pwc-archive/papers-with-abstracts huggingface dataset:
ConSCompF: Consistency-focused Similarity Comparison Framework for Generative Large Language Models
Authors: Alexey Karev, Dong Xu
Date: 2025-03-18 00:00:00
Tasks: Benchmarking, Chatbot

Large language models (LLMs) have been one of the most important discoveries in machine learning in recent years. LLM-based artificial intelligence (AI) assistants, such as ChatGPT, have consistently attracted the attention from researchers, investors, and the general public, driving the rapid growth of this industry. With the frequent introduction of new LLMs to the market, it becomes increasingly difficult to differentiate between them, creating a demand for new LLM comparison methods. In this research, the Consistency-focused Similarity Comparison Framework (ConSCompF) for generative large language models is proposed. It compares texts generated by two LLMs and produces a similarity score, indicating the overall degree of similarity between their responses. The main advantage of this framework is that it can operate on a small number of unlabeled data, such as chatbot instruction prompts, and does not require LLM developers to disclose any information about their product. To evaluate the efficacy of ConSCompF, two experiments aimed at identifying similarities between multiple LLMs are conducted. Additionally, these experiments examine the correlation between the similarity scores generated by ConSCompF and the differences in the outputs produced by other benchmarking techniques, such as ROUGE-L. Finally, a series of few-shot LLM comparison experiments is conducted to evaluate the performance of ConSCompF in a few-shot LLM comparison scenario. The proposed framework can be used for calculating similarity matrices of multiple LLMs, which can be effectively visualized using principal component analysis (PCA). The ConSCompF output may provide useful insights into data that might have been used during LLM training and help detect possible investment fraud attempts.
Empowering Smaller Models: Tuning LLaMA and Gemma with Chain-of-Thought for Ukrainian Exam Tasks
Authors: Mykyta Syromiatnikov, Victoria Ruvinskaya, Nataliia Komleva
Date: 2025-03-18 00:00:00
Tasks: GPU, parameter-efficient fine-tuning

Leading large language models have demonstrated impressive capabilities in reasoning-intensive tasks, such as standardized educational testing. However, they often require extensive training in low-resource settings with inaccessible infrastructure. Small or compact models, though more efficient, frequently lack sufficient support for underrepresented languages, leaving a performance gap in critical domains. This work explores the potential of parameter-efficient fine-tuning of compact open-weight language models to handle reasoning-intensive tasks in the underrepresented Ukrainian language, building on the findings of the ZNO-Eval benchmark. Parameter-efficient fine-tuning of LLaMA 3.1 (8 billion parameters), LLaMA 3.2 (3 billion parameters), and Gemma 2 (9 billion parameters) models on chain-of-thought solutions resulted in a modest test score improvement of up to 17.4% on complex matching tasks and 1.6% overall compared to tuning on answer letters alone, offering enhanced interpretability and robustness. In addition, the proposed tuning method with joint task topic and step-by-step solution generation outperforms standard chain-of-thought tuning in matching tasks and provides a 5.4% gain over the best LLaMA 3.2 model due to guiding the model to recall and apply domain-relevant information. Contrasting obtained results with zero-shot evaluations of leading open-weight and proprietary models such as Qwen, DeepSeek R1, OpenAI o1 and o3, Gemini, and Claude, highlight that fine-tuning LLaMA and Gemma models with 2,032 step-by-step solutions and 20 to 50 million trainable parameters on a single A100 GPU lets them outperform GPT-4o mini, Mistral Large, and larger open-weight models. This research also evaluates how merging the quantized adapter with the base model influences the generation quality. Source code and tuned models are available at https://github.com/NLPForUA/ZNO.
CARE: A QLoRA-Fine Tuned Multi-Domain Chatbot With Fast Learning On Minimal Hardware
Authors: Ankit Dutta, Nabarup Ghosh, Ankush Chatterjee
Date: 2025-03-18 00:00:00
Tasks: Chatbot, Question Answering

