Pith. sign in

REVIEW 3 major objections 5 minor 50 references

Open Your Model's Eyes: Video and Context-Aware Multimodal Backchannel Prediction

T0 review · 3 major / 5 minor · reviewed 2026-08-01 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗

Pith's one-line read Backchannel prediction improves with video when aligned hierarchically, not concatenated

desk verdict First backchannel predictor to actually condition on video, with consistent gains and solid ablations, but the unsupervised pretraining stage may have seen test dialogues—that has to be resolved before the headline numbers are trusted. read the letter →

arxiv 2607.22729 v1 pith:VYWDBG32 submitted 2026-07-22 cs.CV cs.CL

Min-Jae Kim , Jun-Yeong Moon , Mujeen Sung , Gyeong-Moon Park This is my paper · ORCID
classification cs.CVcs.CL
keywords backchannelpredictionmultimodalalignmentvisualmodalitycontext-awarecurriculumlearningcross-attentionempathydetectionspokendialogue
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

The paper tries to establish that visual signals—facial expressions and gestures—improve automatic backchannel prediction, but only when fused through a hierarchy that lets audio lead, text provide context, and video carry reactive cues. It introduces CAMA-BC, which first pre-trains on unlabeled whole dialogues via Context Alignment (MMA-CA) and then fine-tunes on labeled backchannels via Backchannel Alignment (MMA-BA). Across Korean and English datasets, the framework reports higher Macro F1 than prior audio-text models and than models that simply concatenate video features, with the largest gains on the rare 'Empathy' class. If true, this points toward spoken-dialogue systems that react to a listener's visual engagement.

What carries the argument

Multi-Layer Multimodal Alignment (MMA): a loss applied at the last three layers of three modality encoders (audio, text, video) that pulls average-pooled sample embeddings close across modalities while pushing away other samples, augmented with hierarchical cross-attention pairs (text-audio, video-audio, video-text). Shared cross-attention weights across layers let the model learn modality-agnostic interaction patterns, and the same attended features feed the final classifier. The two-stage training (MMA-CA then MMA-BA) is the other core component: unsupervised context learning on whole dialogues followed by supervised backchannel specialization.

What would settle it

Re-run the CAMA-BC pipeline with a strict conversation-disjoint split between MMA-CA pretraining and the downstream train/validation/test sets; if the Macro F1 gap over BPM-V collapses on KC-Dialog or BACKSpeech, the context stage's contribution is leakage rather than transferable conversational understanding.

Watch

Extended reading notes

Core claim

The central claim is that backchannel prediction cannot fully exploit video through naive feature concatenation; it requires Multi-Layer Multimodal Alignment (MMA), a hierarchical cross-attention scheme in which audio is the anchor query, text and video are enhanced using dense audio information, and cross-attention weights are shared across the top encoder layers. The same attention pattern is reused in the classification head, and a two-stage curriculum first learns conversational structure from unlabeled full dialogues (MMA-CA), then adapts to backchannel detection while still optimizing an alignment loss (MMA-BA). The paper reports consistent Macro F1 improvements over audio-text baselin

Load-bearing premise

The load-bearing premise is that the unsupervised Context Alignment (MMA-CA) pretraining uses only training conversations and excludes held-out validation and test dialogues; the paper defines the corpus as the complete conversational dataset but never states that the split is enforced, so the reported improvements could reflect the model having already seen the test conversations.

Editorial extensions

If this is right

  • Spoken-dialogue systems could begin using visual listener cues, not just words and prosody, to decide when to respond.
  • The fixed audio-first hierarchy suggests backchannel models can be built without equal-weight fusion of all modalities.
  • Larger relative gains on Empathy and Continuer imply that contextual alignment particularly helps the semantically sparse classes.
  • The SWBD results, obtained with generated talking-head videos, indicate that the fusion mechanism itself transfers across languages, even when real video is unavailable.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • The paper does not state whether validation/test dialogues are excluded from the MMA-CA pretraining corpus; if they are not, the reported context gain (about 1 Macro F1 point in Table 7) could come from transductive leakage rather than transferable conversational understanding.
  • The use of synthetic videos for SWBD means the English results test the alignment architecture more than the realism of visual input; real conversational video might behave differently.
  • Sharing cross-attention weights across layers is a strong inductive bias; ablating it could reveal whether the benefit comes from the hierarchy or from the parameter sharing.
  • The fixed audio > text > video hierarchy may fail on interactions where visual or textual cues dominate, suggesting a learnable or dynamic hierarchy as a natural next step.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 5 minor

