Large Language Models have emerged as prime candidates to tackle misinformation mitigation. However, existing approaches struggle with hallucinations and overconfident predictions. We propose an uncertainty quantification framework that leverages both direct confidence elicitation and sampled-based consistency methods to provide better calibration for NLP misinformation mitigation solutions. We first investigate the calibration of sample-based consistency methods that exploit distinct features of consistency across sample sizes and stochastic levels. Next, we evaluate the performance and distributional shift of a robust numeric verbalization prompt across single vs. two-step confidence elicitation procedure. We also compare the performance of the same prompt with different versions of GPT and different numerical scales. Finally, we combine the sample-based consistency and verbalized methods to propose a hybrid framework that yields a better uncertainty estimation for GPT models. Overall, our work proposes novel uncertainty quantification methods that will improve the reliability of Large Language Models in misinformation mitigation applications.

The Segment Anything Model (SAM) has shown impressive performance when applied to natural image segmentation. However, it struggles with geographical images like aerial and satellite imagery, especially when segmenting mobility infrastructure including roads, sidewalks, and crosswalks. This inferior performance stems from the narrow features of these objects, their textures blending into the surroundings, and interference from objects like trees, buildings, vehicles, and pedestrians - all of which can disorient the model to produce inaccurate segmentation maps. To address these challenges, we propose Geographical SAM (GeoSAM), a novel SAM-based framework that implements a fine-tuning strategy using the dense visual prompt from zero-shot learning, and the sparse visual prompt from a pre-trained CNN segmentation model. The proposed GeoSAM outperforms existing approaches for geographical image segmentation, specifically by 26%, 7%, and 17% for road infrastructure, pedestrian infrastructure, and on average, respectively, representing a momentous leap in leveraging foundation models to segment mobility infrastructure including both road and pedestrian infrastructure in geographical images. The source code can be found on this GitHub repository: https://github.com/rafiibnsultan/GeoSAM/tree/main.

Named Entity Recognition (NER) is a sub-task of Natural Language Processing (NLP) that distinguishes entities from unorganized text into predefined categorization. In recent years, a lot of Bangla NLP subtasks have received quite a lot of attention; but Named Entity Recognition in Bangla still lags behind. In this research, we explored the existing state of research in Bangla Named Entity Recognition. We tried to figure out the limitations that current techniques and datasets face, and we would like to address these limitations in our research. Additionally, We developed a Gazetteer that has the ability to significantly boost the performance of NER. We also proposed a new NER solution by taking advantage of state-of-the-art NLP tools that outperform conventional techniques.

In text classification, creating an adversarial example means subtly perturbing a few words in a sentence without changing its meaning, causing it to be misclassified by a classifier. A concerning observation is that a significant portion of adversarial examples generated by existing methods change only one word. This single-word perturbation vulnerability represents a significant weakness in classifiers, which malicious users can exploit to efficiently create a multitude of adversarial examples. This paper studies this problem and makes the following key contributions: (1) We introduce a novel metric \r{ho} to quantitatively assess a classifier's robustness against single-word perturbation. (2) We present the SP-Attack, designed to exploit the single-word perturbation vulnerability, achieving a higher attack success rate, better preserving sentence meaning, while reducing computation costs compared to state-of-the-art adversarial methods. (3) We propose SP-Defense, which aims to improve \r{ho} by applying data augmentation in learning. Experimental results on 4 datasets and BERT and distilBERT classifiers show that SP-Defense improves \r{ho} by 14.6% and 13.9% and decreases the attack success rate of SP-Attack by 30.4% and 21.2% on two classifiers respectively, and decreases the attack success rate of existing attack methods that involve multiple-word perturbations.

Singing voice conversion (SVC) automates song covers by converting one singer's singing voice into another target singer's singing voice with the original lyrics and melody. However, it raises serious concerns about copyright and civil right infringements to multiple entities. This work proposes SongBsAb, the first proactive approach to mitigate unauthorized SVC-based illegal song covers. SongBsAb introduces human-imperceptible perturbations to singing voices before releasing them, so that when they are used, the generation process of SVC will be interfered, resulting in unexpected singing voices. SongBsAb features a dual prevention effect by causing both (singer) identity disruption and lyric disruption, namely, the SVC-covered singing voice neither imitates the target singer nor preserves the original lyrics. To improve the imperceptibility of perturbations, we refine a psychoacoustic model-based loss with the backing track as an additional masker, a unique accompanying element for singing voices compared to ordinary speech voices. To enhance the transferability, we propose to utilize a frame-level interaction reduction-based loss. We demonstrate the prevention effectiveness, utility, and robustness of SongBsAb on three SVC models and two datasets using both objective and human study-based subjective metrics. Our work fosters an emerging research direction for mitigating illegal automated song covers.