Large Language models have demonstrated excellent domain-specific question-answering capabilities when finetuned with a particular dataset of that specific domain. However, fine-tuning the models requires a significant amount of training time and a considerable amount of hardware. In this work, we propose CARE (Customer Assistance and Response Engine), a lightweight model made by fine-tuning Phi3.5-mini on very minimal hardware and data, designed to handle queries primarily across three domains: telecommunications support, medical support, and banking support. For telecommunications and banking, the chatbot addresses issues and problems faced by customers regularly in the above-mentioned domains. In the medical domain, CARE provides preliminary support by offering basic diagnoses and medical suggestions that a user might take before consulting a healthcare professional. Since CARE is built on Phi3.5-mini, it can be used even on mobile devices, increasing its usability. Our research also shows that CARE performs relatively well on various medical benchmarks, indicating that it can be used to make basic medical suggestions.
Spatio-Temporal Graph Neural Networks for Infant Language Acquisition Prediction
Authors: Andrew Roxburgh, Floriana Grasso, Terry R. Payne
Date: 2025-03-18 00:00:00
Tasks: Language Acquisition

Predicting the words that a child is going to learn next can be useful for boosting language acquisition, and such predictions have been shown to be possible with both neural network techniques (looking at changes in the vocabulary state over time) and graph model (looking at data pertaining to the relationships between words). However, these models do not fully capture the complexity of the language learning process of an infant when used in isolation. In this paper, we examine how a model of language acquisition for infants and young children can be constructed and adapted for use in a Spatio-Temporal Graph Convolutional Network (STGCN), taking into account the different types of linguistic relationships that occur during child language learning. We introduce a novel approach for predicting child vocabulary acquisition, and evaluate the efficacy of such a model with respect to the different types of linguistic relationships that occur during language acquisition, resulting in insightful observations on model calibration and norm selection. An evaluation of this model found that the mean accuracy of models for predicting new words when using sensorimotor relationships (0.733) and semantic relationships (0.729) were found to be superior to that observed with a 2-layer Feed-forward neural network. Furthermore, the high recall for some relationships suggested that some relationships (e.g. visual) were superior in identifying a larger proportion of relevant words that a child should subsequently learn than others (such as auditory).
Unifying Text Semantics and Graph Structures for Temporal Text-attributed Graphs with Large Language Models
Authors: Siwei Zhang, Yun Xiong, Yateng Tang, Xi Chen, Zian Jia, Zehao Gu, Jiarong Xu, Jiawei Zhang
Date: 2025-03-18 00:00:00
Tasks: Link Prediction, Node Classification

Temporal graph neural networks (TGNNs) have shown remarkable performance in temporal graph modeling. However, real-world temporal graphs often possess rich textual information, giving rise to temporal text-attributed graphs (TTAGs). Such combination of dynamic text semantics and evolving graph structures introduces heightened complexity. Existing TGNNs embed texts statically and rely heavily on encoding mechanisms that biasedly prioritize structural information, overlooking the temporal evolution of text semantics and the essential interplay between semantics and structures for synergistic reinforcement. To tackle these issues, we present \textbf{CROSS}, a flexible framework that seamlessly extends existing TGNNs for TTAG modeling. CROSS is designed by decomposing the TTAG modeling process into two phases: (i) temporal semantics extraction; and (ii) semantic-structural information unification. The key idea is to advance the large language models (LLMs) to dynamically extract the temporal semantics in text space and then generate cohesive representations unifying both semantics and structures. Specifically, we propose a Temporal Semantics Extractor in the CROSS framework, which empowers LLMs to offer the temporal semantic understanding of node's evolving contexts of textual neighborhoods, facilitating semantic dynamics. Subsequently, we introduce the Semantic-structural Co-encoder, which collaborates with the above Extractor for synthesizing illuminating representations by jointly considering both semantic and structural information while encouraging their mutual reinforcement. Extensive experiments show that CROSS achieves state-of-the-art results on four public datasets and one industrial dataset, with 24.7% absolute MRR gain on average in temporal link prediction and 3.7% AUC gain in node classification of industrial application.
SagaLLM: Context Management, Validation, and Transaction Guarantees for Multi-Agent LLM Planning
Authors: Edward Y. Chang, Longling Geng
Date: 2025-03-15 00:00:00
Tasks: Decision Making, Management

