Transforming LLMs into Cross-modal and Cross-lingual Retrieval Systems

• URL: http://arxiv.org/abs/2404.01616v3
• Date: Wed, 10 Jul 2024 15:20:19 GMT
• Title: Transforming LLMs into Cross-modal and Cross-lingual Retrieval Systems
• Authors: Frank Palma Gomez, Ramon Sanabria, Yun-hsuan Sung, Daniel Cer, Siddharth Dalmia, Gustavo Hernandez Abrego,
• Abstract summary: Large language models (LLMs) are trained on text-only data that go far beyond the languages with paired speech and text data. We propose using LLMs to initialize multi-modal DE retrieval systems. Our system is capable of matching speech and text in 102 languages despite only training on 21 languages.
• Score: 16.32944967819047
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large language models (LLMs) are trained on text-only data that go far beyond the languages with paired speech and text data. At the same time, Dual Encoder (DE) based retrieval systems project queries and documents into the same embedding space and have demonstrated their success in retrieval and bi-text mining. To match speech and text in many languages, we propose using LLMs to initialize multi-modal DE retrieval systems. Unlike traditional methods, our system doesn't require speech data during LLM pre-training and can exploit LLM's multilingual text understanding capabilities to match speech and text in languages unseen during retrieval training. Our multi-modal LLM-based retrieval system is capable of matching speech and text in 102 languages despite only training on 21 languages. Our system outperforms previous systems trained explicitly on all 102 languages. We achieve a 10% absolute improvement in Recall@1 averaged across these languages. Additionally, our model demonstrates cross-lingual speech and text matching, which is further enhanced by readily available machine translation data.

Related papers

• Zero-resource Speech Translation and Recognition with LLMs [38.11535502039386]
  We propose to leverage a multilingual Large Language Model (LLM) to perform ST and ASR in languages for which the model has never seen paired audio-text data. We achieve this by using a pre-trained multilingual speech encoder, a multilingual LLM, and a lightweight adaptation module that maps the audio representations to the token embedding space of the LLM.
  arXiv  Detail & Related papers  (2024-12-24T17:37:11Z)
• CLASP: Contrastive Language-Speech Pretraining for Multilingual Multimodal Information Retrieval [0.9023847175654603]
  CLASP (Contrastive Language-Speech Pretraining) is a multilingual representation tailored for audio-text information retrieval. During training, we utilize our newly introduced speech-text dataset, which encompasses 15 diverse categories ranging from fiction to religion. Our evaluations across multiple languages demonstrate that CLASP establishes new benchmarks in HITS@1, MRR, and meanR metrics.
  arXiv  Detail & Related papers  (2024-12-17T16:38:10Z)
• Speech Recognition Rescoring with Large Speech-Text Foundation Models [20.145389016219106]
  Large language models (LLM) have demonstrated the ability to understand human language by leveraging large amount of text data. Automatic speech recognition (ASR) systems are often limited by available transcribed speech data. Recent multi-modal large language models have demonstrated strong spoken language understanding.
  arXiv  Detail & Related papers  (2024-09-25T06:17:23Z)
• LLMs Beyond English: Scaling the Multilingual Capability of LLMs with Cross-Lingual Feedback [61.23008372927665]
  We introduce xLLMs-100, which scales the multilingual capabilities of LLaMA and BLOOM to 100 languages. We evaluate the multilingual understanding and generating capabilities of xLLMs-100 on five multilingual benchmarks.
  arXiv  Detail & Related papers  (2024-06-03T20:25:12Z)
• Teaching a Multilingual Large Language Model to Understand Multilingual Speech via Multi-Instructional Training [29.47243668154796]
  BLOOMZMMS is a novel model that integrates a multilingual LLM with a multilingual speech encoder. We demonstrate the transferability of linguistic knowledge from the text to the speech modality. Our zero-shot evaluation results confirm the robustness of our approach across multiple tasks.
  arXiv  Detail & Related papers  (2024-04-16T21:45:59Z)
• PolyLM: An Open Source Polyglot Large Language Model [57.64420154135178]
  We present PolyLM, a multilingual large language model (LLMs) trained on 640 billion (B) tokens, avaliable in two model sizes: 1.7B and 13B. To enhance its multilingual capabilities, we 1) integrate bilingual data into training data; and 2) adopt a curriculum learning strategy that increases the proportion of non-English data from 30% in the first stage to 60% in the final stage during pre-training. Further, we propose a multilingual self-instruct method which automatically generates 132.7K diverse multilingual instructions for model fine-tuning.
  arXiv  Detail & Related papers  (2023-07-12T09:00:37Z)
• AudioPaLM: A Large Language Model That Can Speak and Listen [79.44757696533709]
  We introduce AudioPaLM, a large language model for speech understanding and generation. AudioPaLM fuses text-based and speech-based language models. It can process and generate text and speech with applications including speech recognition and speech-to-speech translation.
  arXiv  Detail & Related papers  (2023-06-22T14:37:54Z)
• Soft Language Clustering for Multilingual Model Pre-training [57.18058739931463]
  We propose XLM-P, which contextually retrieves prompts as flexible guidance for encoding instances conditionally. Our XLM-P enables (1) lightweight modeling of language-invariant and language-specific knowledge across languages, and (2) easy integration with other multilingual pre-training methods.
  arXiv  Detail & Related papers  (2023-06-13T08:08:08Z)
• Efficiently Aligned Cross-Lingual Transfer Learning for Conversational Tasks using Prompt-Tuning [98.60739735409243]
  Cross-lingual transfer of language models trained on high-resource languages like English has been widely studied for many NLP tasks. We introduce XSGD for cross-lingual alignment pretraining, a parallel and large-scale multilingual conversation dataset. To facilitate aligned cross-lingual representations, we develop an efficient prompt-tuning-based method for learning alignment prompts.
  arXiv  Detail & Related papers  (2023-04-03T18:46:01Z)
• Generalizing Multimodal Pre-training into Multilingual via Language Acquisition [54.69707237195554]
  English-based Vision-Language Pre-training has achieved great success in various downstream tasks. Some efforts have been taken to generalize this success to non-English languages through Multilingual Vision-Language Pre-training. We propose a textbfMultitextbfLingual textbfAcquisition (MLA) framework that can easily generalize a monolingual Vision-Language Pre-training model into multilingual.
  arXiv  Detail & Related papers  (2022-05-29T08:53:22Z)

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.