ymoslem/acl-abstracts huggingface dataset:
Fine-Tuning Medium-Scale LLMs for Joint Intent Classification and Slot Filling: A Data-Efficient and Cost-Effective Solution for SMEs (2025)

Dialogue Systems (DS) are increasingly in demand for automating tasks through natural language interactions. However, the core techniques for user comprehension in DS depend heavily on large amounts of labeled data, limiting their applicability in data-scarce environments common to many companies. This paper identifies best practices for data-efficient development and cost-effective deployment of DS in real-world application scenarios. We evaluate whether fine-tuning a medium-sized Large Language Model (LLM) for joint Intent Classification (IC) and Slot Filling (SF), with moderate hardware resource requirements still affordable by SMEs, can achieve competitive performance using less data compared to current state-of-the-art models. Experiments on the Spanish and English portions of the MASSIVE corpus demonstrate that the Llama-3-8B-Instruct model fine-tuned with only 10{\%} of the data outperforms the JointBERT architecture and GPT-4o in a zero-shot prompting setup in monolingual settings. In cross-lingual scenarios, Llama-3-8B-Instruct drastically outperforms multilingual JointBERT demonstrating a vastly superior performance when fine-tuned in a language and evaluated in the other.
Enhancing Large Language Models for Scientific Multimodal Summarization with Multimodal Output (2025)

The increasing integration of multimedia such as videos and graphical abstracts in scientific publications necessitates advanced summarization techniques. This paper introduces Uni-SciSum, a framework for Scientific Multimodal Summarization with Multimodal Output (SMSMO), addressing the challenges of fusing heterogeneous data sources (e.g., text, images, video, audio) and outputting multimodal summary within a unified architecture. Uni-SciSum leverages the power of large language models (LLMs) and extends its capability to cross-modal understanding through BridgeNet, a query-based transformer that fuses diverse modalities into a fixed-length embedding. A two-stage training process, involving modal-to-modal pre-training and cross-modal instruction tuning, aligns different modalities with summaries and optimizes for multimodal summary generation. Experiments on two new SMSMO datasets show Uni-SciSum outperforms uni- and multi-modality methods, advancing LLM applications in the increasingly multimodal realm of scientific communication.
\textquotedblleftStupid robot, I want to speak to a human!\textquotedblright User Frustration Detection in Task-Oriented Dialog Systems (2025)

Detecting user frustration in modern-day task-oriented dialog (TOD) systems is imperative for maintaining overall user satisfaction, engagement, and retention. However, most recent research is focused on sentiment and emotion detection in academic settings, thus failing to fully encapsulate implications of real-world user data. To mitigate this gap, in this work, we focus on user frustration in a deployed TOD system, assessing the feasibility of out-of-the-box solutions for user frustration detection. Specifically, we compare the performance of our deployed keyword-based approach, open-source approaches to sentiment analysis, dialog breakdown detection methods, and emerging in-context learning LLM-based detection. Our analysis highlights the limitations of open-source methods for real-world frustration detection, while demonstrating the superior performance of the LLM-based approach, achieving a 16{\%} relative improvement in F1 score on an internal benchmark. Finally, we analyze advantages and limitations of our methods and provide an insight into user frustration detection task for industry practitioners.
LLM Evaluate: An Industry-Focused Evaluation Tool for Large Language Models (2025)

Large Language Models (LLMs) have demonstrated impressive capability to solve a wide range of tasks in recent years. This has inspired researchers and practitioners in the real-world industrial domain to build useful products via leveraging LLMs. However, extensive evaluations of LLMs, in terms of accuracy, memory management, and inference latency, while ensuring the reproducibility of the results are crucial before deploying LLM-based solutions for real-world usage. In addition, when evaluating LLMs on internal customer data, an on-premise evaluation system is necessary to protect customer privacy rather than sending customer data to third-party APIs for evaluation. In this paper, we demonstrate how we build an on-premise system for LLM evaluation to address the challenges in the evaluation of LLMs in real-world industrial settings. We demonstrate the complexities of consolidating various datasets, models, and inference-related artifacts in complex LLM inference pipelines. For this purpose, we also present a case study in a real-world industrial setting. The demonstration of the LLM evaluation tool development would help researchers and practitioners in building on-premise systems for LLM evaluation ensuring privacy, reliability, robustness, and reproducibility.
Enhancing Future Link Prediction in Quantum Computing Semantic Networks through LLM-Initiated Node Features (2025)

