pwc-archive/papers-with-abstracts huggingface dataset:
Brain-Model Evaluations Need the NeuroAI Turing Test
Authors: Jenelle Feather, Meenakshi Khosla, N. Apurva Ratan Murty, Aran Nayebi
Date: 2025-02-22 00:00:00
Tasks: model

What makes an artificial system a good model of intelligence? The classical test proposed by Alan Turing focuses on behavior, requiring that an artificial agent's behavior be indistinguishable from that of a human. While behavioral similarity provides a strong starting point, two systems with very different internal representations can produce the same outputs. Thus, in modeling biological intelligence, the field of NeuroAI often aims to go beyond behavioral similarity and achieve representational convergence between a model's activations and the measured activity of a biological system. This position paper argues that the standard definition of the Turing Test is incomplete for NeuroAI, and proposes a stronger framework called the ``NeuroAI Turing Test'', a benchmark that extends beyond behavior alone and \emph{additionally} requires models to produce internal neural representations that are empirically indistinguishable from those of a brain up to measured individual variability, i.e. the differences between a computational model and the brain is no more than the difference between one brain and another brain. While the brain is not necessarily the ceiling of intelligence, it remains the only universally agreed-upon example, making it a natural reference point for evaluating computational models. By proposing this framework, we aim to shift the discourse from loosely defined notions of brain inspiration to a systematic and testable standard centered on both behavior and internal representations, providing a clear benchmark for neuroscientific modeling and AI development.
Evolution 6.0: Evolving Robotic Capabilities Through Generative Design
Authors: Muhammad Haris Khan, Artyom Myshlyaev, Artem Lykov, Miguel Altamirano Cabrera, Dzmitry Tsetserukou
Date: 2025-02-24 00:00:00
Tasks: Action Generation, Text to 3D, Vision-Language-Action

We propose a new concept, Evolution 6.0, which represents the evolution of robotics driven by Generative AI. When a robot lacks the necessary tools to accomplish a task requested by a human, it autonomously designs the required instruments and learns how to use them to achieve the goal. Evolution 6.0 is an autonomous robotic system powered by Vision-Language Models (VLMs), Vision-Language Action (VLA) models, and Text-to-3D generative models for tool design and task execution. The system comprises two key modules: the Tool Generation Module, which fabricates task-specific tools from visual and textual data, and the Action Generation Module, which converts natural language instructions into robotic actions. It integrates QwenVLM for environmental understanding, OpenVLA for task execution, and Llama-Mesh for 3D tool generation. Evaluation results demonstrate a 90% success rate for tool generation with a 10-second inference time, and action generation achieving 83.5% in physical and visual generalization, 70% in motion generalization, and 37% in semantic generalization. Future improvements will focus on bimanual manipulation, expanded task capabilities, and enhanced environmental interpretation to improve real-world adaptability.
Town Hall Debate Prompting: Enhancing Logical Reasoning in LLMs through Multi-Persona Interaction
Authors: Vivaan Sandwar, Bhav Jain, Rishan Thangaraj, Ishaan Garg, Michael Lam, Kevin Zhu
Date: 2025-01-28 00:00:00
Tasks: Logical Reasoning, Multiple-choice

Debate is a commonly used form of human communication catered towards problem-solving because of its efficiency. Debate fundamentally allows multiple viewpoints to be brought up in problem-solving, and for complex problems, each viewpoint opens a new path for problem-solving. In this work, we apply this concept to LLM decision-making by proposing town hall-style debate prompting (THDP), a prompting method that splices a language model into multiple personas that will debate one another to reach a conclusion. Our experimental pipeline varies both the number of personas and the personality types of each persona to find the optimum town hall size and personality for benchmark performance as measured by ZebraLogic bench, a reasoning-intensive benchmark characterized by both multiple-choice and fill-in-the-blank questions. Our experimental results demonstrate that a town hall size of 5 personas with LLM-determined personality types performs optimally on ZebraLogic, achieving a 13\% improvement over one-shot CoT baselines in per-cell accuracy in GPT-4o, 9% puzzle accuracy increase in Claude 3.5 Sonnet, and an improvement in hard puzzle accuracy from 10-15%.
On the Effectiveness of Large Language Models in Automating Categorization of Scientific Texts
Authors: Gautam Kishore Shahi, Oliver Hummel
Date: 2025-02-08 00:00:00
Tasks: Information Retrieval

