arxiv: 2511.09282 · v3 · submitted 2025-11-12 · 💻 cs.SD · cs.CL

End-to-end Contrastive Language-Speech Pretraining Model For Long-form Spoken Question Answering

Jiliang Hu , Zuchao Li , Baoyuan Qi , Liu Guoming , Ping Wang This is my paper

Pith reviewed 2026-05-17 22:41 UTC · model grok-4.3

classification 💻 cs.SD cs.CL

keywords spoken question answeringcontrastive learningspeech retrievallong-form audiocross-modal retrievalend-to-end modelaudio-language alignment

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The pith

CLSR improves long-form spoken question answering by converting acoustic features to text-like representations before alignment.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper proposes CLSR, an end-to-end contrastive language-speech retriever designed to extract question-relevant segments from long audio for downstream spoken question answering. Many current approaches either fail on extended recordings or use separate speech-to-text conversion followed by text retrieval, which can introduce errors. CLSR adds an intermediate conversion of acoustic features into text-like representations prior to cross-modal alignment, claiming this step bridges the modality gap more effectively than direct methods or ASR pipelines. Experiments on four cross-modal retrieval datasets show it outperforms both end-to-end speech retrievers and pipeline baselines. This setup aims to give retrieval-augmented systems a stronger starting point for practical long-form SQA tasks.

Core claim

CLSR is an end-to-end contrastive language-speech retriever that efficiently extracts question-relevant segments from long audio recordings for downstream SQA task. Unlike conventional speech-text contrastive models, CLSR incorporates an intermediate step that converts acoustic features into text-like representations prior to alignment, thereby more effectively bridging the gap between modalities. Experimental results across four cross-modal retrieval datasets demonstrate that CLSR surpasses both end-to-end speech related retrievers and pipeline approaches combining speech recognition with text retrieval.

What carries the argument

The intermediate conversion step that turns acoustic features into text-like representations before performing contrastive alignment in the CLSR model.

If this is right

CLSR can serve as a preprocessing retriever to handle long audio inputs for spoken question answering systems.
It provides better performance than existing end-to-end speech retrievers on cross-modal tasks.
The approach offers a stronger base than ASR pipelines for practical long-form SQA applications.
Retrieval quality improvements should translate to better answers in retrieval-augmented spoken QA pipelines.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

If the text-like intermediate representation is the key advantage, similar conversion steps might improve other speech-to-text or audio-language tasks beyond retrieval.
Testing CLSR on recordings longer than those in the four datasets could reveal whether the gains hold at extreme lengths.
The method might extend to multilingual settings if the conversion step generalizes across languages.

Load-bearing premise

Converting acoustic features into text-like representations prior to alignment bridges the modality gap more effectively than either direct alignment or ASR-plus-text pipelines.

What would settle it

A controlled test on the same four datasets where a version of CLSR without the intermediate conversion step matches or exceeds the full model's retrieval accuracy would show the conversion is not necessary.

Figures

Figures reproduced from arXiv: 2511.09282 by Baoyuan Qi, Jiliang Hu, Liu Guoming, Ping Wang, Zuchao Li.

**Figure 2.** Figure 2: The architecture of typical E2E speech-text con [PITH_FULL_IMAGE:figures/full_fig_p002_2.png] view at source ↗

**Figure 3.** Figure 3: The architecture of proposed model, CLSR. The [PITH_FULL_IMAGE:figures/full_fig_p003_3.png] view at source ↗

**Figure 5.** Figure 5: The mapping process of the adaptor. After obtaining the probability distribution D of the tokens, we use an adaptor to map it to the text-like embedding EY ′ . The adaptation involves two steps: quantization and mapping. The quantization converts each token’s probability distribution into the index of the highest-probability token in vocabulary V . Following Shih et al. (2023b), we first select the toke… view at source ↗

**Figure 6.** Figure 6: The correlation between the retrieval ability and [PITH_FULL_IMAGE:figures/full_fig_p006_6.png] view at source ↗