Quantum computing is rapidly evolving in both physics and computer science, offering the potential to solve complex problems and accelerate computational processes. The development of quantum chips necessitates understanding the correlations among diverse experimental conditions. Semantic networks built on scientific literature, representing meaningful relationships between concepts, have been used across various domains to identify knowledge gaps and novel concept combinations. Neural network-based approaches have shown promise in link prediction within these networks. This study proposes initializing node features using LLMs to enhance node representations for link prediction tasks in graph neural networks. LLMs can provide rich descriptions, reducing the need for manual feature creation and lowering costs. Our method, evaluated using various link prediction models on a quantum computing semantic network, demonstrated efficacy compared to traditional node embedding techniques.
Page Stream Segmentation with LLMs: Challenges and Applications in Insurance Document Automation (2025)

Page Stream Segmentation (PSS) is critical for automating document processing in industries like insurance, where unstructured document collections are common. This paper explores the use of large language models (LLMs) for PSS, applying parameter-efficient fine-tuning to real-world insurance data. Our experiments show that LLMs outperform baseline models in page- and stream-level segmentation accuracy. However, stream-level calibration remains challenging, especially for high-stakes applications. We evaluate post-hoc calibration and Monte Carlo dropout, finding limited improvement. Future work will integrate active learning to enhance model calibration and support deployment in practical settings.
Graph-Augmented Open-Domain Multi-Document Summarization (2025)

In the open-domain multi-document summarization (ODMDS) task, retrieving relevant documents from large repositories and generating coherent summaries are crucial. However, existing methods often treat retrieval and summarization as separate tasks, neglecting the relationships among documents. To address these limitations, we propose an integrated retrieval-summarization framework that captures global document relationships through graph-based clustering, guiding the re-ranking of retrieved documents. This cluster-level thematic information is then used to guide large language models (LLMs) in refining the retrieved documents and generating more accurate, coherent summaries. Experimental results on the ODSUM benchmark demonstrate that our method significantly improves retrieval accuracy and produces summaries that surpass those derived from the oracle documents. These findings highlight the potential of our framework to improve both retrieval and summarization tasks in ODMDS.
Improve Speech Translation Through Text Rewrite (2025)

Despite recent progress in Speech Translation (ST) research, the challenges posed by inherent speech phenomena that distinguish transcribed speech from written text are not well addressed. The informal and erroneous nature of spontaneous speech is inadequately represented in the typical parallel text available for building translation models. We propose to address these issues through a text rewrite approach that aims to transform transcribed speech into a cleaner style more in line with the expectations of translation models built from written text. Moreover, the advantages of the rewrite model can be effectively distilled into a standalone translation model. Experiments on several benchmarks, using both publicly available and in-house translation models, demonstrate that adding a rewrite model to a traditional ST pipeline is a cost-effect way to address a variety of speech irregularities and improve speech translation quality for multiple language directions and domains.
CarMem: Enhancing Long-Term Memory in LLM Voice Assistants through Category-Bounding (2025)

