Tommy Sandbank, Michal Shmueli-Scheuer, Jonathan Herzig, David Konopnicki, John Richards, David Piorkowski
| Challenge: | 80% of businesses plan to use chatbots by 2020, according to recent studies . but some bad conversations can be difficult to detect and could lead to litigation . |
| Approach: | They propose a method to detect bad conversations using behavioral cues from the user and patterns in agent responses. |
| Outcome: | The proposed method improves the detection F1 score by 20% over textual features. |
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Wei Yang, Luchen Tan, Chunwei Lu, Anqi Cui, Han Li, Xi Chen, Kun Xiong, Muzi Wang, Ming Li, Jian Pei, Jimmy Lin
| Challenge: | e-commerce companies often have the option of escalating complaints by filing grievances with a government authority . this is detrimental to an ecommerce company, but this problem is challenging to solve by integrating recurrent neural networks with manually-engineered features. |
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Bot-Adversarial Dialogue for Safe Conversational Agents (2021.naacl-main)
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| Challenge: | a new method for evaluating chatbot safety is proposed to mimic human-generated data . a bot-adversarial dialogue model learns undesirable features from this data, a study finds . |
| Approach: | They propose a human-and-model-in-the-loop framework for evaluating toxicity of chatbots . they propose two methods for safe conversational agents by either training on data or ”baking-in” safety to the generative model itself. |
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Entity Exchange in the Wild: A Diagnostic Study of LLM Based Real-World Conversational Entity Extraction (2026.acl-industry)
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| Challenge: | Prior work has examined the impact of transcription noise and cross-turn reasoning, but it has not systematically analyzed how entity-exchange phenomena themselves shape extraction performance. |
| Approach: | They evaluate 16 large language models on 6,387 real-world customer–agent conversations spanning 12 entity types across numeric, alphanumeric, temporal, and free-text categories. |
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Goal-Oriented End-to-End Conversational Models with Profile Features in a Real-World Setting (N19-2)
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| Challenge: | a recent study has focused on how algorithmic improvements help model performance on fabricated datasets. |
| Approach: | They propose two approaches to train conversational neural models for goal-oriented conversational systems . they train models on historical chat transcripts and test on live contacts . |
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Toward Safe and Human-Aligned Game Conversational Recommendation via Multi-Agent Decomposition (2026.findings-eacl)
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| Challenge: | Existing systems for conversational recommender systems (CRS) have strong results in movies, but games present distinct challenges . MATCHA framework provides specialized agents for intent parsing, tool-augmented retrieval, multi-LLM ranking, and stronger safety. |
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ToxicChat: Unveiling Hidden Challenges of Toxicity Detection in Real-World User-AI Conversation (2023.findings-emnlp)
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| Challenge: | toxicity detection has been largely based on social media content, leaving the unique challenges inherent to real-world user-AI interactions insufficiently explored. |
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Conversations Gone Awry: Detecting Early Signs of Conversational Failure (P18-1)
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Justine Zhang, Jonathan Chang, Cristian Danescu-Niculescu-Mizil, Lucas Dixon, Yiqing Hua, Dario Taraborelli, Nithum Thain
| Challenge: | Prior work focused on characterizing and detecting content exhibiting antisocial online behavior. |
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The Adaptive Interrogator: Detecting Trojan LLMs in Multi-Agent Systems via Evolved Conversational Strategies (2026.findings-acl)
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| Challenge: | Large Language Models (LLMs) have been focused on single-agent, white-box environments, but multi-agend systems (MAS) have a critical blind spot: supply chain vulnerabilities. |
| Approach: | They propose a black-box auditing framework that leverages an Evolutionary Algorithm to autonomously expose hidden threats. |
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One Agent To Rule Them All: Towards Multi-agent Conversational AI (2022.findings-acl)
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Christopher Clarke, Joseph Peper, Karthik Krishnamurthy, Walter Talamonti, Kevin Leach, Walter Lasecki, Yiping Kang, Lingjia Tang, Jason Mars
| Challenge: | Increasing volume of conversational agents (CAs) on the market has resulted in users being burdened with learning and adopting multiple agents to accomplish their tasks. |
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ConvApparel: A Benchmark Dataset and Validation Framework for User Simulators in Conversational Recommenders (2026.eacl-long)
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Ofer Meshi, Krisztian Balog, Sally Goldman, Avi Caciularu, Guy Tennenholtz, Jihwan Jeong, Amir Globerson, Craig Boutilier
| Challenge: | a "realism gap" exists between simulations and real-world user models . large language models (LLMs) are a key component of conversational AI . |
| Approach: | They propose a framework that combines statistical alignment, human-likeness score and counterfactual validation to test for generalization. |
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