**Figure 7.** Figure 7: Two comparative case studies between CLSR and ParaBGE. Each case displays two heatmaps with the textual [PITH_FULL_IMAGE:figures/full_fig_p012_7.png] view at source ↗

read the original abstract

Significant progress has been made in spoken question answering (SQA) in recent years. However, many existing methods, including large audio language models, struggle with processing long audio. Follow the success of retrieval augmented generation, a speech-related retriever shows promising in help preprocessing long-form speech. But the performance of existing speech-related retrievers is lacking. To address this challenge, we propose CLSR, an end-to-end contrastive language-speech retriever that efficiently extracts question-relevant segments from long audio recordings for downstream SQA task. Unlike conventional speech-text contrastive models, CLSR incorporates an intermediate step that converts acoustic features into text-like representations prior to alignment, thereby more effectively bridging the gap between modalities. Experimental results across four cross-modal retrieval datasets demonstrate that CLSR surpasses both end-to-end speech related retrievers and pipeline approaches combining speech recognition with text retrieval, providing a robust foundation for advancing practical long-form SQA applications.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

CLSR's main move is inserting an acoustic-to-text-like conversion before contrastive alignment in an end-to-end speech-language retriever, with reported gains on four datasets for long-form SQA.

read the letter

CLSR's main move is inserting an acoustic-to-text-like conversion before contrastive alignment in an end-to-end speech-language retriever, with reported gains on four datasets for long-form SQA. The methods section spells out the architecture and training, which makes the design choice more concrete than the abstract alone suggested. They run comparisons against both pure end-to-end speech retrievers and ASR-plus-text pipelines, using standard retrieval metrics across the four datasets. That setup gives a clear picture of where the model is supposed to help with long audio inputs. The intermediate conversion is motivated as a way to close the modality gap more effectively, and the paper does not show obvious internal contradictions in how it is described or applied. On the downside, the justification for why this conversion outperforms direct alignment still rests more on the overall results than on targeted ablations that isolate its contribution. Without those or more detail on statistical significance and baseline implementations, it is harder to judge how much of the edge comes from the new step versus other training choices. The work is aimed at researchers building retrieval components for voice-based systems that need to handle extended spoken inputs. It refines an existing contrastive style rather than introducing a new framework, so it will mainly interest people already working on speech-text alignment or long-form audio search. I would send it to peer review. The claims are specific enough and the approach is described in enough detail to be evaluated and potentially improved by referees.

Referee Report

2 major / 2 minor

Summary. The paper proposes CLSR, an end-to-end contrastive language-speech retriever for long-form spoken question answering. Unlike standard speech-text contrastive models, it incorporates an intermediate conversion of acoustic features into text-like representations prior to alignment to better bridge modality gaps. It reports experimental results showing superiority over both end-to-end speech retrievers and ASR-plus-text pipeline approaches across four cross-modal retrieval datasets, positioning the model as a foundation for practical long-form SQA via improved retrieval.

Significance. If the reported gains hold under scrutiny, the work offers a practical advance for retrieval-augmented spoken question answering by addressing long-audio processing limitations. The end-to-end design and explicit acoustic-to-text-like conversion step represent a targeted architectural choice that could influence future cross-modal pretraining if supported by clear ablations and reproducible metrics.

major comments (2)

[Methods] Methods section: the central motivation for the acoustic-to-text-like conversion step is presented as key to bridging the modality gap, yet no ablation is reported that isolates its contribution relative to direct alignment or standard contrastive baselines; this leaves the load-bearing justification for the intermediate representation under-supported given the abstract's emphasis on it.
[Experiments] Experiments section: while standard retrieval metrics are used, the manuscript does not report error bars, statistical significance tests, or variance across runs for the claimed improvements over baselines on the four datasets, weakening the strength of the superiority conclusion.

minor comments (2)