This work delves into the expanding role of large language models (LLMs) in generating artificial data. LLMs are increasingly employed to create a variety of outputs, including annotations, preferences, instruction prompts, simulated dialogues, and free text. As these forms of LLM-generated data often intersect in their application, they exert mutual influence on each other and raise significant concerns about the quality and diversity of the artificial data incorporated into training cycles, leading to an artificial data ecosystem. To the best of our knowledge, this is the first study to aggregate various types of LLM-generated text data, from more tightly constrained data like "task labels" to more lightly constrained "free-form text". We then stress test the quality and implications of LLM-generated artificial data, comparing it with human data across various existing benchmarks. Despite artificial data's capability to match human performance, this paper reveals significant hidden disparities, especially in complex tasks where LLMs often miss the nuanced understanding of intrinsic human-generated content. This study critically examines diverse LLM-generated data and emphasizes the need for ethical practices in data creation and when using LLMs. It highlights the LLMs' shortcomings in replicating human traits and behaviors, underscoring the importance of addressing biases and artifacts produced in LLM-generated content for future research and development. All data and code are available on our project page.

Creativity is core to being human. Generative artificial intelligence (GenAI) -- including ever more powerful large language models (LLMs) -- holds promise for humans to be more creative by offering new ideas, or less creative by anchoring on GenAI ideas. We study the causal impact of GenAI ideas on the production of a short story in an online experimental study where some writers could obtain story ideas from a GenAI platform. We find that access to GenAI ideas causes stories to be evaluated as more creative, better written, and more enjoyable, especially among less creative writers. However, GenAI-enabled stories are more similar to each other than stories by humans alone. These results point to an increase in individual creativity at the risk of losing collective novelty. This dynamic resembles a social dilemma: with GenAI, individual writers are better off, but collectively a narrower scope of novel content may be produced. Our results have implications for researchers, policy-makers and practitioners interested in bolstering creativity.

The development of large language models (LLMs) capable of following instructions and engaging in conversational interactions sparked increased interest in their utilization across various support tools. We investigate the utility of modern LLMs in assisting professional writers via an empirical user study (n=30). The design of our collaborative writing interface is grounded in the cognitive process model of writing that views writing as a goal-oriented thinking process encompassing non-linear cognitive activities: planning, translating, and reviewing. Participants are asked to submit a post-completion survey to provide feedback on the potential and pitfalls of LLMs as writing collaborators. Upon analyzing the writer-LLM interactions, we find that while writers seek LLM's help across all three types of cognitive activities, they find LLMs more helpful in translation and reviewing. Our findings from analyzing both the interactions and the survey responses highlight future research directions in creative writing assistance using LLMs.

We propose a method to explore the flavor structure of quarks and leptons with reinforcement learning. As a concrete model, we utilize a basic value-based algorithm for models with $U(1)$ flavor symmetry. By training neural networks on the $U(1)$ charges of quarks and leptons, the agent finds 21 models to be consistent with experimentally measured masses and mixing angles of quarks and leptons. In particular, an intrinsic value of normal ordering tends to be larger than that of inverted ordering, and the normal ordering is well fitted with the current experimental data in contrast to the inverted ordering. A specific value of effective mass for the neutrinoless double beta decay and a sizable leptonic CP violation induced by an angular component of flavon field are predicted by autonomous behavior of the agent. Our finding results indicate that the reinforcement learning can be a new method for understanding the flavor structure.

The Apple Vision Pro will be missing some major native apps at the outset, including Netflix, Spotify and YouTube. One notable app to which users of the mixed-reality headset will have access when it debuts later this week is Zoom, which will support the Vision Pro's Persona feature. Vision Pro users will be able to create digital versions of themselves. If you have said Persona, others on a Zoom or FaceTime call will be able to see your facial expressions and hand movements via your avatar. So while you may not be using a traditional webcam, other folks might notice your persona cringing at one of your boss' bad jokes.