In today`s assistant landscape, personalisation enhances interactions, fosters long-term relationships, and deepens engagement. However, many systems struggle with retaining user preferences, leading to repetitive user requests and disengagement. Furthermore, the unregulated and opaque extraction of user preferences in industry applications raises significant concerns about privacy and trust, especially in regions with stringent regulations like Europe. In response to these challenges, we propose a long-term memory system for voice assistants, structured around predefined categories. This approach leverages Large Language Models to efficiently extract, store, and retrieve preferences within these categories, ensuring both personalisation and transparency. We also introduce a synthetic multi-turn, multi-session conversation dataset (CarMem), grounded in real industry data, tailored to an in-car voice assistant setting. Benchmarked on the dataset, our system achieves an F1-score of .78 to .95 in preference extraction, depending on category granularity. Our maintenance strategy reduces redundant preferences by 95{\%} and contradictory ones by 92{\%}, while the accuracy of optimal retrieval is at .87. Collectively, the results demonstrate the system`s suitability for industrial applications.
XTR meets ColBERTv2: Adding ColBERTv2 Optimizations to XTR (2025)

XTR (Lee et al., 2023) introduced an efficient multi-vector retrieval method that addresses the limitations of the ColBERT (Khattab and Zaharia, 2020model by simplifying retrieval into a single stage through a modified learning objective. While XTR eliminates the need for multistage retrieval, it doesn`t incorporate the efficiency optimizations from ColBERTv2 (Santhanam et al., 2022, which improve indexing and retrieval speed. In this work, we enhance XTR by integrating ColBERTv2`s optimizations, showing that the combined approach preserves the strengths of both models. This results in a more efficient and scalable solution for multi-vector retrieval, while maintaining XTR`s streamlined retrieval process.
sDPO: Don`t Use Your Data All at Once (2025)

As large language models (LLMs) continue to advance, aligning them with human preferences has become a critical objective. In this paper, we introduce stepwise DPO (sDPO), an innovative extension of the recently popularized Direct Preference Optimization (DPO) technique for alignment tuning. sDPO systematically partitions the available preference datasets and applies them incrementally, rather than utilizing the entire dataset simultaneously. This stepwise manner enables the integration of progressively more aligned reference models within the DPO training framework. Our empirical results demonstrate that sDPO not only enhances the alignment precision of reference models but also significantly improves the overall performance of the final model, surpassing other prominent LLMs with larger parameter counts.
Contextual ASR Error Handling with LLMs Augmentation for Goal-Oriented Conversational AI (2025)

General-purpose automatic speech recognition (ASR) systems do not always perform well in goal-oriented dialogue. Existing ASR correction methods rely on prior user data or named entities. We extend correction to tasks that have no prior user data and exhibit linguistic flexibility such as lexical and syntactic variations. We propose a novel context augmentation with a large language model and a ranking strategy that incorporates contextual information from the dialogue states of a goal-oriented conversational AI and its tasks. Our method ranks (1) n-best ASR hypotheses by their lexical and semantic similarity with context and (2) context by phonetic correspondence with ASR hypotheses. Evaluated in home improvement and cooking domains with real-world users, our method improves recall and F1 of correction by 34{\%} and 16{\%}, respectively, while maintaining precision and false positive rate. Users rated .8-1 point (out of 5) higher when our correction method worked properly, with no decrease due to false positives.
Federated Retrieval Augmented Generation for Multi-Product Question Answering (2025)

Recent advancements in Large Language Models and Retrieval-Augmented Generation have boosted interest in domain-specific question-answering for enterprise products. However, AI Assistants often face challenges in multi-product QA settings, requiring accurate responses across diverse domains. Existing multi-domain RAG-QA approaches either query all domains indiscriminately, increasing computational costs and LLM hallucinations, or rely on rigid resource selection, which can limit search results. We introduce MKP-QA, a novel multi-product knowledge-augmented QA framework with probabilistic federated search across domains and relevant knowledge. This method enhances multi-domain search quality by aggregating query-domain and query-passage probabilistic relevance. To address the lack of suitable benchmarks for multi-product QAs, we also present new datasets focused on three Adobe products: Adobe Experience Platform, Target, and Customer Journey Analytics. Our experiments show that MKP-QA significantly boosts multi-product RAG-QA performance in terms of both retrieval accuracy and response quality.
Luna: A Lightweight Evaluation Model to Catch Language Model Hallucinations with High Accuracy and Low Cost (2025)

