Why Do Speech Language Models Fail to Generate Semantically Coherent Outputs? A Modality Evolving Perspective

• URL: http://arxiv.org/abs/2412.17048v1
• Date: Sun, 22 Dec 2024 14:59:19 GMT
• Title: Why Do Speech Language Models Fail to Generate Semantically Coherent Outputs? A Modality Evolving Perspective
• Authors: Hankun Wang, Haoran Wang, Yiwei Guo, Zhihan Li, Chenpeng Du, Xie Chen, Kai Yu,
• Abstract summary: We explore the influence of three key factors separately by transiting the modality from text to speech in an evolving manner. Factor A has a relatively minor impact, factor B influences syntactical and semantic modeling more obviously, and factor C exerts the most significant impact, particularly in the basic lexical modeling.
• Score: 23.49276487518479
• License:
• Abstract: Although text-based large language models exhibit human-level writing ability and remarkable intelligence, speech language models (SLMs) still struggle to generate semantically coherent outputs. There are several potential reasons for this performance degradation: (A) speech tokens mainly provide phonetic information rather than semantic information, (B) the length of speech sequences is much longer than that of text sequences, and (C) paralinguistic information, such as prosody, introduces additional complexity and variability. In this paper, we explore the influence of three key factors separately by transiting the modality from text to speech in an evolving manner. Our findings reveal that the impact of the three factors varies. Factor A has a relatively minor impact, factor B influences syntactical and semantic modeling more obviously, and factor C exerts the most significant impact, particularly in the basic lexical modeling. Based on these findings, we provide insights into the unique challenges of training SLMs and highlight pathways to develop more effective end-to-end SLMs.

Related papers

• Balanced Multi-Factor In-Context Learning for Multilingual Large Language Models [53.38288894305388]
  Multilingual large language models (MLLMs) are able to leverage in-context learning (ICL) to achieve high performance by leveraging cross-lingual knowledge transfer without parameter updates. Three key factors influence multilingual ICL: (1) semantic similarity, (2) linguistic alignment, and (3) language-specific performance. We propose balanced multi-factor ICL (textbfBMF-ICL), a method that quantifies and optimally balances these factors for improved example selection.
  arXiv  Detail & Related papers  (2025-02-17T06:56:33Z)
• The Impact of Token Granularity on the Predictive Power of Language Model Surprisal [15.073507986272027]
  One factor that has been overlooked in cognitive modeling is the granularity of subword tokens. Experiments with naturalistic reading times reveal a substantial influence of token granularity on surprisal. On garden-path constructions, language models trained on coarser-grained tokens generally assigned higher surprisal to critical regions.
  arXiv  Detail & Related papers  (2024-12-16T16:24:58Z)
• Chain-of-Thought in Large Language Models: Decoding, Projection, and Activation [8.80044898397965]
  Chain-of-Thought prompting has significantly enhanced the reasoning capabilities of large language models. This work examines three key aspects: decoding, projection, and activation, aiming to elucidate the changes that occur within models when employing Chainof-Thought.
  arXiv  Detail & Related papers  (2024-12-05T07:47:29Z)
• DeSTA2: Developing Instruction-Following Speech Language Model Without Speech Instruction-Tuning Data [84.01401439030265]
  Recent end-to-end speech language models (SLMs) have expanded upon the capabilities of large language models (LLMs) We present a simple yet effective automatic process for creating speech-text pair data. Our model demonstrates general capabilities for speech-related tasks without the need for speech instruction-tuning data.
  arXiv  Detail & Related papers  (2024-09-30T07:01:21Z)
• Debiased Multimodal Understanding for Human Language Sequences [14.434841446670726]
  We propose SuCI, a causal intervention module to disentangle the impact of subjects acting as unobserved confounders. As a plug-and-play component, SuCI can be widely applied to most methods that seek unbiased predictions.
  arXiv  Detail & Related papers  (2024-03-08T04:03:54Z)
• A Comparative Analysis of Conversational Large Language Models in Knowledge-Based Text Generation [5.661396828160973]
  We conduct an empirical analysis of conversational large language models in generating natural language text from semantic triples. We compare four large language models of varying sizes with different prompting techniques. Our findings show that the capabilities of large language models in triple verbalization can be significantly improved through few-shot prompting, post-processing, and efficient fine-tuning techniques.
  arXiv  Detail & Related papers  (2024-02-02T15:26:39Z)
• Enhancing Argument Structure Extraction with Efficient Leverage of Contextual Information [79.06082391992545]
  We propose an Efficient Context-aware model (ECASE) that fully exploits contextual information. We introduce a sequence-attention module and distance-weighted similarity loss to aggregate contextual information and argumentative information. Our experiments on five datasets from various domains demonstrate that our model achieves state-of-the-art performance.
  arXiv  Detail & Related papers  (2023-10-08T08:47:10Z)
• How to Handle Different Types of Out-of-Distribution Scenarios in Computational Argumentation? A Comprehensive and Fine-Grained Field Study [59.13867562744973]
  This work systematically assesses LMs' capabilities for out-of-distribution (OOD) scenarios. We find that the efficacy of such learning paradigms varies with the type of OOD. Specifically, while ICL excels for domain shifts, prompt-based fine-tuning surpasses for topic shifts.
  arXiv  Detail & Related papers  (2023-09-15T11:15:47Z)
• Inducing Causal Structure for Abstractive Text Summarization [76.1000380429553]
  We introduce a Structural Causal Model (SCM) to induce the underlying causal structure of the summarization data. We propose a Causality Inspired Sequence-to-Sequence model (CI-Seq2Seq) to learn the causal representations that can mimic the causal factors. Experimental results on two widely used text summarization datasets demonstrate the advantages of our approach.
  arXiv  Detail & Related papers  (2023-08-24T16:06:36Z)
• Towards Language Modelling in the Speech Domain Using Sub-word Linguistic Units [56.52704348773307]
  We propose a novel LSTM-based generative speech LM based on linguistic units including syllables and phonemes. With a limited dataset, orders of magnitude smaller than that required by contemporary generative models, our model closely approximates babbling speech. We show the effect of training with auxiliary text LMs, multitask learning objectives, and auxiliary articulatory features.
  arXiv  Detail & Related papers  (2021-10-31T22:48:30Z)
• Deep generative factorization for speech signal [25.047575079871272]
  This paper presents a speech factorization approach based on a novel factorial discriminative normalization flow model (factorial DNF) Experiments conducted on a two-factor case that involves phonetic content and speaker trait demonstrates that the proposed factorial DNF has powerful capability to factorize speech signals.
  arXiv  Detail & Related papers  (2020-10-27T12:27:58Z)

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.