GenAI4ELab/papercli-papers huggingface dataset:
CUNI-NL@IWSLT 2025: End-to-end Offline Speech Translation and Instruction Following with LLMs
Authors: Nam Luu; Ondřej Bojar
Venue: ACL 2025 (Proceedings of the 22nd International Conference on Spoken Language Translation (IWSLT 2025))

Abstract: This paper describes the CUNI-NL team’s submission to the IWSLT 2025 Offline Speech Translation and Instruction Following tasks, focusing on transcribing the English audio, and translating the English audio to German text. Our systems follow the end-to-end approach, where each system consists of a pretrained, frozen speech encoder, along with a medium-sized large language model fine-tuned with LoRA on three tasks: 1) transcribing the English audio; 2) directly translating the English audio to German text; and 3) a combination of the above two tasks, i.e. simultaneously transcribing the English audio and translating the English audio to German text.
GMU Systems for the IWSLT 2025 Low-Resource Speech Translation Shared Task
Authors: Chutong Meng; Antonios Anastasopoulos
Venue: ACL 2025 (Proceedings of the 22nd International Conference on Spoken Language Translation (IWSLT 2025))

Abstract: This paper describes the GMU systems for the IWSLT 2025 low-resource speech translation shared task. We trained systems for all language pairs, except for Levantine Arabic. We fine-tuned SeamlessM4T-v2 for automatic speech recognition (ASR), machine translation (MT), and end-to-end speech translation (E2E ST). The ASR and MT models are also used to form cascaded ST systems. Additionally, we explored various training paradigms for E2E ST fine-tuning, including direct E2E fine-tuning, multi-task training, and parameter initialization using components from fine-tuned ASR and/or MT models. Our results show that (1) direct E2E fine-tuning yields strong results; (2) initializing with a fine-tuned ASR encoder improves ST performance on languages SeamlessM4T-v2 has not been trained on; (3) multi-task training can be slightly helpful.
BeaverTalk: Oregon State University’s IWSLT 2025 Simultaneous Speech Translation System
Authors: Matthew Raffel; Victor Agostinelli III; Lizhong Chen
Venue: ACL 2025 (Proceedings of the 22nd International Conference on Spoken Language Translation (IWSLT 2025))

Abstract: This paper discusses the construction, fine-tuning, and deployment of BeaverTalk, a cascaded system for speech-to-text translation as part of the IWSLT 2025 simultaneous translation task. The system architecture employs a VAD segmenter for breaking a speech stream into segments, Whisper Large V2 for automatic speech recognition (ASR), and Gemma 3 12B for simultaneous translation. Regarding the simultaneous translation LLM, it is fine-tuned via low-rank adaptors (LoRAs) for a conversational prompting strategy that leverages a single prior-sentence memory bank from the source language as context. The cascaded system participated in the English-German and English-Chinese language directions for both the low and high latency regimes. In particular, on the English-German task, the system achieves a BLEU of 24.64 and 27.83 at a StreamLAAL of 1837.86 and 3343.73, respectively. Then, on the English-Chinese task, the system achieves a BLEU of 34.07 and 37.23 at a StreamLAAL of 2216.99 and 3521.35, respectively.
CMU’s IWSLT 2025 Simultaneous Speech Translation System
Authors: Siqi Ouyang; Xi Xu; Lei Li
Venue: ACL 2025 (Proceedings of the 22nd International Conference on Spoken Language Translation (IWSLT 2025))

Abstract: This paper presents CMU’s submission to the IWSLT 2025 Simultaneous Speech Translation (SST) task for translating unsegmented English speech into Chinese and German text in a streaming manner. Our end-to-end speech-to-text system integrates a chunkwise causal Wav2Vec 2.0 speech encoder, an adapter, and the Qwen2.5-7B-Instruct as the decoder. We use a two-stage simultaneous training procedure on robust speech segments synthesized from LibriSpeech, CommonVoice, and VoxPopuli datasets, utilizing standard cross-entropy loss. Our model supports adjustable latency through a configurable latency multiplier. Experimental results demonstrate that our system achieves 44.3 BLEU for English-to-Chinese and 25.1 BLEU for English-to-German translations on the ACL60/60 development set, with computation-aware latencies of 2.7 seconds and 2.3 seconds, and theoretical latencies of 2.2 and 1.7 seconds, respectively.
JHU IWSLT 2025 Low-resource System Description
Authors: Nathaniel Romney Robinson; Niyati Bafna; Xiluo He; Tom Lupicki; Lavanya Shankar; Cihan Xiao; Qi Sun; Kenton Murray; David Yarowsky
Venue: ACL 2025 (Proceedings of the 22nd International Conference on Spoken Language Translation (IWSLT 2025))

