DIALIGHT: Lightweight Multilingual Development and Evaluation of Task-Oriented Dialogue Systems with Large Language Models

• URL: http://arxiv.org/abs/2401.02208v1
• Date: Thu, 4 Jan 2024 11:27:48 GMT
• Title: DIALIGHT: Lightweight Multilingual Development and Evaluation of Task-Oriented Dialogue Systems with Large Language Models
• Authors: Songbo Hu, Xiaobin Wang, Zhangdie Yuan, Anna Korhonen, Ivan Vuli\'c
• Abstract summary: DIALIGHT is a toolkit for developing and evaluating multilingual Task-Oriented Dialogue (ToD) systems. It features a secure, user-friendly web interface for fine-grained human evaluation at both local utterance level and global dialogue level. Our evaluations reveal that while PLM fine-tuning leads to higher accuracy and coherence, LLM-based systems excel in producing diverse and likeable responses.
• Score: 76.79929883963275
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present DIALIGHT, a toolkit for developing and evaluating multilingual Task-Oriented Dialogue (ToD) systems which facilitates systematic evaluations and comparisons between ToD systems using fine-tuning of Pretrained Language Models (PLMs) and those utilising the zero-shot and in-context learning capabilities of Large Language Models (LLMs). In addition to automatic evaluation, this toolkit features (i) a secure, user-friendly web interface for fine-grained human evaluation at both local utterance level and global dialogue level, and (ii) a microservice-based backend, improving efficiency and scalability. Our evaluations reveal that while PLM fine-tuning leads to higher accuracy and coherence, LLM-based systems excel in producing diverse and likeable responses. However, we also identify significant challenges of LLMs in adherence to task-specific instructions and generating outputs in multiple languages, highlighting areas for future research. We hope this open-sourced toolkit will serve as a valuable resource for researchers aiming to develop and properly evaluate multilingual ToD systems and will lower, currently still high, entry barriers in the field.

