An Improved, Strong Baseline for Pre-Trained Large Language Models as Task-Oriented Dialogue Systems (2025.findings-emnlp)
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| Challenge: | Recent studies have shown that Large Language Models perform insufficiently as TOD systems. |
| Approach: | They propose a self-checking mechanism to improve LLM performance as TOD systems. |
| Outcome: | The proposed model outperforms existing models and improves their performance. |
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DIALIGHT: Lightweight Multilingual Development and Evaluation of Task-Oriented Dialogue Systems with Large Language Models (2024.naacl-demo)
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| Challenge: | DIALIGHT is a toolkit for developing and evaluating multilingual Task-Oriented Dialogue systems. |
| Approach: | They propose a toolkit for developing and evaluating multilingual Task-Oriented Dialogue systems which facilitates systematic evaluations and comparisons between ToD systems using pretrained language models and those utilising the zero-shot and in-context learning capabilities of Large Language Models. |
| Outcome: | The toolkit enables systematic evaluations between ToD systems using pretrained language models and those utilising the zero-shot and in-context learning capabilities of Large Language Models (LLMs). |
TOD-BERT: Pre-trained Natural Language Understanding for Task-Oriented Dialogue (2020.emnlp-main)
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| Challenge: | Existing pre-trained language models with self-attention encoder architectures are less useful in practice. |
| Approach: | They propose to use user and system tokens to model dialogue behavior during pre-training . they propose a contrastive objective function to simulate the response selection task . |
| Outcome: | The proposed model outperforms baseline models on four downstream tasks . it also has a few-shot ability that can mitigate the data scarcity problem . |
DivTOD: Unleashing the Power of LLMs for Diversifying Task-Oriented Dialogue Representations (2024.findings-naacl)
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| Challenge: | Existing language models pre-trained on general text overlook the one-to-many property of task-oriented dialogues, where multiple responses can be appropriate given the same context. |
| Approach: | They propose a model that pre-trains LLMs to learn diverse task-oriented dialogue representations by removing domain knowledge that contradicts TODs. |
| Outcome: | The proposed model outperforms strong TOD baselines on various downstream dialogue tasks and learns the intrinsic diversity of task-oriented dialogues. |
Challenging Large Language Models with New Tasks: A Study on their Adaptability and Robustness (2024.findings-acl)
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| Challenge: | Existing evaluation approaches for large language models (LLMs) rely on existing tasks and benchmarks, raising concerns about test set contamination and the genuine comprehension abilities of LLMs. |
| Approach: | They propose to evaluate LLMs by designing new tasks, automatically generating evaluation datasets for the tasks, and conducting detailed error analyses to scrutinize LLM's adaptability to new tasks. |
| Outcome: | The proposed method examines LLMs’ adaptability to new tasks, their sensitivity to prompt variations, and their error tendencies. |
Natural Language Processing for Multilingual Task-Oriented Dialogue (2022.acl-tutorials)
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| Challenge: | a tutorial will examine the challenges and gaps in multilingual ToD research . multilingual systems are difficult to build, and are limited to English and other languages . |
| Approach: | This tutorial will discuss the importance of multilingual task-oriented dialogue systems . it will provide an overview of current research gaps, challenges and initiatives related to multilingual ToD systems - with a particular focus on their connections to current research and challenges in multilingual and low-resource NLP. |
| Outcome: | This tutorial will provide an overview of current research gaps, challenges and initiatives related to multilingual ToD systems. |
Do LLMs Understand Dialogues? A Case Study on Dialogue Acts (2025.acl-long)
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| Challenge: | Large Language Models (LLMs) have shown remarkable performance on many unseen tasks in a zero-shot setting. |
| Approach: | They propose to identify three key pre-tasks essential for accurate DA prediction: Turn Management, Communicative Function Identification, and Dialogue Structure Prediction. |
| Outcome: | The proposed model fails to outperform basic rule-based tasks on three key pre-tasks, and the results suggest that the model is flawed. |
Reasoning Gets Harder for LLMs Inside A Dialogue (2026.acl-long)
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| Challenge: | Large Language Models (LLMs) achieve strong performance on many reasoning benchmarks, yet these evaluations typically focus on isolated tasks that differ from real-world usage in task-oriented dialogue (TOD). |
| Approach: | They propose to use a dynamic benchmark to examine how framing reasoning tasks within task-oriented dialogue (TOD) affect LLM performance. |
| Outcome: | The proposed model performs well on isolated tasks and in task-oriented dialogues, but performance is inconsistent between them. |
LLMs for Low Resource Languages in Multilingual, Multimodal and Dialectal Settings (2024.eacl-tutorials)
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| Challenge: | Recent advances in AI can be attributed to the remarkable performance of Large Language Models (LLMs) success of LLMs depends on specific training techniques, such as instruction tuning and prompting . |
| Approach: | They explore the capabilities of Large Language Models (LLMs) in various tasks and languages . they also examine their performance, fine-tuning, instructions tuning, and close vs. open models . |
| Outcome: | The proposed model can be used for speech and multimodal tasks across modalities, languages, and dialects. |
How Do Large Language Models Capture the Ever-changing World Knowledge? A Review of Recent Advances (2023.emnlp-main)
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| Challenge: | Large language models (LLMs) are impressive in solving tasks, but they can quickly be outdated after deployment. |
| Approach: | They provide a review of recent advances in aligning deployed large language models with the ever-changing world knowledge. |
| Outcome: | The proposed models can be used to perform various tasks directly through in-context learning or for further fine-tuning for domain-specific uses. |
Current Advances in LLM Reasoning (2026.acl-tutorials)
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| Challenge: | This tutorial examines comprehensive evaluation strategies to assess the reasoning abilities of large language models (LLMs) advanced inference time methods and post-training methods that aim to make LLMs think more like humans are discussed in this tutorial. |
| Approach: | This tutorial explores comprehensive evaluation strategies to assess the reasoning abilities of large language models (LLMs) and discusses two types of methods to improve models’ reasoning: advanced inference time methods, structured and self-improvement inference methods, and post-training methods, such as RLHF, DPO, and GRPO. |
| Outcome: | This tutorial examines evaluation strategies to assess the reasoning abilities of large language models and discusses two types of methods to improve models’ reasoning. |