Abstract: We present the Johns Hopkins University’s submission to the 2025 IWSLT Low-Resource Task. We competed on all 10 language pairs. Our approach centers around ensembling methods – specifically Minimum Bayes Risk Decoding. We find that such ensembling often improves performance only slightly over the best performing stand-alone model, and that in some cases it can even hurt performance slightly.
SYSTRAN @ IWSLT 2025 Low-resource track
Authors: Marko Avila; Josep Crego
Venue: ACL 2025 (Proceedings of the 22nd International Conference on Spoken Language Translation (IWSLT 2025))

Abstract: SYSTRAN submitted systems for one language pair in the 2025 Low-Resource Language Track. Our main contribution lies in the tight coupling and light fine-tuning of an ASR encoder (Whisper) with a neural machine translation decoder (NLLB), forming an efficient speech translation pipeline. We present the modeling strategies and optimizations implemented to build a system that, unlike large-scale end-to-end models, performs effectively under constraints of limited training data and computational resources. This approach enables the development of high-quality speech translation in low-resource settings, while ensuring both efficiency and scalability. We also conduct a comparative analysis of our proposed system against various paradigms, including a cascaded Whisper+NLLB setup and direct end-to-end fine-tuning of Whisper.
IIITH-BUT system for IWSLT 2025 low-resource Bhojpuri to Hindi speech translation
Authors: Bhavana Akkiraju; Aishwarya Pothula; Santosh Kesiraju; Anil Vuppala
Venue: ACL 2025 (Proceedings of the 22nd International Conference on Spoken Language Translation (IWSLT 2025))

Abstract: This paper presents the submission of IIITH-BUT to the IWSLT 2025 shared task on speech translation for the low-resource Bhojpuri-Hindi language pair. We explored the impact of hyperparameter optimisation and data augmentation techniques on the performance of the SeamlessM4T model fine-tuned for this specific task. We systematically investigated a range of hyperparameters including learning rate schedules, number of update steps, warm-up steps, label smoothing, and batch sizes; and report their effect on translation quality. To address data scarcity, we applied speed perturbation and SpecAugment and studied their effect on translation quality. We also examined the use of cross-lingual signal through joint training with Marathi and Bhojpuri speech data. Our experiments reveal that careful selection of hyperparameters and the application of simple yet effective augmentation techniques significantly improve performance in low-resource settings. We also analysed the translation hypotheses to understand various kinds of errors that impacted the translation quality in terms of BLEU
MLLP-VRAIN UPV system for the IWSLT 2025 Simultaneous Speech Translation Translation task
Authors: Jorge Iranzo-Sánchez; Javier Iranzo-Sanchez; Adrià Giménez Pastor; Jorge Civera Saiz; Alfons Juan
Venue: ACL 2025 (Proceedings of the 22nd International Conference on Spoken Language Translation (IWSLT 2025))

Abstract: This work describes the participation of the MLLP-VRAIN research group in the shared task of the IWSLT 2025 Simultaneous Speech Translation track. Our submission addresses the unique challenges of real-time translation of long-form speech by developing a modular cascade system that adapts strong pre-trained models to streaming scenarios. We combine Whisper Large-V3-Turbo for ASR with the multilingual NLLB-3.3B model for MT, implementing lightweight adaptation techniques rather than training new end-to-end models from scratch. Our approach employs document-level adaptation with prefix training to enhance the MT model’s ability to handle incomplete inputs, while incorporating adaptive emission policies including a wait-k strategy and RALCP for managing the translation stream. Specialized buffer management techniques and segmentation strategies ensure coherent translations across long audio sequences. Experimental results on the ACL60/60 dataset demonstrate that our system achieves a favorable balance between translation quality and latency, with a BLEU score of 31.96 and non-computational-aware StreamLAAL latency of 2.94 seconds. Our final model achieves a preliminary score on the official test set (IWSLT25Instruct) of 29.8 BLEU. Our work demonstrates that carefully adapted pre-trained components can create effective simultaneous translation systems for long-form content without requiring extensive in-domain parallel data or specialized end-to-end training.
Instituto de Telecomunicações at IWSLT 2025: Aligning Small-Scale Speech and Language Models for Speech-to-Text Learning
Authors: Giuseppe Attanasio; Sonal Sannigrahi; Ben Peters; André F. T. Martins
Venue: ACL 2025 (Proceedings of the 22nd International Conference on Spoken Language Translation (IWSLT 2025))