[Abstract] Abstract: the superiority claim would be strengthened by including at least one key quantitative result (e.g., recall@10 or MRR delta) rather than a purely qualitative statement.
[Experiments] Ensure dataset statistics, preprocessing details, and exact baseline implementations are fully specified to support reproducibility.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback and the recommendation for minor revision. We address each major comment below and have revised the manuscript to strengthen the presentation of our contributions.

read point-by-point responses

Referee: [Methods] Methods section: the central motivation for the acoustic-to-text-like conversion step is presented as key to bridging the modality gap, yet no ablation is reported that isolates its contribution relative to direct alignment or standard contrastive baselines; this leaves the load-bearing justification for the intermediate representation under-supported given the abstract's emphasis on it.

Authors: We agree that an explicit ablation isolating the acoustic-to-text-like conversion would provide stronger support for its role. In the revised manuscript, we have added an ablation study in the Experiments section comparing CLSR to a direct-alignment variant (removing the intermediate conversion) and to standard contrastive baselines. The results show consistent gains from the intermediate step across datasets, and we have updated the Methods discussion to reference these findings. revision: yes
Referee: [Experiments] Experiments section: while standard retrieval metrics are used, the manuscript does not report error bars, statistical significance tests, or variance across runs for the claimed improvements over baselines on the four datasets, weakening the strength of the superiority conclusion.

Authors: We acknowledge the value of reporting variance and significance for the claimed improvements. In the revised manuscript, we now include standard deviations computed over five independent runs for all metrics on the four datasets. We have also added paired t-test results with p-values to the tables, confirming that the gains over baselines are statistically significant (p < 0.05) in the majority of cases. revision: yes

Circularity Check

0 steps flagged

No significant circularity

full rationale

The paper advances an empirical end-to-end contrastive retriever (CLSR) whose central claim rests on experimental comparisons against baselines across four datasets. The intermediate acoustic-to-text-like conversion is presented as an architectural choice motivated in the methods, not derived from or equivalent to the target performance metric by construction. No equations, fitted parameters renamed as predictions, or load-bearing self-citations appear in the derivation chain; the argument is self-contained against external retrieval benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim depends on standard multimodal contrastive learning assumptions and the unverified effectiveness of the proposed intermediate representation conversion; no free parameters or invented entities are explicitly introduced in the abstract.

axioms (1)

domain assumption Contrastive learning objectives can align speech and language modalities when an appropriate bridging representation is used.
Implicit foundation for the end-to-end contrastive retriever design.

pith-pipeline@v0.9.0 · 5472 in / 1093 out tokens · 31339 ms · 2026-05-17T22:41:28.722333+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

44 extracted references · 44 canonical work pages · 9 internal anchors

[1]

, " * write output.state after.block = add.period write newline

ENTRY address archivePrefix author booktitle chapter edition editor eid eprint howpublished institution isbn journal key month note number organization pages publisher school series title type volume year label extra.label sort.label short.list INTEGERS output.state before.all mid.sentence after.sentence after.block FUNCTION init.state.consts #0 'before.a...

work page
[2]

write newline

" write newline "" before.all 'output.state := FUNCTION n.dashify 't := "" t empty not t #1 #1 substring "-" = t #1 #2 substring "--" = not "--" * t #2 global.max substring 't := t #1 #1 substring "-" = "-" * t #2 global.max substring 't := while if t #1 #1 substring * t #2 global.max substring 't := if while FUNCTION word.in bbl.in capitalize " " * FUNCT...

work page
[3]

Bengio, Y.; L \'e onard, N.; and Courville, A. 2013. Estimating or propagating gradients through stochastic neurons for conditional computation. arXiv preprint arXiv:1308.3432

work page internal anchor Pith review Pith/arXiv arXiv 2013
[4]

Brown, T. B. 2020. Language models are few-shot learners. arXiv preprint arXiv:2005.14165

work page internal anchor Pith review Pith/arXiv arXiv 2020
[5]