Summary. The paper introduces CAMA-BC, a two-stage multimodal framework for backchannel prediction that adds video to the traditional audio+text inputs. The first stage, Context Alignment (MMA-CA), is an unsupervised contrastive alignment objective trained on dialogue segments; the second, Backchannel Alignment (MMA-BA), fine-tunes on annotated backchannel examples with a combined cross-entropy and alignment loss. The core claims are that the proposed Multi-Layer Multimodal Alignment (MMA) with a fixed audio>text>video information hierarchy unlocks visual information, and that the context pretraining stage provides a meaningful gain. Experiments on KC-Dialog, BACKSpeech, and SWBD (with synthetically generated video) report Macro F1 improvements over prior-style and LLM baselines, with the largest gains on the minority Empathy class.

Significance. If the reported results are clean, the contribution is meaningful: it is among the first efforts to systematically incorporate video into backchannel prediction, and the paper includes extensive ablations (loss weight, alignment depth, alignment method, input length, label noise) that go beyond typical practice. The two-stage curriculum idea is plausible and well-motivated. However, the central novelty claim (the MMA-CA gain) is threatened by a possible train/test leakage in the unsupervised pretraining stage, and the SWBD claim rests on synthetic videos that are deterministic re-encodings of the audio. These issues prevent the results from being interpreted as evidence of a transferable, video-grounded conversation-understanding mechanism without additional experiments.

major comments (3)
  1. [Sec. 3.2 and Table 5] Context Alignment is defined on the 'complete conversational corpus' (D_CA), and Table 5 reports only a single 'Total #Samples for MMA-CA' (130,278 for KC-Dialog) with no train/validation/test split. Combined with the explicit statement in Sec. 3.1 that no speaker-disjoint split is enforced, this implies the unsupervised MMA-CA pretraining may include the exact dialogues used for validation and test. The ablation in Table 7 attributes about 0.94 Macro F1 (58.53 vs 57.59) to MMA-CA. If test dialogues are included, this gain is transductive leakage, not evidence of a transferable context mechanism. Please state clearly whether D_CA is restricted to the training dialogues, and if it is not, rerun the full pipeline with train-only DCA and report Table 6/7 accordingly.
  2. [Sec. 3.1, Sec. 4.1 and Table 5] The data split is performed at the sample level ('The dataset was then split into training, validation, and test sets in an 8:0.5:1.5 ratio'), not at the dialogue or speaker level. The paper admits 'We do not enforce a speaker-disjoint split.' This means the same conversation can appear in both training and test sets. While all methods may benefit from this in the supervised stage, only CAMA-BC's unsupervised MMA-CA additionally sees the test conversations, so the relative gain over baselines is not a fair comparison. Please report an additional dialogue-disjoint (or speaker-disjoint) split evaluation, or at least quantify the overlap between train and test dialogues.
  3. [Sec. 4.3.2 and Appendix C.2] The SWBD experiments use video generated by EchoMimic from the same audio segments that are already used as the audio input, conditioned on a random face generated by Flux. The 'visual' stream is therefore a deterministic function of the audio plus random identity; it carries no independent non-verbal information. Table 8's conclusion that 'visual modality consistently improves performance' is not supported for SWBD. The authors should either obtain real video data for English backchannel prediction or explicitly reframe the SWBD result as a consistency/audio-to-video transfer check, not as evidence for visual cues.
minor comments (5)
  1. [Sec. 3.3, Eq. (13)] The set J is written as the Cartesian product of the last n_l layers of each encoder, which would give n_l^3 triples. The intended alignment likely uses corresponding layers (j_a=j_t=j_v), as suggested by Table A.3. Please clarify the notation.
  2. [Table 5] The table formatting is hard to parse: category counts (NoBC, BC, Continuer, Understanding, Empathy) and train/validation/test counts are interleaved without clear column alignment. Please reformat and ensure the 'NoBC and BC samples had equal numbers' statement is consistent with the printed values (e.g., KC-Dialog NoBC=34,710 but BC=19,635).
  3. [Table 7] This central ablation reports no standard deviations or significance tests, unlike Tables 6 and 8. Please add these; the MMA-CA effect of ~0.94 F1 may be within seed noise.
  4. [Appendix C.2] The image/video generation metrics (PDist, SSIM, PSNR for Flux; FID, FVD, E-FID for EchoMimic) are reported without the reference set or protocol. State what these numbers are computed against; otherwise they are uninterpretable.
  5. [Sec. 5, Limitations] The limitation paragraph discusses the fixed modality hierarchy and window length, but it does not mention the potential train/test overlap in DCA or the synthetic nature of the SWBD video. These are exactly the points that currently limit the interpretation of the experiments.
Assumptions & free parameters 4 free parameters · 5 assumptions · 0 invented entities