Abstract: This paper presents Instituto de Telecomunicações’s submission to the IWSLT 2025 Shared Task on Instruction Following Speech Processing. We submit results for the Short Track, i.e., speech recognition, translation, and spoken question answering. Our model is a unified speech-to-text model that integrates a pretrained continuous speech encoder and text decoder through a first phase of modality alignment and a second phase of instruction fine-tuning. Crucially, we focus on using small-scale language model backbones (< 2B) and restrict to high-quality, CC-BY data along with synthetic data generation to supplement existing resources.
Bemba Speech Translation: Exploring a Low-Resource African Language
Authors: Muhammad Hazim Al Farouq; Aman Kassahun Wassie; Yasmin Moslem
Venue: ACL 2025 (Proceedings of the 22nd International Conference on Spoken Language Translation (IWSLT 2025))

Abstract: This paper describes our system submission to the International Conference on Spoken Language Translation (IWSLT 2025), low-resource languages track, namely for Bemba-to-English speech translation. We built cascaded speech translation systems based on Whisper and NLLB-200, and employed data augmentation techniques, such as back-translation. We investigate the effect of using synthetic data and discuss our experimental setup.
NAIST Offline Speech Translation System for IWSLT 2025
Authors: Ruhiyah Faradishi Widiaputri; Haotian Tan; Jan Meyer Saragih; Yuka Ko; Katsuhito Sudoh; Satoshi Nakamura; Sakriani Sakti
Venue: ACL 2025 (Proceedings of the 22nd International Conference on Spoken Language Translation (IWSLT 2025))

Abstract: This paper presents NAIST’s submission to the offline speech translation task of the IWSLT 2025 evaluation campaign, focusing on English-to-German and English-to-Chinese translation. We implemented both cascade and end-to-end frameworks using various components. For the cascade approach, we used Whisper and SALMONN as automatic speech recognition systems, each paired with Qwen2.5 large language model (LLM) for translation. In the end-to-end setting, we used SALMONN as speech translation and also built a custom model combining the Whisper encoder, DeCo projector, and Qwen2.5 LLM. To further leverage the large language model capabilities, we experimented with different prompting strategies. Additionally, since long speech inputs are segmented for processing, we applied hypothesis combination techniques to generate the final translation output. Our results show that combining Whisper and LLMs can yield strong translation performance, even without further fine-tuning in the cascade setup. Moreover, our proposed end-to-end architecture achieved competitive results, despite being trained on significantly less data compared to SALMONN. Finally, we decided to use both SALMONN as an end-to-end speech translation model and our proposed end-to-end model for our IWSLT 2025 submission for both language pairs.
NAIST Simultaneous Speech Translation System for IWSLT 2025
Authors: Haotian Tan; Ruhiyah Faradishi Widiaputri; Jan Meyer Saragih; Yuka Ko; Katsuhito Sudoh; Satoshi Nakamura; Sakriani Sakti
Venue: ACL 2025 (Proceedings of the 22nd International Conference on Spoken Language Translation (IWSLT 2025))

Abstract: This paper describes the NAIST submission to the English-to-German, Japanese, Chinese Simultaneous Speech-to-Text track at IWSLT 2025. Last year, our system was based on an end-to-end speech-to-text translation model that combined HuBERT and mBART. This year, the system consists of a Whisper encoder, the DeCo compressive projector, and the Qwen large language model. The simultaneous translation (SimulST) system is implemented by applying a local agreement policy to an offline-trained translation model. For the streaming translation (StreamST) system, we integrate an online version of the SHAS segmenter into our SimulST architecture. Our results demonstrate that adopting LLMs as the backbone architecture for speech translation tasks yields strong translation performance. Additionally, leveraging robust segmentation capability of SHAS for StreamST achieves good quality-latency trade-off when processing unbounded audio streams.
Efficient Speech Translation through Model Compression and Knowledge Distillation
Authors: Yasmin Moslem
Venue: ACL 2025 (Proceedings of the 22nd International Conference on Spoken Language Translation (IWSLT 2025))

