first comprehensive empirical study investigating how label option (e.g., lexical choice, order, and elaboration) influences zero-shot ICL classification performance. Our findings reveal that lexical choices for label names (e.g., agree vs.support in stance classification) play an important role, with effects also linked to label orders. An analysis of the model internal states further shows that optimal label names tend to activate fewer outlier neurons in the feed forward network. Based on this observation, we propose Label set Optimization via Activation Distribution kurtosiS (LOADS), a post-hoc approach requiring no gradient propagation. LOADS not only demonstrates effectiveness with only 100 unlabelled samples across different model types and sizes, but also shows cross-lingual transferability.
A Test of Time: Predicting the Sustainable Success of Online Collaboration in Wikipedia
Authors: Abraham Israeli, David Jurgens, Daniel Romero
Date: 2024-10-24 00:00:00
Tasks: Articles

The Internet has significantly expanded the potential for global collaboration, allowing millions of users to contribute to collective projects like Wikipedia. While prior work has assessed the success of online collaborations, most approaches are time-agnostic, evaluating success without considering its longevity. Research on the factors that ensure the long-term preservation of high-quality standards in online collaboration is scarce. In this study, we address this gap. We propose a novel metric, `Sustainable Success,' which measures the ability of collaborative efforts to maintain their quality over time. Using Wikipedia as a case study, we introduce the SustainPedia dataset, which compiles data from over 40K Wikipedia articles, including each article's sustainable success label and more than 300 explanatory features such as edit history, user experience, and team composition. Using this dataset, we develop machine learning models to predict the sustainable success of Wikipedia articles. Our best-performing model achieves a high AU-ROC score of 0.88 on average. Our analysis reveals important insights. For example, we find that the longer an article takes to be recognized as high-quality, the more likely it is to maintain that status over time (i.e., be sustainable). Additionally, user experience emerged as the most critical predictor of sustainability. Our analysis provides insights into broader collective actions beyond Wikipedia (e.g., online activism, crowdsourced open-source software), where the same social dynamics that drive success on Wikipedia might play a role. We make all data and code used for this study publicly available for further research.
AlignCap: Aligning Speech Emotion Captioning to Human Preferences
Authors: ZiQi Liang, Haoxiang Shi, Hanhui Chen
Date: 2024-10-24 00:00:00
Tasks: Knowledge Distillation, Language Modeling, Language Modelling, Large Language Model

Speech Emotion Captioning (SEC) has gradually become an active research task. The emotional content conveyed through human speech are often complex, and classifying them into fixed categories may not be enough to fully capture speech emotions. Describing speech emotions through natural language may be a more effective approach. However, existing SEC methods often produce hallucinations and lose generalization on unseen speech. To overcome these problems, we propose AlignCap, which Aligning Speech Emotion Captioning to Human Preferences based on large language model (LLM) with two properties: 1) Speech-Text Alignment, which minimizing the divergence between the LLM's response prediction distributions for speech and text inputs using knowledge distillation (KD) Regularization. 2) Human Preference Alignment, where we design Preference Optimization (PO) Regularization to eliminate factuality and faithfulness hallucinations. We also extract emotional clues as a prompt for enriching fine-grained information under KD-Regularization. Experiments demonstrate that AlignCap presents stronger performance to other state-of-the-art methods on Zero-shot SEC task.
Hybrid Preferences: Learning to Route Instances for Human vs. AI Feedback
Authors: Lester James V. Miranda, Yizhong Wang, Yanai Elazar, Sachin Kumar, Valentina Pyatkin, Faeze Brahman, Noah A. Smith, Hannaneh Hajishirzi, Pradeep Dasigi
Date: 2024-10-24 00:00:00

Learning from human feedback has enabled the alignment of language models (LMs) with human preferences. However, directly collecting human preferences can be expensive, time-consuming, and can have high variance. An appealing alternative is to distill preferences from LMs as a source of synthetic annotations as they are more consistent, cheaper, and scale better than human annotation; however, they are also prone to biases and errors. In this work, we introduce a routing framework that combines inputs from humans and LMs to achieve better annotation quality, while reducing the total cost of human annotation. The crux of our approach is to identify preference instances that will benefit from human annotations. We formulate this as an optimization problem: given a preference dataset and an evaluation metric, we train a performance prediction model to predict a reward model's performance on an arbitrary combination of human and LM annotations and employ a routing strategy that selects a combination that maximizes predicted performance. We train the performance prediction model on MultiPref, a new preference dataset with 10K instances paired with human and LM labels. We show that the selected hybrid mixture of LM and direct human preferences using our routing framework achieves better reward model performance compared to using either one exclusively. We simulate selective human preference collection on three other datasets and show that our method generalizes well to all three. We analyze features from the routing model to identify characteristics of instances that can benefit from human feedback, e.g., prompts with a moderate safety concern or moderate intent complexity. We release the dataset, annotation platform, and source code used in this study to foster more efficient and accurate preference collection in the future.
Retrieving Implicit and Explicit Emotional Events Using Large Language Models
Authors: Guimin Hu, Hasti Seifi
Date: 2024-10-24 00:00:00
Tasks: Diversity, Retrieval