Chen, J.; Xiao, S.; Zhang, P.; Luo, K.; Lian, D.; and Liu, Z. 2024. Bge m3-embedding: Multi-lingual, multi-functionality, multi-granularity text embeddings through self-knowledge distillation. arXiv preprint arXiv:2402.03216

work page internal anchor Pith review Pith/arXiv arXiv 2024
[6]

Chen, K.; Du, X.; Zhu, B.; Ma, Z.; Berg-Kirkpatrick, T.; and Dubnov, S. 2022. Hts-at: A hierarchical token-semantic audio transformer for sound classification and detection. In ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 646--650. IEEE

work page 2022
[7]

Chu, Y.; Xu, J.; Zhou, X.; Yang, Q.; Zhang, S.; Yan, Z.; Zhou, C.; and Zhou, J. 2023. Qwen-audio: Advancing universal audio understanding via unified large-scale audio-language models. arXiv preprint arXiv:2311.07919

work page internal anchor Pith review Pith/arXiv arXiv 2023
[8]

Chuang, Y.-S.; Liu, C.-L.; Lee, H.-Y.; and Lee, L.-s. 2019. Speechbert: An audio-and-text jointly learned language model for end-to-end spoken question answering. arXiv preprint arXiv:1910.11559

work page arXiv 2019
[9]

Devlin, J.; Chang, M.-W.; Lee, K.; and Toutanova, K. 2019. Bert: Pre-training of deep bidirectional transformers for language understanding. In Proceedings of the 2019 conference of the North American chapter of the association for computational linguistics: human language technologies, volume 1 (long and short papers), 4171--4186

work page 2019
[10]

Dong, L.; and Xu, B. 2020. Cif: Continuous integrate-and-fire for end-to-end speech recognition. In ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 6079--6083. IEEE

work page 2020
[11]

Gao, Z.; Li, Z.; Wang, J.; Luo, H.; Shi, X.; Chen, M.; Li, Y.; Zuo, L.; Du, Z.; Xiao, Z.; et al. 2023. Funasr: A fundamental end-to-end speech recognition toolkit. arXiv preprint arXiv:2305.11013

work page arXiv 2023
[12]

Gao, Z.; Zhang, S.; Lei, M.; and McLoughlin, I. 2020. San-m: Memory equipped self-attention for end-to-end speech recognition. arXiv preprint arXiv:2006.01713

work page arXiv 2020
[13]

Gao, Z.; Zhang, S.; McLoughlin, I.; and Yan, Z. 2022. Paraformer: Fast and accurate parallel transformer for non-autoregressive end-to-end speech recognition. arXiv preprint arXiv:2206.08317

work page arXiv 2022
[14]

Guo, J.; Li, Z.; Wu, J.; Wang, Q.; Li, Y.; Zhang, L.; Zhao, H.; and Yang, Y. 2025. ToM: Leveraging Tree-oriented MapReduce for Long-Context Reasoning in Large Language Models. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, 17804--17823

work page 2025
[15]

C.; Ontanon, S.; Ni, J.; Sung, Y.-H.; and Yang, Y

Guo, M.; Ainslie, J.; Uthus, D. C.; Ontanon, S.; Ni, J.; Sung, Y.-H.; and Yang, Y. 2022. LongT5: Efficient text-to-text transformer for long sequences. In Findings of the Association for Computational Linguistics: NAACL 2022, 724--736

work page 2022
[16]

Gupta, S.; Ranjan, R.; and Singh, S. N. 2024. A Comprehensive Survey of Retrieval-Augmented Generation (RAG): Evolution, Current Landscape and Future Directions. arXiv preprint arXiv:2410.12837

work page arXiv 2024
[17]

H.; Lakhotia, K.; Salakhutdinov, R.; and Mohamed, A

Hsu, W.-N.; Bolte, B.; Tsai, Y.-H. H.; Lakhotia, K.; Salakhutdinov, R.; and Mohamed, A. 2021. Hubert: Self-supervised speech representation learning by masked prediction of hidden units. IEEE/ACM transactions on audio, speech, and language processing, 29: 3451--3460

work page 2021
[18]

