What do MLLMs hear? Examining reasoning with text and sound components in Multimodal Large Language Models

• URL: http://arxiv.org/abs/2406.04615v1
• Date: Fri, 7 Jun 2024 03:55:00 GMT
• Title: What do MLLMs hear? Examining reasoning with text and sound components in Multimodal Large Language Models
• Authors: Enis Berk Çoban, Michael I. Mandel, Johanna Devaney,
• Abstract summary: We demonstrate through a captioning/classification experiment that an audio MLLM cannot fully leverage its LLM's text-based reasoning when generating audio captions. We also consider how this may be due to MLLMs separately representing auditory and textual information such as it severs the reasoning pathway from the LLM to the audio encoder.
• Score: 6.313516199029267
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large Language Models (LLMs) have demonstrated remarkable reasoning capabilities, notably in connecting ideas and adhering to logical rules to solve problems. These models have evolved to accommodate various data modalities, including sound and images, known as multimodal LLMs (MLLMs), which are capable of describing images or sound recordings. Previous work has demonstrated that when the LLM component in MLLMs is frozen, the audio or visual encoder serves to caption the sound or image input facilitating text-based reasoning with the LLM component. We are interested in using the LLM's reasoning capabilities in order to facilitate classification. In this paper, we demonstrate through a captioning/classification experiment that an audio MLLM cannot fully leverage its LLM's text-based reasoning when generating audio captions. We also consider how this may be due to MLLMs separately representing auditory and textual information such that it severs the reasoning pathway from the LLM to the audio encoder.

Related papers

• Large Language Models Are Strong Audio-Visual Speech Recognition Learners [53.142635674428874]
  Multimodal large language models (MLLMs) have recently become a focal point of research due to their formidable multimodal understanding capabilities. We propose Llama-AVSR, a new MLLM with strong audio-visual speech recognition capabilities. We evaluate our proposed approach on LRS3, the largest public AVSR benchmark, and we achieve new state-of-the-art results for the tasks of ASR and AVSR with a WER of 0.81% and 0.77%, respectively.
  arXiv  Detail & Related papers  (2024-09-18T21:17:27Z)
• Paying More Attention to Image: A Training-Free Method for Alleviating Hallucination in LVLMs [14.381188702947949]
  Large Vision-Language Models (LVLMs) primarily align image features of vision encoder with Large Language Models (LLMs) to leverage their superior text generation capabilities. This imbalance in LVLMs may result in the instances of hallucinatory. We introduce a training-free algorithm to find an equilibrium point between image comprehension and language inference.
  arXiv  Detail & Related papers  (2024-07-31T17:46:57Z)
• Prompting Large Language Models with Audio for General-Purpose Speech Summarization [13.415189715216354]
  We introduce a framework for speech summarization that leverages the processing and reasoning capabilities of large language models (LLMs) We propose an end-to-end system that combines an instruction-tuned LLM with an audio encoder that converts speech into token representations that the LLM can interpret.
  arXiv  Detail & Related papers  (2024-06-10T02:04:28Z)
• Boosting Large Language Model for Speech Synthesis: An Empirical Study [86.89548753080432]
  Large language models (LLMs) have made significant advancements in natural language processing and are concurrently extending the language ability to other modalities, such as speech and vision. We conduct a comprehensive empirical exploration of boosting LLMs with the ability to generate speech, by combining pre-trained LLM LLaMA/OPT and text-to-speech synthesis model VALL-E. We compare three integration methods between LLMs and speech models, including directly fine-tuned LLMs, superposed layers of LLMs and VALL-E, and coupled LLMs and VALL-E using LLMs as a powerful text encoder
  arXiv  Detail & Related papers  (2023-12-30T14:20:04Z)
• Video Understanding with Large Language Models: A Survey [97.29126722004949]
  Given the remarkable capabilities of large language models (LLMs) in language and multimodal tasks, this survey provides a detailed overview of recent advancements in video understanding. The emergent capabilities Vid-LLMs are surprisingly advanced, particularly their ability for open-ended multi-granularity reasoning. This survey presents a comprehensive study of the tasks, datasets, benchmarks, and evaluation methodologies for Vid-LLMs.
  arXiv  Detail & Related papers  (2023-12-29T01:56:17Z)
• Speech Translation with Large Language Models: An Industrial Practice [64.5419534101104]
  We introduce LLM-ST, a novel and effective speech translation model constructed upon a pre-trained large language model (LLM) By integrating the large language model (LLM) with a speech encoder and employing multi-task instruction tuning, LLM-ST can produce accurate timestamped transcriptions and translations. Through rigorous experimentation on English and Chinese datasets, we showcase the exceptional performance of LLM-ST.
  arXiv  Detail & Related papers  (2023-12-21T05:32:49Z)
• CLAMP: Contrastive LAnguage Model Prompt-tuning [89.96914454453791]
  We show that large language models can achieve good image classification performance when adapted this way. Our approach beats state-of-the-art mLLMs by 13% and slightly outperforms contrastive learning with a custom text model.
  arXiv  Detail & Related papers  (2023-12-04T05:13:59Z)
• Harnessing the Zero-Shot Power of Instruction-Tuned Large Language Model in End-to-End Speech Recognition [23.172469312225694]
  We propose to utilize an instruction-tuned large language model (LLM) for guiding the text generation process in automatic speech recognition (ASR) The proposed model is built on the joint CTC and attention architecture, with the LLM serving as a front-end feature extractor for the decoder. Experimental results show that the proposed LLM-guided model achieves a relative gain of approximately 13% in word error rates across major benchmarks.
  arXiv  Detail & Related papers  (2023-09-19T11:10:50Z)
• Prompting Large Language Models with Speech Recognition Abilities [31.77576008965215]
  We extend the capabilities of large language models by directly attaching a small audio encoder allowing it to perform speech recognition. Experiments on MultilingualSpeech show that incorporating a conformer encoder into the open sourced LLaMA-7B allows it to outperform monolingual baselines by 18%.
  arXiv  Detail & Related papers  (2023-07-21T08:39:15Z)
• SPAE: Semantic Pyramid AutoEncoder for Multimodal Generation with Frozen LLMs [124.29233620842462]
  We introduce SPAE for enabling frozen LLMs to perform both understanding and generation tasks involving non-linguistic modalities such as images or videos. The resulting lexical tokens capture both the semantic meaning and the fine-grained details needed for visual reconstruction. Our method marks the first successful attempt to enable a frozen LLM to generate image content while surpassing state-of-the-art performance in image understanding tasks, under the same setting, by over 25%.
  arXiv  Detail & Related papers  (2023-06-30T17:59:07Z)

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.