Papers with conversational abilities
A Practical Approach for Building Production-Grade Conversational Agents with Workflow Graphs (2025.acl-industry)
Copied to clipboard
Chiwan Park, Wonjun Jang, Daeryong Kim, Aelim Ahn, Kichang Yang, Woosung Hwang, Jihyeon Roh, Hyerin Park, Hyosun Wang, Min Seok Kim, Jihoon Kang
| Challenge: | Large Language Models (LLMs) have led to significant improvements in various service domains, including search, recommendation, and chatbot applications. |
| Approach: | They propose a framework for developing scalable, controllable, and reliable AI-driven agents that can be applied to real-world applications. |
| Outcome: | The proposed framework bridges the gap between academic research and real-world application, and enables scalable, controllable, and reliable AI-driven agents. |
Building a Role Specified Open-Domain Dialogue System Leveraging Large-Scale Language Models (2022.naacl-main)
Copied to clipboard
| Challenge: | Recent large-scale language models have produced human-like responses in open-domain dialogue systems. |
| Approach: | They propose a framework for imposing roles on open-domain dialogue systems . they use few-shot learning to build a Korean dialogue dataset from scratch . |
| Outcome: | The proposed framework meets role specifications while maintaining conversational abilities. |
Anchoring-Guidance Fine-Tuning (AnGFT): Elevating Professional Response Quality in Role-Playing Conversational Agents (2025.emnlp-main)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) have demonstrated significant advancements in various fields, notably in Role-Playing Conversational Agents (RPCAs). |
| Approach: | They propose an Anchoring-Guidance Fine-Tuning Framework to integrate relevant expert knowledge into RPCAs' training process to mitigate this issue. |
| Outcome: | The proposed framework significantly improves the RPCAs’ performance in handling role-specific professional queries while preserving their robust role-playing abilities. |
Frugal Prompting for Dialog Models (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Large language models (LLMs) are used in natural language processing tasks with an unrealistic speed and effectiveness. |
| Approach: | They propose more compact ways of providing dialog history information while ensuring good performance and reducing model’s inference-API costs. |
| Outcome: | The proposed models have the optimal usable-information density while maintaining good performance and reducing model’s inference-API costs. |
Retrieval Augmentation Reduces Hallucination in Conversation (2021.findings-emnlp)
Copied to clipboard
| Challenge: | State-of-the-art dialogue models suffer from factual incorrectness and hallucination of knowledge. |
| Approach: | They propose to use neural-retrieval-in-the-loop architectures to optimize knowledge-grounded dialogue by retrieving, ranking, and encoder-decoders. |
| Outcome: | The proposed architectures exhibit open-domain conversational capabilities and generalize effectively to scenarios not within the training data. |
VoxMind: An End-to-End Agentic Spoken Dialogue System (2026.acl-long)
Copied to clipboard
Tianle Liang, Yifu Chen, Shengpeng Ji, Yijun Chen, Zhiyang Jia, Jingyu Lu, Fan Zhuo, Xueyi Pu, Yangzhuo Li, Zhou Zhao
| Challenge: | Existing research on end-to-end spoken dialogue models has focused on core perception and generation, with limited exploration of tool-augmented extensions. |
| Approach: | They propose a framework to equip end-to-end spoken dialogue models with comprehensive agentic abilities by leveraging a 470-hour AgentChat dataset. |
| Outcome: | The proposed framework outperforms Gemini-2.5-Pro on spoken agent tasks while maintaining general conversational quality. |
LIONs: An Empirically Optimized Approach to Align Language Models (2024.emnlp-main)
Copied to clipboard
| Challenge: | Recent studies have focused on aligning large language models with pre-trained datasets. |
| Approach: | They conduct a rigorous analysis of a three-stage training pipeline using sequence packing, loss masking and increasing the preference dataset size in DPO to improve the performance of language models. |
| Outcome: | The proposed models outperform the official instruct models tuned with closed-source data and algorithms. |
Improving Conversational Abilities of Quantized Large Language Models via Direct Preference Alignment (2024.acl-long)
Copied to clipboard
| Challenge: | Quantization-aware direct preference optimization (QDPO) improves conversational abilities of quantized LLMs . token-flipping is a critical factor for degraded text generation quality . |
| Approach: | They propose a method that aligns quantized LLMs with their full-precision counterparts, improving conversational abilities. |
| Outcome: | The proposed method outperforms established fine-tuning techniques on two instruction-tuned LLMs in various languages and models, setting a new benchmark for conversational chatbot development. |
daDPO: Distribution-Aware DPO for Distilling Conversational Abilities (2025.findings-acl)
Copied to clipboard
| Challenge: | Knowledge distillation (KD) with Direct Preference Optimization (DPO) has emerged as a promising approach to enhance the conversational abilities of smaller models using a larger teacher model. |
| Approach: | They propose a framework that integrates the teacher's distributional information into DPO distillation while preserving theoretical guarantees. |
| Outcome: | The proposed framework outperforms existing methods in restoring performance for pruned models and enhancing smaller models within the same LLM family. |
Arabic Mini-ClimateGPT : A Climate Change and Sustainability Tailored Arabic LLM (2023.findings-emnlp)
Copied to clipboard
Sahal Mullappilly, Abdelrahman Shaker, Omkar Thawakar, Hisham Cholakkal, Rao Anwer, Salman Khan, Fahad Khan
| Challenge: | Recent large language models like ChatGPT and Bard excel in a wide variety of NLP tasks but are not specifically tailored for climate related domain specific information. |
| Approach: | They propose a lightweight Arabic Mini-ClimateGPT that is built on an open-source LLM and specifically fine-tuned on a conversational-style instruction tuning curated Arabic dataset Clima500-Instruct. |
| Outcome: | The proposed model surpasses the baseline LLM in 88.3% of cases during ChatGPT-based evaluation and human expert prefers it over other open-source models. |