Emotion is a central dimension of spoken communication, yet, we still lack a mechanistic account of how modern large audio-language models (LALMs) encode it internally.We present the first neuron-level interpretability study of emotion-sensitive neurons (ESNs) in LALMs and provide causal evidence supporting the existence of such units in Qwen2.5-Omni,Kimi-Audio, and Audio Flamingo 3. Across these three widely used open-source models, we compare frequency-, entropy-, mean-deviation-, and contrast-based neuron selectors on multiple emotion recognition benchmarks.Using inference-time interventions, we reveal a consistent emotion-specific signature: deactivating neurons selected for a given emotion disproportionately degrades recognition of that emotion while largely preserving other classes, whereas targeted steering amplifies these units to bias predictions toward the target emotion.These effects arise with modest amounts of identification data and scale systematically with intervention strength.We further observe that ESNs exhibit non-uniform layerwise clustering with partial cross-dataset transfer.Taken together, our results offer a causal, neuron-level account of emotion decisions in LALMs and highlight targeted neuron interventions as an actionable handle for controllable affective behaviors.

Existing methods aligning language models with various human needs are reliant heavily on high-quality and task-specific data. However, industrial deployment of task-specific language models often encounter challenges in the availability of appropriate training samples. Taking meeting summarization for instance, public datasets are scarce, and private corpora are also hard to obtain due to privacy issues or resource-demanding annotation. To improve meeting summarization in the absence of positively-rated (i.e., ``good'') samples, we propose Score Tuning, a cold start tuning framework that leverages bad samples of distinguishable degrees to incrementally enhance the performance of summary generation without an initial presence of good samples. Our method utilizes asynchronous and numerical human feedback that measure the quality of generated summaries. Formulating data into triplets of (transcript, summary, score), our approach instructs a pre-trained model to learn the association between summary qualities and human-rated scores and hence to generate better summaries corresponding to higher scores. The experiment results show that our method is effective in improving meeting summarization on both English and Chinese corpora while requiring less annotated data and training resources compared to existing alignment methods. Additionally, we also preliminarily explore the transferability of our approach in machine translation tasks and demonstrate its potential for future development and usage in other domains.

Large language model agents have exhibited exceptional performance across a range of complex interactive tasks.Recent approaches have utilized tuning with expert trajectories to enhance agent performance, yet they primarily concentrate on outcome rewards, which may lead to errors or suboptimal actions due to the absence of process supervision signals.In this paper, we introduce the Iterative step-level Process Refinement (IPR) framework, which provides detailed step-by-step guidance to enhance agent training.Specifically, we adopt the Monte Carlo method to estimate step-level rewards.During each iteration, the agent explores along the expert trajectory and generates new actions.These actions are then evaluated against the corresponding step of expert trajectory using step-level rewards.Such comparison helps identify discrepancies, yielding contrastive action pairs that serve as training data for the agent.Our experiments on three complex agent tasks demonstrate that our framework outperforms a variety of strong baselines.Moreover, our analytical findings highlight the effectiveness of IPR in augmenting action efficiency and its applicability to diverse models † .

Modern large language models (LLMs) have exhibited cooperative synergy on complex tasksolving, and collective decision-making (CDM) is a pivotal component in LLM-based multiagent collaboration frameworks.Our survey on 52 recent such systems uncovers a severe lack of diversity, with a heavy reliance on dictatorial and plurality voting for CDM.Through the lens of social choice theory, we scrutinize widelyadopted CDM methods and identify their limitations.To enrich current landscape of LLMbased CDM, we present GEDI, an electoral CDM module that incorporates various ordinal preferential voting mechanisms.Our empirical case study across three benchmarks shows that the integration of certain CDM methods can markedly improve the reasoning capabilities and robustness of some leading LLMs, all without requiring intricate system designs.Additionally, we find that some CDM mechanisms generate positive synergies even with as few as three agents.The voting-based methods also demonstrate robustness against single points of failure, as well as diversity in terms of hitrate@k and subject-wise impacts. 1

Neural Machine Translation (NMT) systems encounter a significant challenge when translating a pro-drop ('pronoun-dropping') language (e.g., Chinese) to a non-pro-drop one (e.g., English), since the pro-drop phenomenon demands NMT systems to recover omitted pronouns.This unique and crucial task, however, lacks sufficient datasets for benchmarking.To bridge this gap, we introduce PROSE, a new benchmark featured in diverse pro-drop instances for document-level Chinese-English spoken language translation.Furthermore, we conduct an in-depth investigation of the prodrop phenomenon in spoken Chinese on this dataset, reconfirming that pro-drop reduces the performance of NMT systems in Chinese-English translation.To alleviate the negative impact introduced by pro-drop, we propose Mention-Aware Semantic Augmentation, a novel approach that leverages the semantic embedding of dropped pronouns to augment training pairs.Results from the experiments on four Chinese-English translation corpora show that our proposed method outperforms existing methods regarding omitted pronoun retrieval and overall translation quality.

Topic segmentation aims to detect topic boundaries and split automatic speech recognition transcriptions (e.g., meeting transcripts) into segments that are bounded by thematic meanings.In this work, we propose M 3 Seg, a novel Maximum-Minimum Mutual information paradigm for linear topic segmentation without using any parallel data.Specifically, by employing sentence representations provided by pre-trained language models, M 3 Seg first learns a region-based segment encoder based on the maximization of mutual information between the global segment representation and the local contextual sentence representation.Secondly, an edge-based boundary detection module aims to segment the whole by topics based on minimizing the mutual information between different segments.Experiment results on two public datasets demonstrate the effectiveness of M 3 Seg, which outperform the state-of-the-art methods by a significant (18%-37% improvement) margin.

Dialogue agents have been receiving increasing attention for years, and this trend has been further boosted by the recent progress of large language models (LLMs).Stance detection and dialogue summarization are two core tasks of dialogue agents in application scenarios that involve argumentative dialogues.However, research on these tasks is limited by the insufficiency of public datasets, especially for non-English languages.To address this language resource gap in Chinese, we present OR-CHID (Oral Chinese Debate), the first Chinese dataset for benchmarking target-independent stance detection and debate summarization.Our dataset consists of 1,218 real-world debates that were conducted in Chinese on 476 unique topics, containing 2,436 stance-specific summaries and 14,133 fully annotated utterances.Besides providing a versatile testbed for future research, we also conduct an empirical study on the dataset and propose an integrated task.The results show the challenging nature of the dataset and suggest a potential of incorporating stance detection in summarization for argumentative dialogue.1