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Shengpeng Ji, Qian Chen, Wen Wang, Jialong Zuo, Minghui Fang, Ziyue Jiang, Hai Huang, Zehan Wang, Xize Cheng, Siqi Zheng 等

In this paper, we present ControlSpeech, a text-to-speech (TTS) system capable of fully cloning the speaker’s voice and enabling arbitrary control and adjustment of speaking style. Prior zero-shot TTS models only mimic the speaker’s voice without further control and adjustment capabilities while prior controllable TTS models cannot perform speaker-specific voice generation. Therefore, ControlSpeech focuses on a more challenging task—a TTS system with controllable timbre, content, and style at the same time. ControlSpeech takes speech prompts, content prompts, and style prompts as inputs and utilizes bidirectional attention and mask-based parallel decoding to capture codec representations corresponding to timbre, content, and style in a discrete decoupling codec space. Moreover, we analyze the many-to-many issue in textual style control and propose the Style Mixture Semantic Density (SMSD) module, which is based on Gaussian mixture density networks, to resolve this problem. To facilitate empirical validations, we make available a new style controllable dataset called VccmDataset. Our experimental results demonstrate that ControlSpeech exhibits comparable or state-of-the-art (SOTA) performance in terms of controllability, timbre similarity, audio quality, robustness, and generalizability. Codes are available at https://github.com/jishengpeng/ControlSpeech.

Minghao Lv, Siyuan Chen, Haoan Jin, Minghao Yuan, Qianqian Ju, Yujia Peng, Kenny Q. Zhu, Mengyue Wu

Social media platforms possess considerable potential in the realm of exploring mental health. Previous research has indicated that major life events can greatly impact individuals’ mental health. However, due to the complexity and ambiguity nature of life events, shedding its light on social media data is quite challenging. In this paper, we are dedicated to uncovering life events mentioned in posts on social media. We hereby provide a carefully-annotated social media event dataset, PsyEvent, which encompasses 12 major life event categories that are likely to occur in everyday life. This dataset is human-annotated under iterative procedure and boasts a high level of quality. Furthermore, by applying the life events extracted from posts to downstream tasks such as early risk detection of depression and suicide risk prediction, we have observed a considerable improvement in performance. This suggests that extracting life events from social media can be beneficial for the analysis of individuals’ mental health.

Fengran Mo, Yifan Gao, Chuan Meng, Xin Liu, Zhuofeng Wu, Kelong Mao, Zhengyang Wang, Pei Chen, Zheng Li, Xian Li 等

The rapid advancement of conversational search systems revolutionizes how information is accessed by enabling the multi-turn interaction between the user and the system. Existing conversational search systems are usually built with two different models. This separation restricts the system from leveraging the intrinsic knowledge of the models simultaneously, which cannot ensure the effectiveness of retrieval benefiting the generation. The existing studies for developing unified models cannot fully address the aspects of understanding conversational context, managing retrieval independently, and generating responses. In this paper, we explore how to unify dense retrieval and response generation for large language models in conversation. We conduct joint fine-tuning with different objectives and design two mechanisms to reduce the inconsistency risks while mitigating data discrepancy. The evaluations on five conversational search datasets demonstrate that our unified model can mutually improve both tasks and outperform the existing baselines.

Zeyao Ma, Xiaokang Zhang, Jing Zhang, Jifan Yu, Sijia Luo, Jie Tang

Current large language models (LLMs) often struggle to produce accurate responses on the first attempt for complex reasoning tasks like code generation. Prior research tackles this challenge by generating multiple candidate solutions and validating them with LLM-generated unit tests. The execution results of unit tests serve as reward signals to identify correct solutions. As LLMs always confidently make mistakes, these unit tests are not reliable, thereby diminishing the quality of reward signals. Motivated by the observation that scaling the number of solutions improves LLM performance, we explore the impact of scaling unit tests to enhance reward signal quality. Our pioneer experiment reveals a positive correlation between the number of unit tests and reward signal quality, with greater benefits observed in more challenging problems. Based on these insights, we propose CodeRM-8B, a lightweight yet effective unit test generator that enables efficient and high-quality unit test scaling. Additionally, we implement a dynamic scaling mechanism that adapts the number of unit tests based on problem difficulty, further improving efficiency. Experimental results show that our approach significantly improves performance across various models on three benchmarks (e.g., with gains of 18.43 for Llama3-8B and 3.42 for GPT-4o-mini on HumanEval Plus). The parameters of CodeRM-8B and corresponding training data will be available upon publication.