The rapid advancement of Large Language Models (LLMs) has led to a multitude of application opportunities. One traditional task for Information Retrieval systems is the summarization and classification of texts, both of which are important for supporting humans in navigating large literature bodies as they e.g. exist with scientific publications. Due to this rapidly growing body of scientific knowledge, recent research has been aiming at building research information systems that not only offer traditional keyword search capabilities, but also novel features such as the automatic detection of research areas that are present at knowledge intensive organizations in academia and industry. To facilitate this idea, we present the results obtained from evaluating a variety of LLMs in their ability to sort scientific publications into hierarchical classifications systems. Using the FORC dataset as ground truth data, we have found that recent LLMs (such as Meta Llama 3.1) are able to reach an accuracy of up to 0.82, which is up to 0.08 better than traditional BERT models.
Hallucination Detection in Large Language Models with Metamorphic Relations
Authors: Borui Yang, Md Afif Al Mamun, Jie M. Zhang, Gias Uddin
Date: 2025-02-20 00:00:00
Tasks: Hallucination

Large Language Models (LLMs) are prone to hallucinations, e.g., factually incorrect information, in their responses. These hallucinations present challenges for LLM-based applications that demand high factual accuracy. Existing hallucination detection methods primarily depend on external resources, which can suffer from issues such as low availability, incomplete coverage, privacy concerns, high latency, low reliability, and poor scalability. There are also methods depending on output probabilities, which are often inaccessible for closed-source LLMs like GPT models. This paper presents MetaQA, a self-contained hallucination detection approach that leverages metamorphic relation and prompt mutation. Unlike existing methods, MetaQA operates without any external resources and is compatible with both open-source and closed-source LLMs. MetaQA is based on the hypothesis that if an LLM's response is a hallucination, the designed metamorphic relations will be violated. We compare MetaQA with the state-of-the-art zero-resource hallucination detection method, SelfCheckGPT, across multiple datasets, and on two open-source and two closed-source LLMs. Our results reveal that MetaQA outperforms SelfCheckGPT in terms of precision, recall, and f1 score. For the four LLMs we study, MetaQA outperforms SelfCheckGPT with a superiority margin ranging from 0.041 - 0.113 (for precision), 0.143 - 0.430 (for recall), and 0.154 - 0.368 (for F1-score). For instance, with Mistral-7B, MetaQA achieves an average F1-score of 0.435, compared to SelfCheckGPT's F1-score of 0.205, representing an improvement rate of 112.2%. MetaQA also demonstrates superiority across all different categories of questions.
A Close Look at Decomposition-based XAI-Methods for Transformer Language Models
Authors: Leila Arras, Bruno Puri, Patrick Kahardipraja, Sebastian Lapuschkin, Wojciech Samek
Date: 2025-02-21 00:00:00

Various XAI attribution methods have been recently proposed for the transformer architecture, allowing for insights into the decision-making process of large language models by assigning importance scores to input tokens and intermediate representations. One class of methods that seems very promising in this direction includes decomposition-based approaches, i.e., XAI-methods that redistribute the model's prediction logit through the network, as this value is directly related to the prediction. In the previous literature we note though that two prominent methods of this category, namely ALTI-Logit and LRP, have not yet been analyzed in juxtaposition and hence we propose to close this gap by conducting a careful quantitative evaluation w.r.t. ground truth annotations on a subject-verb agreement task, as well as various qualitative inspections, using BERT, GPT-2 and LLaMA-3 as a testbed. Along the way we compare and extend the ALTI-Logit and LRP methods, including the recently proposed AttnLRP variant, from an algorithmic and implementation perspective. We further incorporate in our benchmark two widely-used gradient-based attribution techniques. Finally, we make our carefullly constructed benchmark dataset for evaluating attributions on language models, as well as our code, publicly available in order to foster evaluation of XAI-methods on a well-defined common ground.
Modality-Aware Neuron Pruning for Unlearning in Multimodal Large Language Models
Authors: Zheyuan Liu, Guangyao Dou, Xiangchi Yuan, Chunhui Zhang, Zhaoxuan Tan, Meng Jiang
Date: 2025-02-21 00:00:00