Recent LLM-based agent frameworks have demonstrated impressive capabilities in task delegation and workflow orchestration, but face significant challenges in maintaining context awareness and ensuring planning consistency. This paper presents SagaLLM, a structured multi-agent framework that addresses four fundamental limitations in current LLM approaches: inadequate self-validation, context narrowing, lacking transaction properties, and insufficient inter-agent coordination. By implementing specialized context management agents and validation protocols, SagaLLM preserves critical constraints and state information throughout complex planning processes, enabling robust and consistent decision-making even during disruptions. We evaluate our approach using selected problems from the REALM benchmark, focusing on sequential and reactive planning scenarios that challenge both context retention and adaptive reasoning. Our experiments with state-of-the-art LLMs, Claude 3.7, DeepSeek R1, GPT-4o, and GPT-o1, demonstrate that while these models exhibit impressive reasoning capabilities, they struggle with maintaining global constraint awareness during complex planning tasks, particularly when adapting to unexpected changes. In contrast, the distributed cognitive architecture of SagaLLM shows significant improvements in planning consistency, constraint enforcement, and adaptation to disruptions in various scenarios.
SPIN-Bench: How Well Do LLMs Plan Strategically and Reason Socially?
Authors: Jianzhu Yao, Kevin Wang, Ryan Hsieh, Haisu Zhou, Tianqing Zou, Zerui Cheng, Zhangyang Wang, Pramod Viswanath
Date: 2025-03-16 00:00:00
Tasks: Board Games, Card Games, Math

Reasoning and strategic behavior in social interactions is a hallmark of intelligence. This form of reasoning is significantly more sophisticated than isolated planning or reasoning tasks in static settings (e.g., math problem solving). In this paper, we present Strategic Planning, Interaction, and Negotiation (SPIN-Bench), a new multi-domain evaluation designed to measure the intelligence of strategic planning and social reasoning. While many existing benchmarks focus on narrow planning or single-agent reasoning, SPIN-Bench combines classical PDDL tasks, competitive board games, cooperative card games, and multi-agent negotiation scenarios in one unified framework. The framework includes both a benchmark as well as an arena to simulate and evaluate the variety of social settings to test reasoning and strategic behavior of AI agents. We formulate the benchmark SPIN-Bench by systematically varying action spaces, state complexity, and the number of interacting agents to simulate a variety of social settings where success depends on not only methodical and step-wise decision making, but also conceptual inference of other (adversarial or cooperative) participants. Our experiments reveal that while contemporary LLMs handle basic fact retrieval and short-range planning reasonably well, they encounter significant performance bottlenecks in tasks requiring deep multi-hop reasoning over large state spaces and socially adept coordination under uncertainty. We envision SPIN-Bench as a catalyst for future research on robust multi-agent planning, social reasoning, and human--AI teaming. Project Website: https://spinbench.github.io/
DLPO: Towards a Robust, Efficient, and Generalizable Prompt Optimization Framework from a Deep-Learning Perspective
Authors: Dengyun Peng, YuHang Zhou, Qiguang Chen, Jinhao Liu, Jingjing Chen, Libo Qin
Date: 2025-03-17 00:00:00

Large Language Models (LLMs) have achieved remarkable success across diverse tasks, largely driven by well-designed prompts. However, crafting and selecting such prompts often requires considerable human effort, significantly limiting its scalability. To mitigate this, recent studies have explored automated prompt optimization as a promising solution. Despite these efforts, existing methods still face critical challenges in robustness, efficiency, and generalization. To systematically address these challenges, we first conduct an empirical analysis to identify the limitations of current reflection-based prompt optimization paradigm. Building on these insights, we propose 7 innovative approaches inspired by traditional deep learning paradigms for prompt optimization (DLPO), seamlessly integrating these concepts into text-based gradient optimization. Through these advancements, we progressively tackle the aforementioned challenges and validate our methods through extensive experimentation. We hope our study not only provides valuable guidance for future research but also offers a comprehensive understanding of the challenges and potential solutions in prompt optimization. Our code is available at https://github.com/sfasfaffa/DLPO.
Pensez: Less Data, Better Reasoning -- Rethinking French LLM
Authors: Huy Hoang Ha
Date: 2025-03-17 00:00:00
Tasks: Large Language Model, Math, Mathematical Reasoning