Shojiro Yamabe, Futa Kai Waseda, Tsubasa Takahashi, Koki Wataoka

Protecting the intellectual property of Large Language Models (LLMs) has become increasingly critical due to the high cost of training. Model merging, which integrates multiple expert models into a single multi-task model, introduces a novel risk of unauthorized use of LLMs due to its efficient merging process. While fingerprinting techniques have been proposed for verifying model ownership, their resistance to model merging remains unexplored. To address this gap, we propose a novel fingerprinting method, MergePrint, which embeds robust fingerprints capable of surviving model merging. MergePrint enables black-box ownership verification, where owners only need to check if a model produces target outputs for specific fingerprint inputs, without accessing model weights or intermediate outputs. By optimizing against a pseudo-merged model that simulates merged behavior, MergePrint ensures fingerprints that remain detectable after merging. Additionally, to minimize performance degradation, we pre-optimize the fingerprint inputs. MergePrint pioneers a practical solution for black-box ownership verification, protecting LLMs from misappropriation via merging, while also excelling in resistance to broader model theft threats.

Angelina Wang, Michelle Phan, Daniel E. Ho, Sanmi Koyejo

Algorithmic fairness has conventionally adopted the mathematically convenient perspective of racial color-blindness (i.e., difference unaware treatment). However, we contend that in a range of important settings, group difference awareness matters. For example, differentiating between groups may be necessary in legal contexts (e.g., the U.S. compulsory draft applies to men but not women) and harm assessments (e.g., referring to girls as “terrorists” may be less harmful than referring to Muslim people as such). Thus, in contrast to most fairness work, we study fairness through the perspective of treating people differently — when it is contextually appropriate to. We first introduce an important distinction between descriptive (fact-based), normative (value-based), and correlation (association-based) benchmarks. This distinction is significant because each category requires separate interpretation and mitigation tailored to its specific characteristics. Then, we present a benchmark suite composed of eight different scenarios for a total of 16k questions that enables us to assess difference awareness. Finally, we show results across ten models that demonstrate difference awareness is a distinct dimension to fairness where existing bias mitigation strategies may backfire.

Jiazheng Li, Hanqi Yan, Yulan He

As Large Language Models (LLMs) are increasingly applied to complex reasoning tasks, achieving both accurate task performance and faithful explanations becomes crucial. However, LLMs often generate unfaithful explanations, partly because they do not consistently adhere closely to the provided context. Existing approaches to this problem either rely on superficial calibration methods, such as decomposed Chain-of-Thought prompting, or require costly retraining to improve model faithfulness. In this work, we propose a probabilistic inference paradigm that leverages task-specific and lookahead rewards to ensure that LLM-generated rationales are more faithful to model decisions and align better with input context. These rewards are derived from a domain-specific proposal distribution, allowing for optimized sequential Monte Carlo approximations. Our evaluations across three different reasoning tasks show that this method, which allows for controllable generation during inference, improves both accuracy and faithfulness of LLMs. This method offers a promising path towards making LLMs more reliable for reasoning tasks without sacrificing performance.

Xinyu Tang, Xiaolei Wang, Zhihao Lv, Yingqian Min, Wayne Xin Zhao, Binbin Hu, Ziqi Liu, Zhiqiang Zhang

Recent advancements in long chain-of-thoughts (long CoTs) have significantly improved the reasoning capabilities of large language models (LLMs). Existing work finds that the capability of long CoT reasoning can be efficiently elicited by tuning on only a few examples and can easily transfer to other tasks. This motivates us to investigate whether long CoT reasoning is a general capability for LLMs. In this work, we conduct an empirical analysis for this question from the perspective of representation. We find that LLMs do encode long CoT reasoning as a general capability, with a clear distinction from vanilla CoTs. Furthermore, domain-specific representations are also required for the effective transfer of long CoT reasoning. Inspired by these findings, we propose GLORE, a novel representation engineering method to unleash the general long CoT reasoning capabilities of LLMs. Extensive experiments demonstrate the effectiveness and efficiency of GLORE in both in-domain and cross-domain scenarios. The code is available at https://github.com/txy77/GLoRE.