Generative models such as Large Language Models (LLMs) and Multimodal Large Language Models (MLLMs) trained on massive datasets can lead them to memorize and inadvertently reveal sensitive information, raising ethical and privacy concerns. While some prior works have explored this issue in the context of LLMs, it presents a unique challenge for MLLMs due to the entangled nature of knowledge across modalities, making comprehensive unlearning more difficult. To address this challenge, we propose Modality Aware Neuron Unlearning (MANU), a novel unlearning framework for MLLMs designed to selectively clip neurons based on their relative importance to the targeted forget data, curated for different modalities. Specifically, MANU consists of two stages: important neuron selection and selective pruning. The first stage identifies and collects the most influential neurons across modalities relative to the targeted forget knowledge, while the second stage is dedicated to pruning those selected neurons. MANU effectively isolates and removes the neurons that contribute most to the forget data within each modality, while preserving the integrity of retained knowledge. Our experiments conducted across various MLLM architectures illustrate that MANU can achieve a more balanced and comprehensive unlearning in each modality without largely affecting the overall model utility.
The Esethu Framework: Reimagining Sustainable Dataset Governance and Curation for Low-Resource Languages
Authors: Jenalea Rajab, Anuoluwapo Aremu, Everlyn Asiko Chimoto, Dale Dunbar, Graham Morrissey, Fadel Thior, Luandrie Potgieter, Jessico Ojo, Atnafu Lambebo Tonja, Maushami Chetty, Onyothi Nekoto, Pelonomi Moiloa, Jade Abbott, Vukosi Marivate, Benjamin Rosman
Date: 2025-02-21 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), speech-recognition, Speech Recognition

This paper presents the Esethu Framework, a sustainable data curation framework specifically designed to empower local communities and ensure equitable benefit-sharing from their linguistic resources. This framework is supported by the Esethu license, a novel community-centric data license. As a proof of concept, we introduce the Vuk'uzenzele isiXhosa Speech Dataset (ViXSD), an open-source corpus developed under the Esethu Framework and License. The dataset, containing read speech from native isiXhosa speakers enriched with demographic and linguistic metadata, demonstrates how community-driven licensing and curation principles can bridge resource gaps in automatic speech recognition (ASR) for African languages while safeguarding the interests of data creators. We describe the framework guiding dataset development, outline the Esethu license provisions, present the methodology for ViXSD, and present ASR experiments validating ViXSD's usability in building and refining voice-driven applications for isiXhosa.
Mind the Gap! Static and Interactive Evaluations of Large Audio Models
Authors: Minzhi Li, William Barr Held, Michael J Ryan, Kunat Pipatanakul, Potsawee Manakul, Hao Zhu, Diyi Yang
Date: 2025-02-21 00:00:00
Tasks: Question Answering

As AI chatbots become ubiquitous, voice interaction presents a compelling way to enable rapid, high-bandwidth communication for both semantic and social signals. This has driven research into Large Audio Models (LAMs) to power voice-native experiences. However, aligning LAM development with user goals requires a clear understanding of user needs and preferences to establish reliable progress metrics. This study addresses these challenges by introducing an interactive approach to evaluate LAMs and collecting 7,500 LAM interactions from 484 participants. Through topic modeling of user queries, we identify primary use cases for audio interfaces. We then analyze user preference rankings and qualitative feedback to determine which models best align with user needs. Finally, we evaluate how static benchmarks predict interactive performance - our analysis reveals no individual benchmark strongly correlates with interactive results ($\tau \leq 0.33$ for all benchmarks). While combining multiple coarse-grained features yields modest predictive power ($R^2$=$0.30$), only two out of twenty datasets on spoken question answering and age prediction show significantly positive correlations. This suggests a clear need to develop LAM evaluations that better correlate with user preferences.
Improving Consistency in Large Language Models through Chain of Guidance
Authors: Harsh Raj, Vipul Gupta, Domenic Rosati, Subhabrata Majumdar
Date: 2025-02-21 00:00:00
Tasks: Question Answering