Abstract: Efficient deployment of large audio-language models for speech translation remains challenging due to their significant computational requirements. In this paper, we address this challenge through our system submissions to the ‘Model Compression’ track at the International Conference on Spoken Language Translation (IWSLT 2025). We experiment with a combination of approaches including iterative layer pruning based on layer importance evaluation, low-rank adaptation with 4-bit quantization (QLoRA), and knowledge distillation. In our experiments, we use Qwen2-Audio-7B-Instruct for speech translation into German and Chinese. Our pruned (student) models achieve up to a 50% reduction in both model parameters and storage footprint, while retaining 97-100% of the translation quality of the in-domain (teacher) models.
Simultaneous Translation with Offline Speech and LLM Models in CUNI Submission to IWSLT 2025
Authors: Dominik Macháček; Peter Polák
Venue: ACL 2025 (Proceedings of the 22nd International Conference on Spoken Language Translation (IWSLT 2025))

Abstract: This paper describes Charles University submission to the Simultaneous Speech Translation Task of the IWSLT 2025. We cover all four language pairs with a direct or cascade approach. The backbone of our systems is the offline Whisper speech model, which we use for both translation and transcription in simultaneous mode with the state-of-the-art simultaneous policy AlignAtt. We further improve the performance by prompting to inject in-domain terminology, and we accommodate context. Our cascaded systems further use EuroLLM for unbounded simultaneous translation. Compared to the Organizers’ baseline, our systems improve by 2 BLEU points on Czech to English and 13-22 BLEU points on English to German, Chinese and Japanese on the development sets. Additionally, we also propose a new enhanced measure of speech recognition latency.
Effectively combining Phi-4 and NLLB for Spoken Language Translation: SPRING Lab IITM’s submission to Low Resource Multilingual Indic Track
Authors: Sankalpa Sarkar; Samriddhi Kashyap; Advait Joglekar; Srinivasan Umesh
Venue: ACL 2025 (Proceedings of the 22nd International Conference on Spoken Language Translation (IWSLT 2025))

Abstract: This paper presents the methodologies implemented for Spoken Language Translation for the language pairs Hindi-English, Bengali-English and Tamil-English for the Low Resource Multilingual Indic Track of The International Conference on Spoken Language Translation (IWSLT) for 2025. We adopt a cascaded approach and use a fine-tuned Phi-4 multimodal instruct model for Automatic Speech Recognition(ASR) and a fine-tuned NLLB model for Machine Translation(MT).
HITSZ’s End-To-End Speech Translation Systems Combining Sequence-to-Sequence Auto Speech Recognition Model and Indic Large Language Model for IWSLT 2025 in Indic Track
Authors: Xuchen Wei; Yangxin Wu; Yaoyin Zhang; Henglyu Liu; Kehai Chen; Xuefeng Bai; Min Zhang
Venue: ACL 2025 (Proceedings of the 22nd International Conference on Spoken Language Translation (IWSLT 2025))

Abstract: This paper presents HITSZ’s submission for the IWSLT 2025 Indic track, focusing on speech-to-text translation (ST) for English-to-Indic and Indic-to-English language pairs. To enhance translation quality in this low-resource scenario, we propose an end-to-end system integrating the pre-trained Whisper automated speech recognition (ASR) model with Krutrim, an Indic-specialized large language model (LLM). Experimental results demonstrate that our end-to-end system achieved average BLEU scores of 28.88 for English-to-Indic directions and 27.86 for Indic-to-English directions. Furthermore, we investigated the Chain-of-Thought (CoT) method. While this method showed potential for significant translation quality improvements on successfully parsed outputs (e.g. a 13.84 BLEU increase for Tamil-to-English), we observed challenges in ensuring the model consistently adheres to the required CoT output format.
Findings of the IWSLT 2025 Evaluation Campaign
Authors: Idris Abdulmumin; Victor Agostinelli; Tanel Alumäe; Antonios Anastasopoulos; Luisa Bentivogli; Ondřej Bojar; Claudia Borg; Fethi Bougares; Roldano Cattoni; Mauro Cettolo; Lizhong Chen; William Chen; Raj Dabre; Yannick Estève; Marcello Federico; Mark Fishel; Marco Gaido; Dávid Javorský; Marek Kasztelnik; Fortuné Kponou; Mateusz Krubiński; Tsz Kin Lam; Danni Liu; Evgeny Matusov; Chandresh Kumar Maurya; John P. McCrae; Salima Mdhaffar; Yasmin Moslem; Kenton Murray; Satoshi Nakamura; Matteo Negri; Jan Niehues; Atul Kr. Ojha; John E. Ortega; Sara Papi; Pavel Pecina; Peter Polák; Piotr Połeć; Ashwin Sankar; Beatrice Savoldi; Nivedita Sethiya; Claytone Sikasote; Matthias Sperber; Sebastian Stüker; Katsuhito Sudoh; Brian Thompson; Marco Turchi; Alex Waibel; Patrick Wilken; Rodolfo Zevallos; Vilém Zouhar; Maike Züfle
Venue: ACL 2025 (Proceedings of the 22nd International Conference on Spoken Language Translation (IWSLT 2025))

