This study systematically compares end-to-end (E2E) audio language models (AudioLMs) against modular (ASR, LLM, TTS) systems for multi-phase task-oriented dialogues. We evaluate open-source models on key metrics: conversational naturalness and dialogue consistency. Our findings show that E2E configurations consistently underperform their modular counterparts, exhibiting severe degradation in dialogue quality across turns. Investigating this failure, our analysis reveals that the core issue lies in the E2E models’ dialogue modeling capabilities, specifically in context maintenance and topic tracking. This work highlights a critical gap between the purported low-latency benefit of AudioLMs and their practical ability to maintain coherence in complex, multi-turn dialogues, suggesting a need for focused architectural improvements.

Multiple-choice exam questions with “None of the above” (NA) options have been extensively studied in educational testing, in which existing research suggests that they better assess true knowledge. However, their impact on Large Language Models (LLMs) evaluation remains underexplored. Through systematic experiments with 28 LLMs on the MMLU benchmark, we examine how NA options affect model performance and confidence calibration. Our analysis reveals that NA options, when used as the correct answer, lead to a consistent 30-50% performance drop across models regardless of scale–suggesting that LLMs lack the meta-cognitive ability to systematically evaluate and reject all given options when none are correct. This degradation shows strong domain dependence, with minimal impact on mathematical reasoning (14.6% drop) but severe effects on tasks requiring uncertainty handling like business ethics (48.1% drop). Our results highlight important implications for benchmark design and raise questions about LLMs’ ability to handle uncertainty in real-world applications.

This study explores the proactive ability of LLMs to seek user support. We propose metrics to evaluate the trade-off between performance improvements and user burden, and investigate whether LLMs can determine when to request help under varying information availability. Our experiments show that without external feedback, many LLMs struggle to recognize their need for user support. The findings highlight the importance of external signals and provide insights for future research on improving support-seeking strategies. Source code: https://github.com/appier-research/i-need-help

Structured generation, the process of producing content in standardized formats like JSON and XML, is widely utilized in real-world applications to extract key output information from large language models (LLMs).This study investigates whether such constraints on generation space impact LLMs’ abilities, including reasoning and domain knowledge comprehension. Specifically, we evaluate LLMs’ performance when restricted to adhere to structured formats versus generating free-form responses across various common tasks. Surprisingly, we observe a significant decline in LLMs’ reasoning abilities under format restrictions. Furthermore, we find that stricter format constraints generally lead to greater performance degradation in reasoning tasks.