Related papers

• The AI Language Proficiency Monitor -- Tracking the Progress of LLMs on Multilingual Benchmarks [0.0]
  We introduce the AI Language Monitor, a comprehensive benchmark that assesses large language models (LLMs) performance across up to 200 languages.<n>Our benchmark aggregates diverse tasks including translation, question answering, math, and reasoning, using datasets such as FLORES+, MMLU, GSM8K, TruthfulQA, and ARC.<n>We provide an open-source, auto-updating leaderboard and dashboard that supports researchers, developers, and policymakers in identifying strengths and gaps in model performance.
  arXiv  Detail & Related papers  (2025-07-11T12:38:02Z)
• Large Language Models for Multilingual Vulnerability Detection: How Far Are We? [13.269680075539135]
  We evaluate the effectiveness of pre-trained language models (PLMs) and large language models (LLMs) for multilingual vulnerability detection.<n>Using over 30,000 real-world vulnerability-fixing patches across seven programming languages, we assess model performance at both the function-level and line-level.<n>Our key findings indicate that GPT-4o, enhanced through instruction tuning and few-shot prompting, significantly outperforms all other evaluated models.
  arXiv  Detail & Related papers  (2025-06-09T07:27:49Z)
• MUG-Eval: A Proxy Evaluation Framework for Multilingual Generation Capabilities in Any Language [16.21019515431378]
  We propose MUG-Eval, a novel framework that evaluates large language models' multilingual generation capabilities.<n>We transform existing benchmarks into conversational tasks and measure the LLMs' accuracies on those tasks.<n>We evaluate 8 LLMs across 30 languages spanning high, mid, and low-resource categories, and we find that MUG-Eval correlates strongly with established benchmarks.
  arXiv  Detail & Related papers  (2025-05-20T14:14:00Z)
• Large Language Models for Code Generation: The Practitioners Perspective [4.946128083535776]
  Large Language Models (LLMs) have emerged as coding assistants, capable of generating source code from natural language prompts. We propose and develop a multi-model unified platform to generate and execute code based on natural language prompts. We conducted a survey with 60 software practitioners from 11 countries across four continents to evaluate the usability, performance, strengths, and limitations of each model.
  arXiv  Detail & Related papers  (2025-01-28T14:52:16Z)
• INCLUDE: Evaluating Multilingual Language Understanding with Regional Knowledge [36.234295907476515]
  The development of functional large language models (LLM) is bottlenecked by the lack of high-quality evaluation resources in languages other than English. In this work, we construct an evaluation suite of 197,243 QA pairs from local exam sources to measure the capabilities of multilingual LLMs in a variety of regional contexts.
  arXiv  Detail & Related papers  (2024-11-29T16:03:14Z)
• Balancing Accuracy and Efficiency in Multi-Turn Intent Classification for LLM-Powered Dialog Systems in Production [6.459396785817196]
  This paper presents two novel approaches to enhance scalability and reduce latency in production dialogue systems. First, we introduce Symbol Tuning, which simplifies intent labels to reduce task complexity and improve performance in multi-turn dialogues. Second, we propose C-LARA, a framework that employs LLMs for data augmentation and pseudo-labeling to generate synthetic multi-turn dialogues.
  arXiv  Detail & Related papers  (2024-11-19T07:48:35Z)
• P-MMEval: A Parallel Multilingual Multitask Benchmark for Consistent Evaluation of LLMs [84.24644520272835]
  We introduce P-MMEval, a large-scale benchmark covering effective fundamental and capability-specialized datasets.<n>P-MMEval delivers consistent language coverage across various datasets and provides parallel samples.<n>We conduct extensive experiments on representative multilingual model series to compare performances across models and tasks.
  arXiv  Detail & Related papers  (2024-11-14T01:29:36Z)
• Training of Scaffolded Language Models with Language Supervision: A Survey [62.59629932720519]
  This survey organizes the literature on the design and optimization of emerging structures around post-trained LMs.<n>We refer to this overarching structure as scaffolded LMs and focus on LMs that are integrated into multi-step processes with tools.
  arXiv  Detail & Related papers  (2024-10-21T18:06:25Z)
• Lens: Rethinking Multilingual Enhancement for Large Language Models [70.85065197789639]
  Lens is a novel approach to enhance multilingual capabilities of large language models (LLMs) It operates by manipulating the hidden representations within the language-agnostic and language-specific subspaces from top layers of LLMs. It achieves superior results with much fewer computational resources compared to existing post-training approaches.
  arXiv  Detail & Related papers  (2024-10-06T08:51:30Z)
• CUDRT: Benchmarking the Detection Models of Human vs. Large Language Models Generated Texts [9.682499180341273]
  Large language models (LLMs) have greatly enhanced text generation across industries. Their human-like outputs make distinguishing between human and AI authorship challenging. Current benchmarks mainly rely on static datasets, limiting their effectiveness in assessing model-based detectors.
  arXiv  Detail & Related papers  (2024-06-13T12:43:40Z)
• Machine Translation with Large Language Models: Prompt Engineering for Persian, English, and Russian Directions [0.0]
  Generative large language models (LLMs) have demonstrated exceptional proficiency in various natural language processing (NLP) tasks. We conducted an investigation into two popular prompting methods and their combination, focusing on cross-language combinations of Persian, English, and Russian.
  arXiv  Detail & Related papers  (2024-01-16T15:16:34Z)
• LMRL Gym: Benchmarks for Multi-Turn Reinforcement Learning with Language Models [56.25156596019168]
  This paper introduces the LMRL-Gym benchmark for evaluating multi-turn RL for large language models (LLMs) Our benchmark consists of 8 different language tasks, which require multiple rounds of language interaction and cover a range of tasks in open-ended dialogue and text games.
  arXiv  Detail & Related papers  (2023-11-30T03:59:31Z)
• Cross-lingual Transfer in Programming Languages: An Extensive Empirical Study [5.350495525141013]
  Large language models (LLMs) have achieved state-of-the-art performance in various software engineering tasks.<n>critical languages, such as Rust and Swift, remain low-resource due to limited openly available code.<n>We develop a performance prediction model to guess the best source languages for a given target and task.
  arXiv  Detail & Related papers  (2023-10-25T19:04:33Z)
• A Systematic Study of Performance Disparities in Multilingual Task-Oriented Dialogue Systems [68.76102493999134]
  We take stock of and empirically analyse task performance disparities that exist between multilingual task-oriented dialogue systems. We prove the existence of the adaptation and intrinsic biases in current ToD systems. Our analyses offer practical tips on how to approach ToD data collection and system development for new languages.
  arXiv  Detail & Related papers  (2023-10-19T16:41:44Z)
• L2CEval: Evaluating Language-to-Code Generation Capabilities of Large Language Models [102.00201523306986]
  We present L2CEval, a systematic evaluation of the language-to-code generation capabilities of large language models (LLMs) We analyze the factors that potentially affect their performance, such as model size, pretraining data, instruction tuning, and different prompting methods. In addition to assessing model performance, we measure confidence calibration for the models and conduct human evaluations of the output programs.
  arXiv  Detail & Related papers  (2023-09-29T17:57:00Z)
• Overcoming Language Disparity in Online Content Classification with Multimodal Learning [22.73281502531998]
  Large language models are now the standard to develop state-of-the-art solutions for text detection and classification tasks. The development of advanced computational techniques and resources is disproportionately focused on the English language. We explore the promise of incorporating the information contained in images via multimodal machine learning.
  arXiv  Detail & Related papers  (2022-05-19T17:56:02Z)
• Reinforced Iterative Knowledge Distillation for Cross-Lingual Named Entity Recognition [54.92161571089808]
  Cross-lingual NER transfers knowledge from rich-resource language to languages with low resources. Existing cross-lingual NER methods do not make good use of rich unlabeled data in target languages. We develop a novel approach based on the ideas of semi-supervised learning and reinforcement learning.
  arXiv  Detail & Related papers  (2021-06-01T05:46:22Z)

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.