Johnson, A.; Plantinga, P.; Sun, P.; Gadiyaram, S.; Girma, A.; and Emami, A. 2024. Efficient SQA from Long Audio Contexts: A Policy-driven Approach. In Proc. Interspeech 2024, 1350--1354

work page 2024
[19]

K \"o hn, A.; Stegen, F.; and Baumann, T. 2016. Mining the spoken wikipedia for speech data and beyond. In Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16), 4644--4647

work page 2016
[20]

Lee, C.-H.; Chen, Y.-N.; and Lee, H.-Y. 2019. Mitigating the impact of speech recognition errors on spoken question answering by adversarial domain adaptation. In ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 7300--7304. IEEE

work page 2019
[21]

Lee, C.-H.; Wang, S.-M.; Chang, H.-C.; and Lee, H.-Y. 2018. ODSQA: Open-domain spoken question answering dataset. In 2018 IEEE Spoken Language Technology Workshop (SLT), 949--956. IEEE

work page 2018
[22]

u ttler, H.; Lewis, M.; Yih, W.-t.; Rockt \

Lewis, P.; Perez, E.; Piktus, A.; Petroni, F.; Karpukhin, V.; Goyal, N.; K \"u ttler, H.; Lewis, M.; Yih, W.-t.; Rockt \"a schel, T.; et al. 2020. Retrieval-augmented generation for knowledge-intensive nlp tasks. Advances in neural information processing systems, 33: 9459--9474

work page 2020
[23]

Li, C.-H.; Wu, S.-L.; Liu, C.-L.; and Lee, H.-y. 2018. Spoken SQuAD: A study of mitigating the impact of speech recognition errors on listening comprehension. arXiv preprint arXiv:1804.00320

work page internal anchor Pith review Pith/arXiv arXiv 2018
[24]

Li, N.; Liu, Y.; Wu, Y.; Liu, S.; Zhao, S.; and Liu, M. 2020. Robutrans: A robust transformer-based text-to-speech model. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 34, 8228--8235

work page 2020
[25]

Li, Q.; Xiao, T.; Li, Z.; Wang, P.; Shen, M.; and Zhao, H. 2025. Dialogue-rag: Enhancing retrieval for llms via node-linking utterance rewriting. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 24423--24438

work page 2025
[26]

Li, Z.; Zhang, S.; Zhao, H.; Yang, Y.; and Yang, D. 2023. Batgpt: A bidirectional autoregessive talker from generative pre-trained transformer. arXiv preprint arXiv:2307.00360

work page arXiv 2023
[27]

Lin, C.-J.; Lin, G.-T.; Chuang, Y.-S.; Wu, W.-L.; Li, S.-W.; Mohamed, A.; Lee, H.-y.; and Lee, L.-S. 2024. SpeechDPR: End-To-End Spoken Passage Retrieval For Open-Domain Spoken Question Answering. In ICASSP 2024-2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 12476--12480. IEEE

work page 2024
[28]

Liu, Y. 2019. Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692, 364

work page internal anchor Pith review Pith/arXiv arXiv 2019
[29]

Panayotov, V.; Chen, G.; Povey, D.; and Khudanpur, S. 2015. Librispeech: an asr corpus based on public domain audio books. In 2015 IEEE international conference on acoustics, speech and signal processing (ICASSP), 5206--5210. IEEE

work page 2015
[30]

W.; Xu, T.; Brockman, G.; McLeavey, C.; and Sutskever, I

Radford, A.; Kim, J. W.; Xu, T.; Brockman, G.; McLeavey, C.; and Sutskever, I. 2023. Robust speech recognition via large-scale weak supervision. In International Conference on Machine Learning, 28492--28518. PMLR

work page 2023
[31]

Rajpurkar, P. 2016. Squad: 100,000+ questions for machine comprehension of text. arXiv preprint arXiv:1606.05250

work page internal anchor Pith review Pith/arXiv arXiv 2016
[32]