The method has no theoretical derivation; it rests on a stack of empirical choices. The central tuned quantities are lambda, the layer set, and the context window. No new physical or theoretical entities are proposed. The biggest unstated assumptions are the train/test integrity of Context Alignment and the validity of synthetic SWBD video.

free parameters (4)
  • alignment loss weight lambda = 0.1
    Tuned on KC-Dialog (Table A.2); lambda=0 drops Macro F1 by about 1.96 points.
  • alignment depth and layer set J = n_l=3, J={9,10,11}
    Selected empirically on KC-Dialog (Table A.3); other depths give lower Macro F1.
  • context window (n, m, l) = 1500 ms, 5 words, 12 frames
    Taken from prior work, but Table A.5 shows 3000 ms/10 words improves CAMA-BC by 1.23 Macro F1, so the default choice is fitted to the task, not derived.
  • training hyperparameters = LRs 5e-6/5e-5/5e-4, batch 16, decay 0.95/epoch
    Reported in Appendix D; these affect the exact numbers and are not justified by a principle.
assumptions (5)
  • domain assumption Pretrained encoders (KoBERT, HuBERT, VideoMAE) provide sufficiently good modality features.
    Section 3.3 uses them as the backbone; the central results depend on their quality and on the encoders being fine-tuned.
  • ad hoc to paper The modality information hierarchy audio > text > video is fixed and globally correct.
    Inferred from Table 4 on the same datasets and used to fix query/key roles in Equations (16)-(17). The paper's own Limitations section says the hierarchy may not generalize.
  • ad hoc to paper Context Alignment windows are drawn only from permitted training data and do not overlap the test set.
    Section 3.2 says the "complete conversational corpus" is used without stating that evaluation splits are excluded. If false, the results leak.
  • ad hoc to paper Synthetic SWBD videos (Flux reference images + EchoMimic animation) are a valid proxy for real video.
    Section C.2 generates faces from the same audio; no genuine facial expressions or gestures exist, so the SWBD visual claims may recapitulate audio rather than test real video.
  • domain assumption Weight-sharing across alignment layers is beneficial.
    Asserted in Section 3.3; the ablation varies the number of layers, but not the weight-sharing choice itself.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Open Your Model's Eyes: Video and Context-Aware Multimodal Backchannel Prediction." pith.science (2026). https://pith.science/paper/VYWDBG32

@misc{pith2026260722729,
  author       = {Pith},
  title        = {Pith review of: Open Your Model's Eyes: Video and Context-Aware Multimodal Backchannel Prediction},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/VYWDBG32}},
  note         = {Machine review of arXiv:2607.22729}
}
read the original abstract

