ECLM: Entity Level Language Model for Spoken Language Understanding with Chain of Intent

• URL: http://arxiv.org/abs/2403.04481v4
• Date: Tue, 07 Oct 2025 22:04:38 GMT
• Title: ECLM: Entity Level Language Model for Spoken Language Understanding with Chain of Intent
• Authors: Shangjian Yin, Peijie Huang, Jiatian Chen, Haojing Huang, Yuhong Xu,
• Abstract summary: Large Language Models (LLMs) have demonstrated impressive capabilities in language generation and general task performance.<n>We propose the Entity-level Language Model (ECLM) framework, which reformulates slot-filling as an entity recognition task.<n>ECLM significantly outperforms strong baselines such as Uni-MIS, achieving gains of 3.7% on MixATIS and 3.1% on MixSNIPS.
• Score: 20.3491383818583
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large Language Models (LLMs) have demonstrated impressive capabilities in language generation and general task performance. However, their application to spoken language understanding (SLU) remains challenging, particularly for token-level tasks, where the autoregressive nature of LLMs often leads to misalignment issues. They also struggle to capture nuanced interrelations in semantic-level tasks through direct fine-tuning alone. To address these challenges, we propose the Entity-level Language Model (ECLM) framework, which reformulates slot-filling as an entity recognition task and introduces a novel concept, \textit{Chain of Intent}, to enable step-by-step multi-intent recognition. Experimental results show that ECLM significantly outperforms strong baselines such as Uni-MIS, achieving gains of 3.7\% on MixATIS and 3.1\% on MixSNIPS. Compared to standard supervised fine-tuning of LLMs, ECLM further achieves improvements of 8.5\% and 21.2\% on these datasets, respectively. Our code is available at https://github.com/SJY8460/ECLM.