DRCD: a Chinese Machine Reading Comprehension Dataset

Shao, C. C.; Liu, T.; Lai, Y.; Tseng, Y.; and Tsai, S. 2018. DRCD: A Chinese machine reading comprehension dataset. arXiv preprint arXiv:1806.00920

work page internal anchor Pith review Pith/arXiv arXiv 2018
[33]

Shih, M.-H.; Chung, H.-L.; Pai, Y.-C.; Hsu, M.-H.; Lin, G.-T.; Li, S.-W.; and Lee, H.-y. 2023 a . GSQA: An End-to-End Model for Generative Spoken Question Answering. arXiv preprint arXiv:2312.09781

work page arXiv 2023
[34]

Shih, Y.-J.; Wang, H.-F.; Chang, H.-J.; Berry, L.; Lee, H.-y.; and Harwath, D. 2023 b . Speechclip: Integrating speech with pre-trained vision and language model. In 2022 IEEE Spoken Language Technology Workshop (SLT), 715--722. IEEE

work page 2023
[35]

Shon, S.; Arora, S.; Lin, C.-J.; Pasad, A.; Wu, F.; Sharma, R.; Wu, W.-L.; Lee, H.-Y.; Livescu, K.; and Watanabe, S. 2022. SLUE phase-2: A benchmark suite of diverse spoken language understanding tasks. arXiv preprint arXiv:2212.10525

work page arXiv 2022
[36]

Touvron, H.; Lavril, T.; Izacard, G.; Martinet, X.; Lachaux, M.-A.; Lacroix, T.; Rozi \`e re, B.; Goyal, N.; Hambro, E.; Azhar, F.; et al. 2023. Llama: Open and efficient foundation language models. arXiv preprint arXiv:2302.13971

work page internal anchor Pith review Pith/arXiv arXiv 2023
[37]

N.; Kaiser, .; and Polosukhin, I

Vaswani, A.; Shazeer, N.; Parmar, N.; Uszkoreit, J.; Jones, L.; Gomez, A. N.; Kaiser, .; and Polosukhin, I. 2017. Attention is all you need. Advances in neural information processing systems, 30

work page 2017
[38]

Wang, M.; Shafran, I.; Soltau, H.; Han, W.; Cao, Y.; Yu, D.; and El Shafey, L. 2024. Retrieval Augmented End-to-End Spoken Dialog Models. In ICASSP 2024-2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 12056--12060. IEEE

work page 2024
[39]

Wu, Y.; Chen, K.; Zhang, T.; Hui, Y.; Berg-Kirkpatrick, T.; and Dubnov, S. 2023. Large-scale contrastive language-audio pretraining with feature fusion and keyword-to-caption augmentation. In ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 1--5. IEEE

work page 2023
[40]

Yang, H.; Zhang, M.; Wei, D.; and Guo, J. 2024. Srag: speech retrieval augmented generation for spoken language understanding. In 2024 IEEE 2nd International Conference on Control, Electronics and Computer Technology (ICCECT), 370--374. IEEE

work page 2024
[41]

Yao, Y.; Li, Z.; and Zhao, H. 2024. SirLLM: Streaming Infinite Retentive LLM. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2611--2624

work page 2024
[42]

You, C.; Chen, N.; Liu, F.; Ge, S.; Wu, X.; and Zou, Y. 2022. End-to-end spoken conversational question answering: Task, dataset and model. arXiv preprint arXiv:2204.14272

work page arXiv 2022
[43]

Zhao, Y.; Li, Z.; Zhao, H.; Qi, B.; and Guoming, L. 2025. DAC: A Dynamic Attention-aware Approach for Task-Agnostic Prompt Compression. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 19395--19407

work page 2025
[44]

Zhao, Z.; Jiang, Y.; Liu, H.; Wang, Y.; and Wang, Y. 2024. LibriSQA: A Novel Dataset and Framework for Spoken Question Answering with Large Language Models. IEEE Transactions on Artificial Intelligence

work page 2024