Xianzhen Luo, Yixuan Wang, Qingfu Zhu, Zhiming Zhang, Xuanyu Zhang, Qing Yang, Dongliang Xu

The rapid growth in the parameters of LLMs has made inference latency a fundamental bottleneck. Speculative decoding represents a lossless approach to accelerate inference through a guess-and-verify paradigm. Some methods rely on additional architectures to guess draft tokens, which need extra training before use. Alternatively, retrieval-based train-free techniques build libraries from pre-existing corpora or by n-gram generation. However, they face challenges like large storage requirements, time-consuming retrieval, and limited adaptability. Observing that candidate tokens generated during the decoding process are likely to reoccur in future sequences, we propose Token Recycling. This approach stores candidate tokens in an adjacency matrix and employs a breadth-first-search (BFS)-like algorithm to construct a draft tree, which is then validated through tree attention. New candidate tokens from the decoding process are then used to update the matrix. Token Recycling requires <2MB of additional storage and achieves approximately 2x speedup across all sizes of LLMs. It significantly outperforms existing train-free methods by 30% and even a training method by 25%.

Dmitry Morozov, Lizaveta Astapenka, Anna Glazkova, Timur Garipov, Olga Lyashevskaya

Automatic morpheme segmentation algorithms are applicable in various tasks, such as building tokenizers and language education. For Slavic languages, the development of such algorithms is complicated by the rich derivational capabilities of these languages. Previous research has shown that, on average, these algorithms have already reached expert-level quality. However, a key unresolved issue is the significant decline in performance when segmenting words containing roots not present in the training data. This problem can be partially addressed by using pre-trained language models to better account for word semantics. In this work, we explored the possibility of fine-tuning BERT-like models for morpheme segmentation using data from Belarusian, Czech, and Russian. We found that for Czech and Russian, our models outperform all previously proposed approaches, achieving word-level accuracy of 92.5-95.1%. For Belarusian, this task was addressed for the first time. The best-performing approach for Belarusian was an ensemble of convolutional neural networks with word-level accuracy of 90.45%.

Liwei Jiang, Taylor Sorensen, Sydney Levine, Yejin Choi

Recent calls for pluralistic alignment emphasize that AI systems should address the diverse needs of all people. Yet, efforts in this space often require sorting people into fixed buckets of pre-specified diversity-defining dimensions (e.g., demographics), risking smoothing out individualistic variations or even stereotyping. To achieve an authentic representation of diversity that respects individuality, we propose individualistic alignment. While individualistic alignment can take various forms, in this paper, we introduce IndieValueCatalog, a dataset transformed from the influential World Values Survey (WVS), to study language models (LMs) on the specific challenge of individualistic value reasoning. Given a sample of an individual’s value-expressing statements, models are tasked with predicting their value judgments in novel cases. With IndieValueCatalog, we reveal critical limitations in frontier LMs’ abilities to predict individualistic values with accuracies only ranging between 55% to 65%. Moreover, our results highlight that a precise description of individualistic values cannot be approximated only via demographic information. Finally, we train a series of IndieValueReasoners to reveal new patterns and dynamics into global human values.

Leane Jourdan, Nicolas Hernandez, Florian Boudin, Richard Dufour

Evaluating text revision in scientific writing remains a challenge, as traditional metrics such as ROUGE and BERTScore primarily focus on similarity rather than capturing meaningful improvements. In this work, we analyse and identify the limitations of these metrics and explore alternative evaluation methods that better align with human judgments. We first conduct a manual annotation study to assess the quality of different revisions. Then, we investigate reference-free evaluation metrics from related NLP domains. Additionally, we examine LLM-as-a-judge approaches, analysing their ability to assess revisions with and without a gold reference. Our results show that LLMs effectively assess instruction-following but struggle with correctness, while domain-specific metrics provide complementary insights. We find that a hybrid approach combining LLM-as-a-judge evaluation and task-specific metrics offers the most reliable assessment of revision.