Related papers

• Enhanced Multimodal Aspect-Based Sentiment Analysis by LLM-Generated Rationales [7.119479942471737]
  Existing methods rely on pre-trained small language models (SLMs) to collect information related to aspects and sentiments from both image and text.<n>We propose a novel framework, termed LRSA, which combines the decision-making capabilities of SLMs with additional information provided by LLMs for MABSA.
  arXiv  Detail & Related papers  (2025-05-20T15:28:26Z)
• Ensemble Learning for Large Language Models in Text and Code Generation: A Survey [6.041894045506043]
  We focus on four methods and models that show strong performance and potential for broader applications. These include better representation of diversity, improved output quality, and greater flexibility in applications.
  arXiv  Detail & Related papers  (2025-03-13T18:50:57Z)
• EMCee: Improving Multilingual Capability of LLMs via Bridging Knowledge and Reasoning with Extracted Synthetic Multilingual Context [6.612630497074871]
  Large Language Models (LLMs) have achieved impressive progress across a wide range of tasks.<n> heavy reliance on English-centric training data leads to significant performance degradation in non-English languages.<n>We propose EMCee, a framework that enhances the multilingual capabilities of LLMs by explicitly extracting and utilizing query-relevant knowledge.
  arXiv  Detail & Related papers  (2025-03-07T06:05:34Z)
• ExLM: Rethinking the Impact of [MASK] Tokens in Masked Language Models [11.997499811414837]
  Masked Language Models (ML)Mss are trained by randomly masking portions of the input sequences with [MASK] tokens and learning to reconstruct the original content based on the remaining context.
  arXiv  Detail & Related papers  (2025-01-23T05:46:50Z)
• Exploring the Implicit Semantic Ability of Multimodal Large Language Models: A Pilot Study on Entity Set Expansion [26.47488223403437]
  We use multimodal large language models (MLLMs) to understand implicit semantic information at the entity-level granularity. We introduce a listwise ranking method LUSAR that maps local scores to global rankings. Our LUSAR demonstrates significant improvements in MLLM's performance on the MESE task, marking the first use of generative MLLM for ESE tasks and extending the applicability of listwise ranking.
  arXiv  Detail & Related papers  (2024-12-31T08:03:48Z)
• LLaVA-KD: A Framework of Distilling Multimodal Large Language Models [70.19607283302712]
  We propose a novel framework to transfer knowledge from l-MLLM to s-MLLM. Specifically, we introduce Multimodal Distillation (MDist) to minimize the divergence between the visual-textual output distributions of l-MLLM and s-MLLM. We also propose a three-stage training scheme to fully exploit the potential of s-MLLM.
  arXiv  Detail & Related papers  (2024-10-21T17:41:28Z)
• Stacking Small Language Models for Generalizability [0.0]
  Large language models (LLMs) generalize strong performance across different natural language benchmarks. This paper introduces a new approach called fine-tuning stacks of language models (FSLM) By fine-tuning each SLM to perform a specific task, this approach breaks down high level reasoning into multiple lower-level steps that specific SLMs are responsible for. As a result, FSLM allows for lower training and inference costs, and also improves model interpretability as each SLM communicates with the subsequent one through natural language.
  arXiv  Detail & Related papers  (2024-10-21T01:27:29Z)
• A Comprehensive Review of Multimodal Large Language Models: Performance and Challenges Across Different Tasks [74.52259252807191]
  Multimodal Large Language Models (MLLMs) address the complexities of real-world applications far beyond the capabilities of single-modality systems. This paper systematically sorts out the applications of MLLM in multimodal tasks such as natural language, vision, and audio.
  arXiv  Detail & Related papers  (2024-08-02T15:14:53Z)
• 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)
• Large Language Models as Software Components: A Taxonomy for LLM-Integrated Applications [0.0]
  Large Language Models (LLMs) have become widely adopted recently. Research explores their use both as autonomous agents and as tools for software engineering. LLMs-integrated applications, on the other hand, are software systems that leverage an LLM to perform tasks that would otherwise be impossible or require significant coding effort. This study provides a taxonomy for LLM-integrated applications, offering a framework for analyzing and describing these systems.
  arXiv  Detail & Related papers  (2024-06-13T21:32:56Z)
• The Strong Pull of Prior Knowledge in Large Language Models and Its Impact on Emotion Recognition [74.04775677110179]
  In-context Learning (ICL) has emerged as a powerful paradigm for performing natural language tasks with Large Language Models (LLM) We show that LLMs have strong yet inconsistent priors in emotion recognition that ossify their predictions. Our results suggest that caution is needed when using ICL with larger LLMs for affect-centered tasks outside their pre-training domain.
  arXiv  Detail & Related papers  (2024-03-25T19:07:32Z)
• Found in the Middle: How Language Models Use Long Contexts Better via Plug-and-Play Positional Encoding [78.36702055076456]
  This paper introduces Multi-scale Positional. (Ms-PoE) which is a simple yet effective plug-and-play approach to enhance the capacity of. LLMs to handle relevant information located in the middle of the context.
  arXiv  Detail & Related papers  (2024-03-05T04:58:37Z)
• SPHINX-X: Scaling Data and Parameters for a Family of Multi-modal Large Language Models [97.40590590880144]
  We develop an extensive Multimodality Large Language Model (MLLM) series. We assemble a comprehensive dataset covering publicly available resources in language, vision, and vision-language tasks. We obtain a spectrum of MLLMs that vary in parameter size and multilingual capabilities.
  arXiv  Detail & Related papers  (2024-02-08T18:59:48Z)
• Knowledge Fusion of Large Language Models [73.28202188100646]
  This paper introduces the notion of knowledge fusion for large language models (LLMs) We externalize their collective knowledge and unique strengths, thereby elevating the capabilities of the target model beyond those of any individual source LLM. Our findings confirm that the fusion of LLMs can improve the performance of the target model across a range of capabilities such as reasoning, commonsense, and code generation.
  arXiv  Detail & Related papers  (2024-01-19T05:02:46Z)
• 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)
• Supervised Knowledge Makes Large Language Models Better In-context Learners [94.89301696512776]
  Large Language Models (LLMs) exhibit emerging in-context learning abilities through prompt engineering. The challenge of improving the generalizability and factuality of LLMs in natural language understanding and question answering remains under-explored. We propose a framework that enhances the reliability of LLMs as it: 1) generalizes out-of-distribution data, 2) elucidates how LLMs benefit from discriminative models, and 3) minimizes hallucinations in generative tasks.
  arXiv  Detail & Related papers  (2023-12-26T07:24:46Z)
• Towards Vision Enhancing LLMs: Empowering Multimodal Knowledge Storage and Sharing in LLMs [72.49064988035126]
  We propose an approach called MKS2, aimed at enhancing multimodal large language models (MLLMs) Specifically, we introduce the Modular Visual Memory, a component integrated into the internal blocks of LLMs, designed to store open-world visual information efficiently. Our experiments demonstrate that MKS2 substantially augments the reasoning capabilities of LLMs in contexts necessitating physical or commonsense knowledge.
  arXiv  Detail & Related papers  (2023-11-27T12:29:20Z)
• TRACE: A Comprehensive Benchmark for Continual Learning in Large Language Models [52.734140807634624]
  Aligned large language models (LLMs) demonstrate exceptional capabilities in task-solving, following instructions, and ensuring safety. Existing continual learning benchmarks lack sufficient challenge for leading aligned LLMs. We introduce TRACE, a novel benchmark designed to evaluate continual learning in LLMs.
  arXiv  Detail & Related papers  (2023-10-10T16:38:49Z)
• On the Performance of Multimodal Language Models [4.677125897916577]
  This study conducts a comparative analysis of different multimodal instruction tuning approaches. We reveal key insights for guiding architectural choices when incorporating multimodal capabilities into large language models.
  arXiv  Detail & Related papers  (2023-10-04T23:33:36Z)
• Improving Open Information Extraction with Large Language Models: A Study on Demonstration Uncertainty [52.72790059506241]
  Open Information Extraction (OIE) task aims at extracting structured facts from unstructured text. Despite the potential of large language models (LLMs) like ChatGPT as a general task solver, they lag behind state-of-the-art (supervised) methods in OIE tasks.
  arXiv  Detail & Related papers  (2023-09-07T01:35:24Z)
• Macaw-LLM: Multi-Modal Language Modeling with Image, Audio, Video, and Text Integration [50.94902442781148]
  We propose a novel multi-modal large language model (LLM) that seamlessly integrates visual, audio, and textual information. Macaw-LLM consists of three main components: a modality module for encoding multi-modal data, a cognitive module for harnessing pretrained LLMs, and an alignment module for harmonizing diverse representations. We construct a large-scale multi-modal instruction dataset in terms of multi-turn dialogue, including 69K image instances and 50K video instances.
  arXiv  Detail & Related papers  (2023-06-15T12:45:25Z)

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.