Consistency is a fundamental dimension of trustworthiness in Large Language Models (LLMs). For humans to be able to trust LLM-based applications, their outputs should be consistent when prompted with inputs that carry the same meaning or intent. Despite this need, there is no known mechanism to control and guide LLMs to be more consistent at inference time. In this paper, we introduce a novel alignment strategy to maximize semantic consistency in LLM outputs. Our proposal is based on Chain of Guidance (CoG), a multistep prompting technique that generates highly consistent outputs from LLMs. For closed-book question-answering (Q&A) tasks, when compared to direct prompting, the outputs generated using CoG show improved consistency. While other approaches like template-based responses and majority voting may offer alternative paths to consistency, our work focuses on exploring the potential of guided prompting. We use synthetic data sets comprised of consistent input-output pairs to fine-tune LLMs to produce consistent and correct outputs. Our fine-tuned models are more than twice as consistent compared to base models and show strong generalization capabilities by producing consistent outputs over datasets not used in the fine-tuning process.
CVE-LLM : Ontology-Assisted Automatic Vulnerability Evaluation Using Large Language Models
Authors: Rikhiya Ghosh, Hans-Martin von Stockhausen, Martin Schmitt, George Marica Vasile, Sanjeev Kumar Karn, Oladimeji Farri
Date: 2025-02-21 00:00:00
Tasks: Management

The National Vulnerability Database (NVD) publishes over a thousand new vulnerabilities monthly, with a projected 25 percent increase in 2024, highlighting the crucial need for rapid vulnerability identification to mitigate cybersecurity attacks and save costs and resources. In this work, we propose using large language models (LLMs) to learn vulnerability evaluation from historical assessments of medical device vulnerabilities in a single manufacturer's portfolio. We highlight the effectiveness and challenges of using LLMs for automatic vulnerability evaluation and introduce a method to enrich historical data with cybersecurity ontologies, enabling the system to understand new vulnerabilities without retraining the LLM. Our LLM system integrates with the in-house application - Cybersecurity Management System (CSMS) - to help Siemens Healthineers (SHS) product cybersecurity experts efficiently assess the vulnerabilities in our products. Also, we present guidelines for efficient integration of LLMs into the cybersecurity tool.
AutoMedPrompt: A New Framework for Optimizing LLM Medical Prompts Using Textual Gradients
Authors: Sean Wu, Michael Koo, Fabien Scalzo, Ira Kurtz
Date: 2025-02-21 00:00:00
Tasks: MedQA, Prompt Engineering

Large language models (LLMs) have demonstrated increasingly sophisticated performance in medical and other fields of knowledge. Traditional methods of creating specialist LLMs require extensive fine-tuning and training of models on large datasets. Recently, prompt engineering, instead of fine-tuning, has shown potential to boost the performance of general foundation models. However, prompting methods such as chain-of-thought (CoT) may not be suitable for all subspecialty, and k-shot approaches may introduce irrelevant tokens into the context space. We present AutoMedPrompt, which explores the use of textual gradients to elicit medically relevant reasoning through system prompt optimization. AutoMedPrompt leverages TextGrad's automatic differentiation via text to improve the ability of general foundation LLMs. We evaluated AutoMedPrompt on Llama 3, an open-source LLM, using several QA benchmarks, including MedQA, PubMedQA, and the nephrology subspecialty-specific NephSAP. Our results show that prompting with textual gradients outperforms previous methods on open-source LLMs and surpasses proprietary models such as GPT-4, Claude 3 Opus, and Med-PaLM 2. AutoMedPrompt sets a new state-of-the-art (SOTA) performance on PubMedQA with an accuracy of 82.6$\%$, while also outperforming previous prompting strategies on open-sourced models for MedQA (77.7$\%$) and NephSAP (63.8$\%$).
KVLink: Accelerating Large Language Models via Efficient KV Cache Reuse
Authors: Jingbo Yang, Bairu Hou, Wei Wei, Yujia Bao, Shiyu Chang
Date: 2025-02-21 00:00:00
Tasks: Question Answering