Retriever-Augmented Generation (RAG) systems have become pivotal in enhancing the capabilities of language models by incorporating external knowledge retrieval mechanisms. However, a significant challenge in deploying these systems in industry applications is the detection and mitigation of hallucinations - instances where the model generates information that is not grounded in the retrieved context. Addressing this issue is crucial for ensuring the reliability and accuracy of responses generated by large language models (LLMs) in industry settings. Current hallucination detection techniques fail to deliver accuracy, low latency, and low cost simultaneously. We introduce Luna: a DeBERTA-large encoder, fine-tuned for hallucination detection in RAG settings. We demonstrate that Luna outperforms GPT-3.5 and commercial evaluation frameworks on the hallucination detection task, with 97{\%} and 91{\%} reduction in cost and latency, respectively. Luna`s generalization capacity across multiple industry verticals and out-of-domain data makes it a strong candidate for guardrailing industry LLM applications.
Seeing Beyond: Enhancing Visual Question Answering with Multi-Modal Retrieval (2025)

Multi-modal Large language models (MLLMs) have made significant strides in complex content understanding and reasoning. However, they still suffer from model hallucination and lack of specific knowledge when facing challenging questions. To address these limitations, retrieval augmented generation (RAG) has emerged as an effective solution. While incorporating knowledge has led to improvements, it also highlights the need for a more robust knowledge selection strategy. For multi-modal tasks, such as visual question answering (VQA), integrating all modalities is crucial in providing comprehensive information for accurate answers. Therefore, we propose to construct an encoder model for extracting joint embedding from all modalities, enabling alignment between the corresponding query and knowledge through contrastive learning. To further improve performance, we introduce an additional MLLM re-selection step, which selects the best matching knowledge from the top-k retrieved results of our alignment model. We evaluated our method, SeBe-VQA, on the Encyclopedic VQA dataset. Our knowledge retrieval results demonstrate the benefit of our multi-modal framework. By incorporating the retrieved knowledge along with the question, we achieve a significant performance improvement compared with the previous method and scenarios without knowledge provision.
AutoProteinEngine: A Large Language Model Driven Agent Framework for Multimodal AutoML in Protein Engineering (2025)

Protein engineering is important for various biomedical applications, but traditional approaches are often inefficient and resource-intensive. While deep learning (DL) models have shown promise, their implementation remains challenging for biologists without specialized computational expertise. To address this gap, we propose AutoProteinEngine (AutoPE), an innovative agent framework that leverages large language models (LLMs) for multimodal automated machine learning (AutoML) in protein engineering. AutoPE introduces a conversational interface that allows biologists without DL backgrounds to interact with DL models using natural language, lowering the entry barrier for protein engineering tasks. Our AutoPE uniquely integrates LLMs with AutoML to handle both protein sequence and graph modalities, automate hyperparameter optimization, and facilitate data retrieval from protein databases. We evaluated AutoPE through two real-world protein engineering tasks, demonstrating substantial improvements in model performance compared to traditional zero-shot and manual fine-tuning approaches. By bridging the gap between DL and biologists' domain expertise, AutoPE empowers researchers to leverage advanced computational tools without extensive programming knowledge.
Building a Family of Data Augmentation Models for Low-cost LLM Fine-tuning on the Cloud (2025)