Large language models (LLMs) have demonstrated remarkable capabilities in various natural language processing tasks. However, achieving strong performance in specialized domains like mathematical reasoning and non-English languages often requires extensive training on massive datasets. This paper investigates a contrasting approach: strategic fine-tuning on a small, high-quality, bilingual (English-French) dataset to enhance both the reasoning capabilities and French language proficiency of a large language model. Rather than relying on scale, we explore the hypothesis that targeted data curation and optimized training can achieve competitive, or even superior, performance. We demonstrate, through targeted supervised fine-tuning (SFT) on only 2,000 carefully selected samples, significant improvements in mathematical reasoning. Specifically, Pensez 7B exhibits an increase in accuracy of the base model up to 20% on the AIME25 and a 12% increase on a French MATH level 5 benchmark. These results challenge the prevailing assumption that massive datasets are aprerequisite for strong reasoning performance in LLMs, highlighting the potential of strategic data curation and optimized fine-tuning for enhancing both specialized skills and multilingual capabilities. Our findings have implications for the efficient development of high-performing, multilingual LLMs, especially in resource-constrained scenarios.
Feature Extraction and Analysis for GPT-Generated Text
Authors: A. Selvioğlu, V. Adanova, M. Atagoziev
Date: 2025-03-17 00:00:00

With the rise of advanced natural language models like GPT, distinguishing between human-written and GPT-generated text has become increasingly challenging and crucial across various domains, including academia. The long-standing issue of plagiarism has grown more pressing, now compounded by concerns about the authenticity of information, as it is not always clear whether the presented facts are genuine or fabricated. In this paper, we present a comprehensive study of feature extraction and analysis for differentiating between human-written and GPT-generated text. By applying machine learning classifiers to these extracted features, we evaluate the significance of each feature in detection. Our results demonstrate that human and GPT-generated texts exhibit distinct writing styles, which can be effectively captured by our features. Given sufficiently long text, the two can be differentiated with high accuracy.
CoDet-M4: Detecting Machine-Generated Code in Multi-Lingual, Multi-Generator and Multi-Domain Settings
Authors: Daniil Orel, Dilshod Azizov, Preslav Nakov
Date: 2025-03-17 00:00:00
Tasks: Code Generation, Ethics, Feature Engineering

Large language models (LLMs) have revolutionized code generation, automating programming with remarkable efficiency. However, these advancements challenge programming skills, ethics, and assessment integrity, making the detection of LLM-generated code essential for maintaining accountability and standards. While, there has been some research on this problem, it generally lacks domain coverage and robustness, and only covers a small number of programming languages. To this end, we propose a framework capable of distinguishing between human- and LLM-written code across multiple programming languages, code generators, and domains. We use a large-scale dataset from renowned platforms and LLM-based code generators, alongside applying rigorous data quality checks, feature engineering, and comparative analysis using evaluation of traditional machine learning models, pre-trained language models (PLMs), and LLMs for code detection. We perform an evaluation on out-of-domain scenarios, such as detecting the authorship and hybrid authorship of generated code and generalizing to unseen models, domains, and programming languages. Moreover, our extensive experiments show that our framework effectively distinguishes human- from LLM-written code and sets a new benchmark for this task.
AccelGen: Heterogeneous SLO-Guaranteed High-Throughput LLM Inference Serving for Diverse Applications
Authors: Haiying Shen, Tanmoy Sen
Date: 2025-03-17 00:00:00
Tasks: Chunking, GPU, Large Language Model