We describe KVLink, an approach for efficient key-value (KV) cache reuse in large language models (LLMs). In many LLM applications, different inputs can share overlapping context, such as the same retrieved document appearing in multiple queries. However, the LLMs still need to encode the entire context for each query, leading to redundant computation. In this paper, we propose a new strategy to eliminate such inefficiency, where the KV cache of each document is precomputed independently. During inference, the KV caches of retrieved documents are concatenated, allowing the model to reuse cached representations instead of recomputing them. To mitigate the performance degradation of LLMs when using KV caches computed independently for each document, KVLink introduces three key components: adjusting positional embeddings of the KV cache at inference to match the global position after concatenation, using trainable special tokens to restore self-attention across independently encoded documents, and applying mixed-data fine-tuning to enhance performance while preserving the model's original capabilities. Experiments across 7 datasets demonstrate that KVLink improves question answering accuracy by an average of 4% over state-of-the-art methods. Furthermore, by leveraging precomputed KV caches, our approach reduces time-to-first-token by up to 90% compared to standard LLM inference, making it a scalable and efficient solution for context reuse.
Enhancing LLMs for Identifying and Prioritizing Important Medical Jargons from Electronic Health Record Notes Utilizing Data Augmentation
Authors: Won Seok Jang, Sharmin Sultana, Zonghai Yao, Hieu Tran, Zhichao Yang, Sunjae Kwon, Hong Yu
Date: 2025-02-22 00:00:00
Tasks: Data Augmentation

Objective: OpenNotes enables patients to access EHR notes, but medical jargon can hinder comprehension. To improve understanding, we evaluated closed- and open-source LLMs for extracting and prioritizing key medical terms using prompting, fine-tuning, and data augmentation. Materials and Methods: We assessed LLMs on 106 expert-annotated EHR notes, experimenting with (i) general vs. structured prompts, (ii) zero-shot vs. few-shot prompting, (iii) fine-tuning, and (iv) data augmentation. To enhance open-source models in low-resource settings, we used ChatGPT for data augmentation and applied ranking techniques. We incrementally increased the augmented dataset size (10 to 10,000) and conducted 5-fold cross-validation, reporting F1 score and Mean Reciprocal Rank (MRR). Results and Discussion: Fine-tuning and data augmentation improved performance over other strategies. GPT-4 Turbo achieved the highest F1 (0.433), while Mistral7B with data augmentation had the highest MRR (0.746). Open-source models, when fine-tuned or augmented, outperformed closed-source models. Notably, the best F1 and MRR scores did not always align. Few-shot prompting outperformed zero-shot in vanilla models, and structured prompts yielded different preferences across models. Fine-tuning improved zero-shot performance but sometimes degraded few-shot performance. Data augmentation performed comparably or better than other methods. Conclusion: Our evaluation highlights the effectiveness of prompting, fine-tuning, and data augmentation in improving model performance for medical jargon extraction in low-resource scenarios.
Moving Beyond Medical Exam Questions: A Clinician-Annotated Dataset of Real-World Tasks and Ambiguity in Mental Healthcare
Authors: Max Lamparth, Declan Grabb, Amy Franks, Scott Gershan, Kaitlyn N. Kunstman, Aaron Lulla, Monika Drummond Roots, Manu Sharma, Aryan Shrivastava, Nina Vasan, Colleen Waickman
Date: 2025-02-22 00:00:00
Tasks: Decision Making, Multiple-choice

Current medical language model (LM) benchmarks often over-simplify the complexities of day-to-day clinical practice tasks and instead rely on evaluating LMs on multiple-choice board exam questions. Thus, we present an expert-created and annotated dataset spanning five critical domains of decision-making in mental healthcare: treatment, diagnosis, documentation, monitoring, and triage. This dataset - created without any LM assistance - is designed to capture the nuanced clinical reasoning and daily ambiguities mental health practitioners encounter, reflecting the inherent complexities of care delivery that are missing from existing datasets. Almost all 203 base questions with five answer options each have had the decision-irrelevant demographic patient information removed and replaced with variables (e.g., AGE), and are available for male, female, or non-binary-coded patients. For question categories dealing with ambiguity and multiple valid answer options, we create a preference dataset with uncertainties from the expert annotations. We outline a series of intended use cases and demonstrate the usability of our dataset by evaluating eleven off-the-shelf and four mental health fine-tuned LMs on category-specific task accuracy, on the impact of patient demographic information on decision-making, and how consistently free-form responses deviate from human annotated samples.