Large language models (LLMs) have garnered significant attention in recent years due to their impressive performance. While considerable research has evaluated these models from various perspectives, the extent to which LLMs can perform implicit and explicit emotion retrieval remains largely unexplored. To address this gap, this study investigates LLMs' emotion retrieval capabilities in commonsense. Through extensive experiments involving multiple models, we systematically evaluate the ability of LLMs on emotion retrieval. Specifically, we propose a supervised contrastive probing method to verify LLMs' performance for implicit and explicit emotion retrieval, as well as the diversity of the emotional events they retrieve. The results offer valuable insights into the strengths and limitations of LLMs in handling emotion retrieval.
Watermarking Large Language Models and the Generated Content: Opportunities and Challenges
Authors: Ruisi Zhang, Farinaz Koushanfar
Date: 2024-10-24 00:00:00
Tasks: Code Generation, Misinformation

The widely adopted and powerful generative large language models (LLMs) have raised concerns about intellectual property rights violations and the spread of machine-generated misinformation. Watermarking serves as a promising approch to establish ownership, prevent unauthorized use, and trace the origins of LLM-generated content. This paper summarizes and shares the challenges and opportunities we found when watermarking LLMs. We begin by introducing techniques for watermarking LLMs themselves under different threat models and scenarios. Next, we investigate watermarking methods designed for the content generated by LLMs, assessing their effectiveness and resilience against various attacks. We also highlight the importance of watermarking domain-specific models and data, such as those used in code generation, chip design, and medical applications. Furthermore, we explore methods like hardware acceleration to improve the efficiency of the watermarking process. Finally, we discuss the limitations of current approaches and outline future research directions for the responsible use and protection of these generative AI tools.
GCoder: Improving Large Language Model for Generalized Graph Problem Solving
Authors: Qifan Zhang, Xiaobin Hong, Jianheng Tang, Nuo Chen, Yuhan Li, Wenzhong Li, Jing Tang, Jia Li
Date: 2024-10-24 00:00:00
Tasks: Language Modeling, Language Modelling, Large Language Model

Large Language Models (LLMs) have demonstrated strong reasoning abilities, making them suitable for complex tasks such as graph computation. Traditional reasoning steps paradigm for graph problems is hindered by unverifiable steps, limited long-term reasoning, and poor generalization to graph variations. To overcome these limitations, we introduce GCoder, a code-based LLM designed to enhance problem-solving in generalized graph computation problems. Our method involves constructing an extensive training dataset, GraphWild, featuring diverse graph formats and algorithms. We employ a multi-stage training process, including Supervised Fine-Tuning (SFT) and Reinforcement Learning from Compiler Feedback (RLCF), to refine model capabilities. For unseen tasks, a hybrid retrieval technique is used to augment performance. Experiments demonstrate that GCoder outperforms GPT-4o, with an average accuracy improvement of 16.42% across various graph computational problems. Furthermore, GCoder efficiently manages large-scale graphs with millions of nodes and diverse input formats, overcoming the limitations of previous models focused on the reasoning steps paradigm. This advancement paves the way for more intuitive and effective graph problem-solving using LLMs. Code and data are available at here: https://github.com/Bklight999/WWW25-GCoder/tree/master.
Optimizing Hearthstone Agents using an Evolutionary Algorithm
Authors: Pablo García-Sánchez, Alberto Tonda, Antonio J. Fernández-Leiva, Carlos Cotta
Date: 2024-10-25 00:00:00
Tasks: Card Games, Decision Making, Evolutionary Algorithms, Self-Learning

Digital collectible card games are not only a growing part of the video game industry, but also an interesting research area for the field of computational intelligence. This game genre allows researchers to deal with hidden information, uncertainty and planning, among other aspects. This paper proposes the use of evolutionary algorithms (EAs) to develop agents who play a card game, Hearthstone, by optimizing a data-driven decision-making mechanism that takes into account all the elements currently in play. Agents feature self-learning by means of a competitive coevolutionary training approach, whereby no external sparring element defined by the user is required for the optimization process. One of the agents developed through the proposed approach was runner-up (best 6%) in an international Hearthstone Artificial Intelligence (AI) competition. Our proposal performed remarkably well, even when it faced state-of-the-art techniques that attempted to take into account future game states, such as Monte-Carlo Tree search. This outcome shows how evolutionary computation could represent a considerable advantage in developing AIs for collectible card games such as Hearthstone.
Hybrid Memetic Search for Electric Vehicle Routing with Time Windows, Simultaneous Pickup-Delivery, and Partial Recharges
Authors: Zubin Zheng, Shengcai Liu, Yew-Soon Ong
Date: 2024-10-25 00:00:00