Abstract: This paper presents the outcomes of the shared tasks conducted at the 22nd International Workshop on Spoken Language Translation (IWSLT). The workshop addressed seven critical challenges in spoken language translation: simultaneous and offline translation, automatic subtitling and dubbing, model compression, speech-to-speech translation, dialect and low-resource speech translation, and Indic languages. The shared tasks garnered significant participation, with 32 teams submitting their runs. The field’s growing importance is reflected in the increasing diversity of shared task organizers and contributors to this overview paper, representing a balanced mix of industrial and academic institutions. This broad participation demonstrates the rising prominence of spoken language translation in both research and practical applications.
Temporal Information Retrieval via Time-Specifier Model Merging
Authors: SeungYoon Han; Taeho Hwang; Sukmin Cho; Soyeong Jeong; Hoyun Song; Huije Lee; Jong C. Park
Venue: ACL 2025 (Proceedings of the 3rd Workshop on Towards Knowledgeable Foundation Models (KnowFM))

Abstract: The rapid expansion of digital information and knowledge across structured and unstructured sources has heightened the importance of Information Retrieval (IR). While dense retrieval methods have substantially improved semantic matching for general queries, they consistently underperform on queries with explicit temporal constraints–often those containing numerical expressions and time specifiers such as “in 2015.” Existing approaches to Temporal Information Retrieval (TIR) improve temporal reasoning but often suffer from catastrophic forgetting, leading to reduced performance on non-temporal queries. To address this, we propose Time-Specifier Model Merging (TSM), a novel method that enhances temporal retrieval while preserving accuracy on non-temporal queries. TSM trains specialized retrievers for individual time specifiers and merges them into a unified model, enabling precise handling of temporal constraints without compromising non-temporal retrieval. Extensive experiments on both temporal and non-temporal datasets demonstrate that TSM significantly improves performance on temporally constrained queries while maintaining strong results on non-temporal queries, consistently outperforming other training methods. Our code is available at https://github.com/seungyoonee/TSM.
EdTec-ItemGen: Enhancing Retrieval-Augmented Item Generation Through Key Point Extraction
Authors: Alonso Palomino; David Buschhüter; Roland Roller; Niels Pinkwart; Benjamin Paassen
Venue: ACL 2025 (Proceedings of the 3rd Workshop on Towards Knowledgeable Foundation Models (KnowFM))

Abstract: A major bottleneck in exam construction involves designing test items (i.e., questions) that accurately reflect key content from domain-aligned curricular materials. For instance, during formative assessments in vocational education and training (VET), exam designers must generate updated test items that assess student learning progress while covering the full breadth of topics in the curriculum. Large language models (LLMs) can partially support this process, but effective use requires careful prompting and task-specific understanding. We propose a new key point extraction method for retrieval-augmented item generation that enhances the process of generating test items with LLMs. We exhaustively evaluated our method using a TREC-RAG approach, finding that prompting LLMs with key content rather than directly using full curricular text passages significantly improves item quality regarding key information coverage by 8%. To demonstrate these findings, we release EdTec-ItemGen, a retrieval-augmented item generation demo tool to support item generation in education.
Teaching Large Language Models to Express Knowledge Boundary from Their Own Signals
Authors: Lida Chen; Zujie Liang; Xintao Wang; Jiaqing Liang; Yanghua Xiao; Feng Wei; Jinglei Chen; Zhenghong Hao; Bing Han; Wei Wang
Venue: ACL 2025 (Proceedings of the 3rd Workshop on Towards Knowledgeable Foundation Models (KnowFM))

Abstract: Large language models (LLMs) have achieved great success, but their occasional content fabrication, or hallucination, limits their practical application. Hallucination arises because LLMs struggle to admit ignorance due to inadequate training on knowledge boundaries. We call it a limitation of LLMs that they can not accurately express their knowledge boundary, answering questions they know while admitting ignorance to questions they do not know. In this paper, we aim to teach LLMs to recognize and express their knowledge boundary, so they can reduce hallucinations caused by fabricating when they do not know. We propose CoKE, which first probes LLMs’ knowledge boundary via internal confidence given a set of questions, and then leverages the probing results to elicit the expression of the knowledge boundary. Extensive experiments show CoKE helps LLMs express knowledge boundaries, answering known questions while declining unknown ones, significantly improving in-domain and out-of-domain performance.