Specializing LLMs in various domain-specific tasks has emerged as a critical step towards achieving high performance. However, the construction and annotation of datasets in specific domains are always very costly. Apart from using superior and expensive closed-source LLM APIs to construct datasets, some open-source models have become strong enough to handle dataset construction in many scenarios. Thus, we present a family of data augmentation models designed to significantly improve the efficiency for model fine-tuning. These models, trained based on sufficiently small LLMs, support key functionalities with low inference costs: instruction expansion, instruction refinement, and instruction-response pair expansion. To fulfill this goal, we first construct an automatic data collection system with seed datasets generated from both public repositories and our in-house datasets. This system leverages powerful LLMs to expand, refine and re-write the instructions and responses, incorporating quality assessment techniques. Following this, we introduce the training process of our models, which effectively distills task-solving and text synthesis abilities from teacher LLMs. Finally, we demonstrate how we integrate these functionalities into a machine learning platform to support low-cost LLM fine-tuning from both dataset preparation and training perspectives for users. Experiments and an application study prove the effectiveness of our approach.
Where do LLMs Encode the Knowledge to Assess the Ambiguity? (2025)

Recently, large language models (LLMs) have shown remarkable performance across various natural language processing tasks, thanks to their vast amount of knowledge. Nevertheless, they often generate unreliable responses. A common example is providing a single biased answer to an ambiguous question that could have multiple correct answers. To address this issue, in this study, we discuss methods to detect such ambiguous samples. More specifically, we propose a classifier that uses a representation from an intermediate layer of the LLM as input. This is based on observations from previous research that representations of ambiguous samples in intermediate layers are closer to those of relevant label samples in the embedding space, but not necessarily in higher layers. The experimental results demonstrate that using representations from intermediate layers detects ambiguous input prompts more effectively than using representations from the final layer. Furthermore, in this study, we propose a method to train such classifiers without ambiguity labels, as most datasets lack labels regarding the ambiguity of samples, and evaluate its effectiveness.
On the effective transfer of knowledge from English to Hindi Wikipedia (2025)

Although Wikipedia is the largest multilingual encyclopedia, it remains inherently incomplete. There is a significant disparity in the quality of content between high-resource languages (HRLs, e.g., English) and low-resource languages (LRLs, e.g., Hindi), with many LRL articles lacking adequate information. To bridge these content gaps, we propose a lightweight framework to enhance knowledge equity between English and Hindi. In case the English Wikipedia page is not up-to-date, our framework extracts relevant information from external resources readily available (such as English books), and adapts it to align with Wikipedia`s distinctive style, including its \textit{neutral point of view} (NPOV) policy, using in-context learning capabilities of large language models. The adapted content is then machine-translated into Hindi for integration into the corresponding Wikipedia articles. On the other hand, if the English version is comprehensive and up-to-date, the framework directly transfers knowledge from English to Hindi. Our framework effectively generates new content for Hindi Wikipedia sections, enhancing Hindi Wikipedia articles respectively by 65{\%} and 62{\%} according to automatic and human judgment-based evaluations.
BackMATH: Towards Backward Reasoning for Solving Math Problems Step by Step (2025)

Large language models (LLMs) have achieved impressive results in reasoning, particularly in multi-step reasoning tasks. However, when faced with more complex mathematical problems, the performance of LLMs drops significantly. To address this issue, in this paper, we propose a backward reasoning dataset, BackMATH-Data. The dataset comprises approximately 14K backward reasoning problems and 100K reasoning steps. It follows a result-oriented approach, to construct backward reasoning problems by swapping the reasoning results with specific solving conditions in the original problems.Additionally, we introduce Backward-reasoning Process-supervision Reward Model (BackPRM) and BackMATH-LLM. BackPRM supervises the quality of the generated backward reasoning problems, while BackMATH-LLM is designed for mathematical reasoning. BackMATH-LLM is fine-tuned and enhanced through reinforcement learning by supervising the quality of backward reasoning problems and by providing feedback on reasoning steps, thereby improving the mathematical reasoning capabilities of LLMs.Extensive experiments demonstrate that our model achieves an accuracy of 68.1{\%} on the GSM8K dataset and 21.9{\%} on the MATH dataset, exceeding the SOTA by 1.6{\%} and 2.1{\%} respectively.