In this paper, we consider a mixed-prompt scenario for a large language model (LLM) inference serving system that supports diverse applications with both short prompts and long prompts and heterogeneous SLOs for iteration time. To improve throughput when handling long prompts, previous research introduces a chunking method, but has not addressed heterogeneous SLOs. To address the limitation, we propose AccelGen, a high-throughput LLM inference serving system with heterogeneous SLO guarantees for diverse applications. AccelGen introduces four core components: (1) SLO-guaranteed dynamic chunking, which dynamically adjusts chunk sizes to maximize GPU compute utilization while meeting iteration-level SLOs; (2) Iteration-level SLO-based task prioritization, which prioritizes tight-SLO requests and batches requests with similar SLOs; (3) Multi-resource-aware batching, which selects queued requests to maximize the utilizations of both GPU compute resource and key-value cache (KVC). Trace-driven real experiments demonstrate that AccelGen achieves 1.42-11.21X higher throughput, 1.43-13.71X higher goodput, 37-90% higher SLO attainment, and 1.61-12.22X lower response latency compared to the state-of-the-art approaches. It achieves performance near the Oracle, which optimally maximizes goodput.
Mitigating KV Cache Competition to Enhance User Experience in LLM Inference
Authors: Haiying Shen, Tanmoy Sen, Masahiro Tanaka
Date: 2025-03-17 00:00:00
Tasks: Large Language Model

In Large Language Model (LLM) serving, the KV-cache (KVC) bottleneck causes high tail Time-to-First-Token (TTFT) and Time-Between-Tokens (TBT), impairing user experience, particularly in time-sensitive applications. However, satisfying both TTFT and TBT service-level objectives (SLOs) is challenging. To address this, we propose a system, named CacheOPT for mitigating KV Cache competition, based on key insights from our measurements, incorporating novel components. First, it estimates a request's output length, bounding the deviation with a high specified probability, adjusted based on the request arrival rate. Second, it allocates the estimated KVC demand to a request, and reuses other requests' allocated KVC to avoid preemptions while reducing waiting time. Third, it proactively allocates KVC before instead of at the time a request exhausts its allocation and reserves KVC globally to prevent preemptions. Fourth, it chooses a request that has long TBT SLO, long job remaining time and short preemption time to preempt. Fifth, it selects the shortest-latency strategy between swapping and recomputation for preemptions. Experiments show that CacheOPT achieves up to 3.29$\times$ and 2.83$\times$ lower tail TBT and tail TTFT, 47\% and 53\% higher TTFT and TBT SLO attainments, and supports up to 1.58$\times$ higher request arrival rate than the state-of-the-art methods.
Enabling Inclusive Systematic Reviews: Incorporating Preprint Articles with Large Language Model-Driven Evaluations
Authors: Rui Yang, Jiayi Tong, Haoyuan Wang, Hui Huang, Ziyang Hu, Peiyu Li, Nan Liu, Christopher J. Lindsell, Michael J. Pencina, Yong Chen, Chuan Hong
Date: 2025-03-18 00:00:00
Tasks: Articles, Language Modeling, Language Modelling, Large Language Model

Background. Systematic reviews in comparative effectiveness research require timely evidence synthesis. Preprints accelerate knowledge dissemination but vary in quality, posing challenges for systematic reviews. Methods. We propose AutoConfidence (automated confidence assessment), an advanced framework for predicting preprint publication, which reduces reliance on manual curation and expands the range of predictors, including three key advancements: (1) automated data extraction using natural language processing techniques, (2) semantic embeddings of titles and abstracts, and (3) large language model (LLM)-driven evaluation scores. Additionally, we employed two prediction models: a random forest classifier for binary outcome and a survival cure model that predicts both binary outcome and publication risk over time. Results. The random forest classifier achieved AUROC 0.692 with LLM-driven scores, improving to 0.733 with semantic embeddings and 0.747 with article usage metrics. The survival cure model reached AUROC 0.716 with LLM-driven scores, improving to 0.731 with semantic embeddings. For publication risk prediction, it achieved a concordance index of 0.658, increasing to 0.667 with semantic embeddings. Conclusion. Our study advances the framework for preprint publication prediction through automated data extraction and multiple feature integration. By combining semantic embeddings with LLM-driven evaluations, AudoConfidence enhances predictive performance while reducing manual annotation burden. The framework has the potential to facilitate systematic incorporation of preprint articles in evidence-based medicine, supporting researchers in more effective evaluation and utilization of preprint resources.