VidLanKD: Improving Language Understanding via Video-Distilled Knowledge Transfer (2107.02681v2)

Abstract: Since visual perception can give rich information beyond text descriptions for world understanding, there has been increasing interest in leveraging visual grounding for language learning. Recently, vokenization (Tan and Bansal, 2020) has attracted attention by using the predictions of a text-to-image retrieval model as labels for LLM supervision. Despite its success, the method suffers from approximation error of using finite image labels and the lack of vocabulary diversity of a small image-text dataset. To overcome these limitations, we present VidLanKD, a video-language knowledge distillation method for improving language understanding. We train a multi-modal teacher model on a video-text dataset, and then transfer its knowledge to a student LLM with a text dataset. To avoid approximation error, we propose to use different knowledge distillation objectives. In addition, the use of a large-scale video-text dataset helps learn diverse and richer vocabularies. In our experiments, VidLanKD achieves consistent improvements over text-only LLMs and vokenization models, on several downstream language understanding tasks including GLUE, SQuAD, and SWAG. We also demonstrate the improved world knowledge, physical reasoning, and temporal reasoning capabilities of our model by evaluating on the GLUE-diagnostics, PIQA, and TRACIE datasets. Lastly, we present comprehensive ablation studies as well as visualizations of the learned text-to-video grounding results of our teacher and student LLMs. Our code and models are available at: https://github.com/zinengtang/VidLanKD

VidLanKD: Enhancing Language Understanding Through Video-Distilled Knowledge Transfer

The paper "VidLanKD: Improving Language Understanding via Video-Distilled Knowledge Transfer" addresses the challenges associated with grounding language learning in visual information by introducing VidLanKD, a video-language knowledge distillation framework aimed at improving natural language understanding (NLU). This method leverages the extensive and diverse knowledge embedded in video datasets to compensate for the limitations of text corpora, which often lack the richness needed for effective multi-modal learning.

Proposed Methodology

The VidLanKD framework operates through a two-stage process. Initially, a multi-modal teacher model comprising a video encoder and a language encoder is trained on a large-scale video-text dataset using both video-language contrastive learning and masked LLMing objectives. Subsequently, this consolidated knowledge is transferred to a student LLM using only text data. To mitigate approximation errors inherent in earlier models like vokenization, the authors employ novel knowledge distillation (KD) objectives, specifically neuron selectivity transfer (NST) and contrastive representation distillation (CRD).

Experimental Validation

Empirical results from experiments demonstrated that the VidLanKD framework consistently surpasses text-only LLMs and those enhanced with vokenization on standard NLU benchmarks such as GLUE, SQuAD, and SWAG. The paper highlights specific improvements in models’ world knowledge, physical reasoning, and temporal reasoning capabilities, as verified on GLUE-diagnostics, PIQA, and TRACIE datasets respectively. The paper details a comprehensive series of ablation studies that juxtapose various architectural and training strategies, supporting the robustness of the proposed approach.

Numerical Results and Claims

Several numerical results showcased the advantages of VidLanKD over traditional methods. The student models distilled with VidLanKD’s methods consistently showed improvement across various benchmark datasets. The findings indicate a notable enhancement in knowledge representation and reasoning capabilities, which could not be achieved through text corpora alone.

Implications and Future Directions

The implications of this research are significant for the field of artificial intelligence, particularly in the domain of NLU. By integrating visual data into the language learning process, models can achieve a deeper, more contextual understanding of text that's grounded in real-world experience. This paper opens pathways for future research into multimodal learning and knowledge distillation, suggesting potential exploration of larger and more diverse video datasets, as well as the incorporation of more sophisticated visual encoders to further refine the KD process.

Conclusion

VidLanKD represents a valuable step forward in the integration of visual grounding in LLMs, harnessing the depth of video data to enrich language understanding frameworks. By overcoming the limitations of vokenization and text-only learning, VidLanKD paves the way for more intuitive and contextually aware AI systems. As such methodologies evolve, they promise to revolutionize the efficacy of NLU systems, pushing the boundaries of what AI can achieve in understanding human language.