| Challenge: | Existing knowledge-grounded models rely on elaborate data engineering or increasing the model’s parameters ignoring to track the tokens that significantly influence losses, which is decisive for the optimization direction of the model in each iteration. |
| Approach: | They propose a novel learning approach that adjusts the contribution of each token to the optimization direction by directly scaling the corresponding objective loss. |
| Outcome: | The proposed approach achieves the new state-of-the-art results and generates more reliable responses while maintaining training stability. |
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Chenxu Yang, Zheng Lin, Lanrui Wang, Chong Tian, Liang Pang, Jiangnan Li, Qirong Ho, Yanan Cao, Weiping Wang
| Challenge: | Existing knowledge-grounded dialogue generation models struggle with dull and repetitive outputs, a problem commonly termed as text degeneration. |
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Relevance Is a Guiding Light: Relevance-aware Adaptive Learning for End-to-end Task-oriented Dialogue System (2024.emnlp-main)
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| Challenge: | Existing approaches to training task-oriented dialogue systems struggle with the Distractive Attributes Problem (DAP) Existing methods struggle to deal with false but similar knowledge (hard negative entities) |
| Approach: | They propose a two-stage training framework that eliminates hard negatives step-by-step and aligns retrieval with generation. |
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Reciprocal Learning of Knowledge Retriever and Response Ranker for Knowledge-Grounded Conversations (2022.coling-1)
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| Challenge: | Recent work on grounding dialogue agents with knowledge documents has sparked increased attention . hand-labeling data to that end is time-consuming and many datasets lack knowledge annotations . |
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Increasing Faithfulness in Knowledge-Grounded Dialogue with Controllable Features (2021.acl-long)
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| Challenge: | Existing systems that strive to be informative teachers are difficult to build . knowledge grounded dialogue systems are difficult because of limited training objectives . |
| Approach: | They propose to train a generative neural dialogue model that is controlled to stay faithful to evidence . they propose to use additional inputs to generate more objective responses . |
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Contextual Knowledge Learning for Dialogue Generation (2023.acl-long)
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| Challenge: | Incorporating conversational context and knowledge into dialogue generation models has been essential for improving the quality of the generated responses. |
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Focus Attention: Promoting Faithfulness and Diversity in Summarization (2021.acl-long)
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| Challenge: | Currently, document summarization is challenging even for humans. |
| Approach: | They propose a focus attention mechanism which encourages decoders to generate tokens that are topically similar to the input document. |
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Neural Path Hunter: Reducing Hallucination in Dialogue Systems via Path Grounding (2021.emnlp-main)
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| Challenge: | Dialogue systems that generate factually incorrect responses are often unfitful and hallucinate factuality invalid. |
| Approach: | They propose a method to improve faithfulness and reduce hallucination of neural dialogue systems to known facts supplied by a Knowledge Graph. |
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A Contextual Hierarchical Attention Network with Adaptive Objective for Dialogue State Tracking (2020.acl-main)
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| Challenge: | Existing methods for dialogue state tracking ignore the slot imbalance problem and treat all slots indiscriminately, which limits the learning of hard slots. |
| Approach: | They propose to employ a contextual hierarchical attention network to enhance the DST by learning contextual representations. |
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Large-Scale Multi-Domain Belief Tracking with Knowledge Sharing (P18-2)
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| Challenge: | Existing approaches to scalability of dialogue belief tracking are dependent on the ontology of the dialogue . current approaches are not scalable to multi-domain dialogues because of the effort required to define a semantic dictionary for each domain. |
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Beyond Reasoning Gains: Mitigating General-Capability Forgetting in Large Reasoning Models (2026.findings-acl)
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Hoang Phan, Xianjun Yang, Yuanshun Yao, Jingyu Zhang, Shengjie Bi, Xiaocheng Tang, Madian Khabsa, Lijuan Liu, Deren Lei
| Challenge: | Reinforcement learning with verifiable rewards (RLVR) has delivered impressive gains in mathematical and multimodal reasoning . however, the recipe introduces a significant risk of capability regression, where models forget foundational skills after prolonged training without employing regularization strategies. |
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