| Challenge: | Recent advances in large language models have increased the capabilities of conversational AI to solve challenging dialogue problems. |
| Approach: | They propose a task to verify whether two sets of utterances originate from the same speaker. |
| Outcome: | The proposed task aims to verify whether two sets of utterances originate from the same speaker. |
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| Challenge: | Recent studies have shown that LLMs can generate content that aligns with their assigned personality traits, but there is limited research on whether they consistently reflect specific personality traits. |
| Approach: | They propose to study the behavior of LLM-based agents which they refer to as LLM personas and simulate them to measure their personality traits. |
| Outcome: | The proposed model is based on the Big Five personality model and has been validated by human evaluations and automatic evaluations. |
SpeakerSleuth: Can Large Audio-Language Models Judge Speaker Consistency across Multi-turn Dialogues? (2026.acl-long)
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| Challenge: | Large Audio-Language Models (LALMs) are a popular approach for evaluating speech quality, yet their ability to assess speaker consistency across multi-turn dialogues remains unexplored. |
| Approach: | They construct 1,818 human-verified evaluation instances across four datasets spanning synthetic and real speech, with controlled acoustic difficulty. |
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Spoken Conversational Agents with Large Language Models (2025.emnlp-tutorials)
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| Challenge: | This tutorial focuses on the evolution of voice-native LLMs . it reviews the adaptation of text LLM to audio, cross-modal alignment, and joint speech–text training . |
| Approach: | This tutorial examines the evolution of voice-native LLMs in conversational agents . it compares cascaded and voice-based LLM systems to end-to-end retrieval-and vision-grounded systems . |
| Outcome: | This tutorial examines the evolution of voice-native LLMs . it compares the performance of voice assistants to current open-domain agents . |
Can LLM Agents Maintain a Persona in Discourse? (2025.emnlp-main)
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Pranav Bhandari, Nicolas Fay, Michael J Wise, Amitava Datta, Stephanie Meek, Usman Naseem, Mehwish Nasim
| Challenge: | Large language models are often subjected to context-shifting behaviour, resulting in a lack of consistent and interpretable personality-aligned interactions. |
| Approach: | They propose to use two conversation agents to generate a discourse with an assigned personality from the OCEAN framework and then use multiple judge agents to infer original traits. |
| Outcome: | The proposed model is based on two conversation agents with a personality assigned from the OCEAN framework and then multiple judge agents to infer the original traits assigned. |
Evaluating Conversational Agents with Persona-driven User Simulations based on Large Language Models: A Sales Bot Case Study (2025.emnlp-industry)
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Justyna Gromada, Alicja Kasicka, Ewa Komkowska, Lukasz Krajewski, Natalia Krawczyk, Morgan Veyret, Bartosz Przybył, Lina M. Rojas-Barahona, Michał K. Szczerbak
| Challenge: | Recent advances in LLMs enable sophisticated user simulations that can replace traditional rule-based evaluations. |
| Approach: | They propose a persona-driven approach to conversational agent evaluation using Large Language Models (LLMs) they introduce a dataset of customer personas, which are then used to configure a single LLM-based user simulator. |
| Outcome: | The proposed model emulates nuanced customer roles and can implement cross-selling strategies with minimal impact on customer satisfaction, varying by customer type. |
Can LLM be a Personalized Judge? (2024.findings-emnlp)
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| Challenge: | a new study examines the reliability of large language models (LLMs) for personalization and role-playing evaluation without examining its validity. |
| Approach: | They investigate the reliability of LLM-as-a-Personalized-Judge for personalization . they find that personas provided to LLMs have limited predictive power . |
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Evaluating Large Language Models via Linguistic Profiling (2024.emnlp-main)
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| Challenge: | Large Language Models (LLMs) undergo extensive evaluation against various benchmarks collected in established leaderboards to assess their performance across multiple tasks. |
| Approach: | They propose a new evaluation methodology to test LLMs' sentence generation abilities under specific linguistic constraints. |
| Outcome: | The proposed evaluation methodology is based on the 'linguistic profiling' approach and is not intended to be a task-oriented evaluation. |
Can Language Models Recognize Convincing Arguments? (2024.findings-emnlp)
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| Challenge: | Existing studies have found that large language models can generate persuasive content without engaging in human experimentation. |
| Approach: | They extend a dataset with debates, votes, and user traits to measure LLMs' ability to distinguish between strong and weak arguments, predict stances based on beliefs and demographic characteristics, and determine appeal of argument to individual based upon their traits. |
| Outcome: | The proposed tasks outperform human predictions in detecting convincing arguments in debates, votes, and user traits. |
Simple Agents, Biased Judges: Efficient Multi-Party Dialogue Generation & The Evaluation Gap (2026.acl-long)
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| Challenge: | Multiparty social dialogue is difficult to formalize and expensive to evaluate, especially at scale. |
| Approach: | They propose a lightweight and controllable multi-party dialoguegeneration framework as an experimental instrument for studying generation and evaluation in social interaction. |
| Outcome: | The proposed framework shows that human judgments against state-of-the-art LLM judges are consistent with human preferences for naturalness, engagingness, and overall quality in multi-party social dialogue. |
Real or Robotic? Assessing Whether LLMs Accurately Simulate Qualities of Human Responses in Human-LLM Dialogue (2026.findings-acl)
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Jonathan Ivey, Shivani Kumar, Jiayu Liu, Hua Shen, Sushrita Rakshit, Rohan Raju, Haotian Zhang, Aparna Ananthasubramaniam, Junghwan Kim, Bowen Yi, Dustin Wright, Abraham Israeli, Anders Giovanni Møller, Lechen Zhang, David Jurgens
| Challenge: | Recent work has sought to use large language models to simulate human-human and human-LLM interactions. |
| Approach: | They use a large-scale dataset to generate a paired LLM-LLM and human-LLm dialogues from the WildChat dataset and quantify how well they align with their human counterparts. |
| Outcome: | The proposed models perform similarly in simulating English, Chinese, and Russian dialogues. |