Backchannels, which signal listener states like empathy and understanding, are fundamental to natural human interaction. However, current approaches rely solely on audio and text. This omits crucial visual cues, such as facial expressions and gestures, as well as broader conversational contexts, which are necessary for accurate prediction. In this paper, we introduce Context-Aware Multimodal Alignment for Backchannel Prediction (CAMA-BC), a novel framework that leverages visual information through Multi-Layer Multimodal Alignment (MMA). Our alignment process comprises two stages. First, Context Alignment (MMA-CA) utilizes unlabeled dialogues with videos to capture conversational contexts. Next, Backchannel Alignment (MMA-BA) fine-tunes the representations specifically for backchannel prediction. Experimental results show that CAMA-BC significantly outperforms both existing methods and simple multimodal baselines, with particular effectiveness in recognizing complex backchannels such as empathy.

Figures

Figures reproduced from arXiv: 2607.22729 by the authors.

Figure 1
Figure 1. Schematic of the proposed data utilization in [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Illustration of the data construction and architecture of Context-Aware Multimodal Alignment for [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

50 extracted references · 4 canonical work pages

  1. [1]

    proceedings of INTERSPEECH 2016 , pages=

    Tatsuya Kawahara and Takashi Yamaguchi and Koji Inoue and Katsuya Takanashi and Nigel Ward , title=. proceedings of INTERSPEECH 2016 , pages=

    2016

  2. [2]

    The Backchannel Norms of Native English Speakers: A Target for Japanese L2 English Learners , volume =

    Cutrone, Pino , year =. The Backchannel Norms of Native English Speakers: A Target for Japanese L2 English Learners , volume =

  3. [3]

    IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) , pages=

    Oh, Jeez! or uh-huh? A listener-aware Backchannel predictor on ASR transcriptions , author=. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) , pages=. 2020 , organization=

    2020

  4. [4]

    Acoustics, speech, and signal processing, ieee international conference on , volume=

    SWITCHBOARD: Telephone speech corpus for research and development , author=. Acoustics, speech, and signal processing, ieee international conference on , volume=. 1992 , organization=

    1992

  5. [5]

    Proceedings of the AAAI Conference on Artificial Intelligence , volume=

    Echomimic: Lifelike audio-driven portrait animations through editable landmark conditions , author=. Proceedings of the AAAI Conference on Artificial Intelligence , volume=

  6. [6]

    2025 , howpublished=

    Meta , title=. 2025 , howpublished=

    2025

  7. [7]

    Multimodal Cross- and Self-Attention Network for Speech Emotion Recognition , year=

    Sun, Licai and Liu, Bin and Tao, Jianhua and Lian, Zheng , booktitle=. Multimodal Cross- and Self-Attention Network for Speech Emotion Recognition , year=

  8. [8]

    Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , pages=

    A joint cross-attention model for audio-visual fusion in dimensional emotion recognition , author=. Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , pages=

Show all 50 references
  1. [9]

    BPM \_ MT : Enhanced Backchannel Prediction Model using Multi-Task Learning

    Jang, Jin Yea and Kim, San and Jung, Minyoung and Shin, Saim and Gweon, Gahgene. BPM \_ MT : Enhanced Backchannel Prediction Model using Multi-Task Learning. Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (EMNLP). 2021. doi:10.18653/v1/2...

    2021 doi

  2. [10]

    Papers from the sixth regional meeting Chicago Linguistic Society, Chicago Linguistic Society, Chicago , pages=

    On getting a word in edgewise , author=. Papers from the sixth regional meeting Chicago Linguistic Society, Chicago Linguistic Society, Chicago , pages=

  3. [11]

    proceedings of INTERSPEECH 2017 , pages=

    Robin Ruede and Markus Müller and Sebastian Stüker and Alex Waibel , title=. proceedings of INTERSPEECH 2017 , pages=

    2017

  4. [12]

    2021 , booktitle =

    AVLnet: Learning Audio-Visual Language Representations from Instructional Videos , author =. 2021 , booktitle =. doi:10.21437/Interspeech.2021-1312 , issn =

    2021 doi

  5. [13]

    Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , pages=

    Everything at once-multi-modal fusion transformer for video retrieval , author=. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , pages=

  6. [14]

    Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , year=

    ImageBind: One Embedding Space To Bind Them All , author=. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , year=

  7. [15]

    2024 , booktitle=

    LanguageBind: Extending Video-Language Pretraining to N-modality by Language-based Semantic Alignment , author=. 2024 , booktitle=

    2024

  8. [16]

    International Workshop on Spoken Dialogue Systems Technology (IWSDS) , year=

    Modeling Speaker-Listener Interaction for Backchannel Prediction , author=. International Workshop on Spoken Dialogue Systems Technology (IWSDS) , year=

  9. [17]

    arXiv preprint arXiv:1705.06950 , year=

    The kinetics human action video dataset , author=. arXiv preprint arXiv:1705.06950 , year=

    arXiv

  10. [18]

    GitHub repository , howpublished =

    SKTBrain , title =. GitHub repository , howpublished =. 2021 , publisher =

    2021

  11. [19]

    2023 , howpublished=

    Backchannel Annotation Corpus in Korean Speech , author=. 2023 , howpublished=

    2023

  12. [20]

    IEEE/ACM Transactions on Audio, Speech, and Language Processing , volume=

    Hubert: Self-supervised speech representation learning by masked prediction of hidden units , author=. IEEE/ACM Transactions on Audio, Speech, and Language Processing , volume=. 2021 , publisher=

    2021

  13. [21]

    North American Chapter of the Association for Computational Linguistics (NAACL) , year=

    BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding , author=. North American Chapter of the Association for Computational Linguistics (NAACL) , year=

  14. [22]

    Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , month =

    Duan, Jiali and Chen, Liqun and Tran, Son and Yang, Jinyu and Xu, Yi and Zeng, Belinda and Chilimbi, Trishul , title =. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , month =. 2022 , pages =

    2022

  15. [23]

    Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , pages=

    MEGA: Multimodal alignment aggregation and distillation for cinematic video segmentation , author=. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , pages=

  16. [24]

    Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , pages=

    Align and attend: Multimodal summarization with dual contrastive losses , author=. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , pages=

  17. [25]

    Proceedings of the AAAI Conference on Artificial Intelligence , volume=

    Token-level contrastive learning with modality-aware prompting for multimodal intent recognition , author=. Proceedings of the AAAI Conference on Artificial Intelligence , volume=

  18. [26]

    MAF : Multimodal Alignment Framework for Weakly-Supervised Phrase Grounding

    Wang, Qinxin and Tan, Hao and Shen, Sheng and Mahoney, Michael and Yao, Zhewei. MAF : Multimodal Alignment Framework for Weakly-Supervised Phrase Grounding. Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). 2020. doi:10.18653/v1/20...

    2020 doi

  19. [27]

    Att-Sinkhorn: Multimodal Alignment with Sinkhorn-based Deep Attention Architecture , year=

    Ma, Qianxia and Zhang, Ming and Tang, Yan and Huang, Zhen , booktitle=. Att-Sinkhorn: Multimodal Alignment with Sinkhorn-based Deep Attention Architecture , year=

  20. [28]

    Proceedings of the 30th ACM International Conference on Multimedia , pages =

    Huang, Yi and Yang, Xiaoshan and Zhang, Ji and Xu, Changsheng , title =. Proceedings of the 30th ACM International Conference on Multimedia , pages =. 2022 , isbn =. doi:10.1145/3503161.3548009 , abstract =

    2022

  21. [29]

    Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , month =

    Wang, Yikai and Chen, Xinghao and Cao, Lele and Huang, Wenbing and Sun, Fuchun and Wang, Yunhe , title =. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , month =. 2022 , pages =

    2022

  22. [30]

    Entity-Oriented Multi-Modal Alignment and Fusion Network for Fake News Detection , year=

    Li, Peiguang and Sun, Xian and Yu, Hongfeng and Tian, Yu and Yao, Fanglong and Xu, Guangluan , journal=. Entity-Oriented Multi-Modal Alignment and Fusion Network for Fake News Detection , year=