With growing environmental concerns, electric vehicles for logistics have gained significant attention within the computational intelligence community in recent years. This work addresses an emerging and significant extension of the electric vehicle routing problem (EVRP), namely EVRP with time windows, simultaneous pickup-delivery, and partial recharges (EVRP-TW-SPD), which has wide real-world applications. We propose a hybrid memetic algorithm (HMA) for solving EVRP-TW-SPD. HMA incorporates two novel components: a parallel-sequential station insertion procedure for handling partial recharges that can better avoid local optima compared to purely sequential insertion, and a cross-domain neighborhood search that explores solution spaces of both electric and non-electric problem domains simultaneously. These components can also be easily applied to various EVRP variants. To bridge the gap between existing benchmarks and real-world scenarios, we introduce a new, large-scale EVRP-TW-SPD benchmark set derived from real-world applications, containing instances with many more customers and charging stations than existing benchmark instances. Extensive experiments demonstrate the significant performance advantages of HMA over existing algorithms across a wide range of problem instances. Both the benchmark set and HMA will be open-sourced to facilitate further research in this area.
Non-Dominated Sorting Bidirectional Differential Coevolution
Authors: Cicero S. R. Mendes, Aluizio F. R. Araújo, Lucas R. C. Farias
Date: 2024-10-25 00:00:00
Tasks: Multiobjective Optimization

Constrained multiobjective optimization problems (CMOPs) are commonly found in real-world applications. CMOP is a complex problem that needs to satisfy a set of equality or inequality constraints. This paper proposes a variant of the bidirectional coevolution algorithm (BiCo) with differential evolution (DE). The novelties in the model include the DE differential mutation and crossover operators as the main search engine and a non-dominated sorting selection scheme. Experimental results on two benchmark test suites and eight real-world CMOPs suggested that the proposed model reached better overall performance than the original model.
Evolving choice hysteresis in reinforcement learning: comparing the adaptive value of positivity bias and gradual perseveration
Authors: Isabelle Hoxha, Leo Sperber, Stefano Palminteri
Date: 2024-10-25 00:00:00
Tasks: Descriptive, reinforcement-learning, Reinforcement Learning

The tendency of repeating past choices more often than expected from the history of outcomes has been repeatedly empirically observed in reinforcement learning experiments. It can be explained by at least two computational processes: asymmetric update and (gradual) choice perseveration. A recent meta-analysis showed that both mechanisms are detectable in human reinforcement learning. However, while their descriptive value seems to be well established, they have not been compared regarding their possible adaptive value. In this study, we address this gap by simulating reinforcement learning agents in a variety of environments with a new variant of an evolutionary algorithm. Our results show that positivity bias (in the form of asymmetric update) is evolutionary stable in many situations, while the emergence of gradual perseveration is less systematic and robust. Overall, our results illustrate that biases can be adaptive and selected by evolution, in an environment-specific manner.
Genetic Motifs as a Blueprint for Mismatch-Tolerant Neuromorphic Computing
Authors: Tommaso Boccato, Dmitrii Zendrikov, Nicola Toschi, Giacomo Indiveri
Date: 2024-10-25 00:00:00
Tasks: Edge-computing

Mixed-signal implementations of SNNs offer a promising solution to edge computing applications that require low-power and compact embedded processing systems. However, device mismatch in the analog circuits of these neuromorphic processors poses a significant challenge to the deployment of robust processing in these systems. Here we introduce a novel architectural solution inspired by biological development to address this issue. Specifically we propose to implement architectures that incorporate network motifs found in developed brains through a differentiable re-parameterization of weight matrices based on gene expression patterns and genetic rules. Thanks to the gradient descent optimization compatibility of the method proposed, we can apply the robustness of biological neural development to neuromorphic computing. To validate this approach we benchmark it using the Yin-Yang classification dataset, and compare its performance with that of standard multilayer perceptrons trained with state-of-the-art hardware-aware training method. Our results demonstrate that the proposed method mitigates mismatch-induced noise without requiring precise device mismatch measurements, effectively outperforming alternative hardware-aware techniques proposed in the literature, and providing a more general solution for improving the robustness of SNNs in neuromorphic hardware.
How high dimensional neural dynamics are confined in phase space
Authors: Shishe Wang, Haiping Huang
Date: 2024-10-25 00:00:00
Tasks: Diversity

High dimensional dynamics play a vital role in brain function, ecological systems, and neuro-inspired machine learning. Where and how these dynamics are confined in the phase space remains challenging to solve. Here, we provide an analytic argument that the confinement region is an M-shape when the neural dynamics show a diversity, with two sharp boundaries and a flat low-density region in between. Despite increasing synaptic strengths in a neural circuit, the shape remains qualitatively the same, while the left boundary is continuously pushed away. However, in deep chaotic regions, an arch-shaped confinement gradually emerges. Our theory is supported by numerical simulations on finite-sized networks. This analytic theory opens up a geometric route towards addressing fundamental questions about high dimensional non-equilibrium dynamics.