LiPost: Improved Content Understanding With Effective Use of Multi-task Contrastive Learning

• URL: http://arxiv.org/abs/2405.11344v3
• Date: Sat, 13 Jul 2024 20:00:31 GMT
• Title: LiPost: Improved Content Understanding With Effective Use of Multi-task Contrastive Learning
• Authors: Akanksha Bindal, Sudarshan Ramanujam, Dave Golland, TJ Hazen, Tina Jiang, Fengyu Zhang, Peng Yan,
• Abstract summary: We fine-tune a pre-trained, transformer-based LLM using multi-task contrastive learning with data from a diverse set of semantic labeling tasks. Our model outperforms the baseline on zero shot learning and offers improved multilingual support. This work provides a robust foundation for vertical teams across LinkedIn to customize and fine-tune the LLM to their specific applications.
• Score: 2.611731148829789
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In enhancing LinkedIn core content recommendation models, a significant challenge lies in improving their semantic understanding capabilities. This paper addresses the problem by leveraging multi-task learning, a method that has shown promise in various domains. We fine-tune a pre-trained, transformer-based LLM using multi-task contrastive learning with data from a diverse set of semantic labeling tasks. We observe positive transfer, leading to superior performance across all tasks when compared to training independently on each. Our model outperforms the baseline on zero shot learning and offers improved multilingual support, highlighting its potential for broader application. The specialized content embeddings produced by our model outperform generalized embeddings offered by OpenAI on Linkedin dataset and tasks. This work provides a robust foundation for vertical teams across LinkedIn to customize and fine-tune the LLM to their specific applications. Our work offers insights and best practices for the field to build on.

Related papers

• NVLM: Open Frontier-Class Multimodal LLMs [64.00053046838225]
  We introduce NVLM 1.0, a family of frontier-class multimodal large language models (LLMs) that achieve state-of-the-art results on vision-language tasks. We propose a novel architecture that enhances both training efficiency and multimodal reasoning capabilities. We develop production-grade multimodality for the NVLM-1.0 models, enabling them to excel in vision-language tasks.
  arXiv  Detail & Related papers  (2024-09-17T17:59:06Z)
• Fine-tuning Multimodal Large Language Models for Product Bundling [53.01642741096356]
  We introduce Bundle-MLLM, a novel framework that fine-tunes large language models (LLMs) through a hybrid item tokenization approach. Specifically, we integrate textual, media, and relational data into a unified tokenization, introducing a soft separation token to distinguish between textual and non-textual tokens. We propose a progressive optimization strategy that fine-tunes LLMs for disentangled objectives: 1) learning bundle patterns and 2) enhancing multimodal semantic understanding specific to product bundling.
  arXiv  Detail & Related papers  (2024-07-16T13:30:14Z)
• SELF-GUIDE: Better Task-Specific Instruction Following via Self-Synthetic Finetuning [70.21358720599821]
  Large language models (LLMs) hold the promise of solving diverse tasks when provided with appropriate natural language prompts. We propose SELF-GUIDE, a multi-stage mechanism in which we synthesize task-specific input-output pairs from the student LLM. We report an absolute improvement of approximately 15% for classification tasks and 18% for generation tasks in the benchmark's metrics.
  arXiv  Detail & Related papers  (2024-07-16T04:41:58Z)
• Optimization of Prompt Learning via Multi-Knowledge Representation for Vision-Language Models [26.964848679914354]
  CoKnow is a framework that enhances Prompt Learning for Vision-Language Models with rich contextual knowledge. We conducted extensive experiments on 11 publicly available datasets, demonstrating that CoKnow outperforms a series of previous methods.
  arXiv  Detail & Related papers  (2024-04-16T07:44:52Z)
• Multi-modal Semantic Understanding with Contrastive Cross-modal Feature Alignment [11.897888221717245]
  This paper proposes a novel CLIP-guided contrastive-learning-based architecture to perform multi-modal feature alignment. Our model is simple to implement without using task-specific external knowledge, and thus can easily migrate to other multi-modal tasks.
  arXiv  Detail & Related papers  (2024-03-11T01:07:36Z)
• Distribution Matching for Multi-Task Learning of Classification Tasks: a Large-Scale Study on Faces & Beyond [62.406687088097605]
  Multi-Task Learning (MTL) is a framework, where multiple related tasks are learned jointly and benefit from a shared representation space. We show that MTL can be successful with classification tasks with little, or non-overlapping annotations. We propose a novel approach, where knowledge exchange is enabled between the tasks via distribution matching.
  arXiv  Detail & Related papers  (2024-01-02T14:18:11Z)
• SPHINX: The Joint Mixing of Weights, Tasks, and Visual Embeddings for Multi-modal Large Language Models [86.478087039015]
  We present a versatile multi-modal large language model (MLLM) with a joint mixing of model weights, tuning tasks, and visual embeddings. Based on our proposed joint mixing, we propose an efficient strategy aiming to better capture fine-grained appearances of high-resolution images. We hope our work may cast a light on the exploration of joint mixing in future MLLM research.
  arXiv  Detail & Related papers  (2023-11-13T18:59:47Z)
• YOLOR-Based Multi-Task Learning [12.5920336941241]
  Multi-task learning (MTL) aims to learn multiple tasks using a single model and jointly improve all of them assuming generalization and shared semantics. We propose building on You Only Learn One Representation (YOLOR), a network architecture specifically designed for multitasking. We find that our method achieves competitive performance on all tasks while maintaining a low parameter count and without any pre-training.
  arXiv  Detail & Related papers  (2023-09-29T01:42:21Z)
• Pre-training Multi-task Contrastive Learning Models for Scientific Literature Understanding [52.723297744257536]
  Pre-trained language models (LMs) have shown effectiveness in scientific literature understanding tasks. We propose a multi-task contrastive learning framework, SciMult, to facilitate common knowledge sharing across different literature understanding tasks.
  arXiv  Detail & Related papers  (2023-05-23T16:47:22Z)
• Understanding and Improving Information Transfer in Multi-Task Learning [14.43111978531182]
  We study an architecture with a shared module for all tasks and a separate output module for each task. We show that misalignment between task data can cause negative transfer (or hurt performance) and provide sufficient conditions for positive transfer. Inspired by the theoretical insights, we show that aligning tasks' embedding layers leads to performance gains for multi-task training and transfer learning.
  arXiv  Detail & Related papers  (2020-05-02T23:43:52Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.