  23. [31]

    arXiv preprint arXiv:1508.01991 , year=

    Bidirectional LSTM-CRF models for sequence tagging , author=. arXiv preprint arXiv:1508.01991 , year=

    arXiv

  24. [32]

    proceedings of INTERSPEECH 2017 , pages=

    Robin Ruede and Markus Müller and Sebastian Stüker and Alex Waibel , title=. proceedings of INTERSPEECH 2017 , pages=. doi:10.21437/Interspeech.2017-1606 , issn=

    2017 doi

  25. [33]

    Advances in neural information processing systems , volume=

    Attention is all you need , author=. Advances in neural information processing systems , volume=

  26. [34]

    Advances in experimental social psychology , volume=

    Nonverbal behavior and nonverbal communication: What do conversational hand gestures tell us? , author=. Advances in experimental social psychology , volume=. 1996 , publisher=

    1996

  27. [35]

    The social and psychological contexts of language , pages=

    The functions of nonverbal signs in conversation , author=. The social and psychological contexts of language , pages=. 2013 , publisher=

    2013

  28. [36]

    Journal of human behavior in the social environment , volume=

    Nonverbal communication in humans , author=. Journal of human behavior in the social environment , volume=. 2014 , publisher=

    2014

  29. [37]

    2016 , URL =

    Listen, Attend and Spell: A Neural Network for Large Vocabulary Conversational Speech Recognition , author =. 2016 , URL =

    2016

  30. [38]

    arXiv preprint arXiv:2501.02832 , year=

    Samba-asr state-of-the-art speech recognition leveraging structured state-space models , author=. arXiv preprint arXiv:2501.02832 , year=

    arXiv

  31. [39]

    ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) , pages=

    Self-training and pre-training are complementary for speech recognition , author=. ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) , pages=. 2021 , organization=

    2021

  32. [40]

    International Conference on Learning Representations , year=

    Finetuned Language Models are Zero-Shot Learners , author=. International Conference on Learning Representations , year=

  33. [41]

    Proceedings of the First Conference on Machine Translation: Volume 1, Research Papers , pages=

    Linguistic Input Features Improve Neural Machine Translation , author=. Proceedings of the First Conference on Machine Translation: Volume 1, Research Papers , pages=

  34. [42]

    Journal of machine learning research , volume=

    Exploring the limits of transfer learning with a unified text-to-text transformer , author=. Journal of machine learning research , volume=

  35. [43]

    arXiv preprint arXiv:1909.11942 , year=

    Albert: A lite bert for self-supervised learning of language representations , author=. arXiv preprint arXiv:1909.11942 , year=

    1909 arXiv

  36. [44]

    Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics , pages=

    SMART: Robust and Efficient Fine-Tuning for Pre-trained Natural Language Models through Principled Regularized Optimization , author=. Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics , pages=

  37. [45]

    Journal of Machine Learning Research , volume=

    Palm: Scaling language modeling with pathways , author=. Journal of Machine Learning Research , volume=

  38. [46]

    arXiv preprint arXiv:2212.01853 , year=

    Toward efficient language model pretraining and downstream adaptation via self-evolution: A case study on superglue , author=. arXiv preprint arXiv:2212.01853 , year=

    arXiv

  39. [47]

    1993 , issn =

    Learning and development in neural networks: the importance of starting small , journal =. 1993 , issn =. doi:https://doi.org/10.1016/0010-0277(93)90058-4 , url =

    1993 doi

  40. [48]

    arXiv preprint arXiv:2407.21783 , year=

    The llama 3 herd of models , author=. arXiv preprint arXiv:2407.21783 , year=

    arXiv

  41. [49]

    arXiv preprint arXiv:2303.08774 , year=

    Gpt-4 technical report , author=. arXiv preprint arXiv:2303.08774 , year=

    arXiv

  42. [50]

    Zhan Tong and Yibing Song and Jue Wang and Limin Wang , booktitle=. Video

Pith tools

Reviewed August 1, 2026 · model on record in the stance chip above.