Sergey Berezin, Reza Farahbakhsh, Noel Crespi

We present a novel class of jailbreak adversarial attacks on LLMs, termed Task-in-Prompt (TIP) attacks. Our approach embeds sequence-to-sequence tasks (e.g., cipher decoding, riddles, code execution) into the model’s prompt to indirectly generate prohibited inputs. To systematically assess the effectiveness of these attacks, we introduce the PHRYGE benchmark. We demonstrate that our techniques successfully circumvent safeguards in six state-of-the-art language models, including GPT-4o and LLaMA 3.2. Our findings highlight critical weaknesses in current LLM safety alignment and underscore the urgent need for more sophisticated defence strategies.

Guizhen Chen, Weiwen Xu, Hao Zhang, Hou Pong Chan, Chaoqun Liu, Lidong Bing, Deli Zhao, Anh Tuan Luu, Yu Rong

Many challenging reasoning tasks require not just rapid, intuitive responses, but a more deliberate, multi-step approach. Recent progress in large language models (LLMs) highlights an important shift from the “System 1” way of quick reactions to the “System 2” style of reflection-and-correction problem solving. However, current benchmarks heavily rely on the final-answer accuracy, leaving much of a model’s intermediate reasoning steps unexamined. This fails to assess the model’s ability to reflect and rectify mistakes within the reasoning process. To bridge this gap, we introduce FINEREASON, a logic-puzzle benchmark for systematic evaluation of LLMs’ reasoning capabilities. Each puzzle can be decomposed into atomic steps, making it ideal for rigorous validation of intermediate correctness. Building on this, we introduce two tasks: state checking and state transition, for a comprehensive evaluation of how models assess the current situation and plan the next move. To support broader research, we also provide a puzzle training set aimed at enhancing general reasoning. We show that models trained on our state checking and transition data demonstrate gains in mathematical reasoning by up to 5.1%.

Max Ku, Cheuk Hei Chong, Jonathan Leung, Krish Shah, Alvin Yu, Wenhu Chen

Understanding domain-specific theorems often requires more than just text-based reasoning; effective communication through structured visual explanations is crucial for deeper comprehension. While large language models (LLMs) demonstrate strong performance in text-based theorem reasoning, their ability to generate coherent and pedagogically meaningful visual explanations remains an open challenge. In this work, we introduce TheoremExplainAgent, an agentic approach for generating long-form theorem explanation videos (over 5 minutes) using Manim animations. To systematically evaluate multimodal theorem explanations, we propose TheoremExplainBench, a benchmark covering 240 theorems across multiple STEM disciplines, along with 5 automated evaluation metrics. Our results reveal that agentic planning is essential for generating detailed long-form videos, and the o3-mini agent achieves a success rate of 93.8% and an overall score of 0.77. However, our quantitative and qualitative studies show that most of the videos produced exhibit minor issues with visual element layout. Furthermore, multimodal explanations expose deeper reasoning flaws that text-based explanations fail to reveal, highlighting the importance of multimodal explanations.

Jun Yin, Pengyu Zeng, Haoyuan Sun, Yuqin Dai, Han Zheng, Miao Zhang, Yachao Zhang, Shuai Lu

Floor plans serve as a graphical language through which architects sketch and communicate their design ideas. Actually, in the Architecture, Engineering, and Construction (AEC) design stages, generating floor plans is a complex task requiring domain expertise and alignment with user requirements. However, existing evaluation methods for floor plan generation rely mainly on statistical metrics like FID, GED, and PSNR, which often fail to evaluate using domain knowledge. As a result, even high-performing models on these metrics struggle to generate viable floor plans in practice. To address this, (1) we propose ArchiMetricsNet, the first floor plan dataset that includes functionality, flow, and overall evaluation scores, along with detailed textual analyses. We trained FloorPlan-MPS (Multi-dimensional Preference Score) on it. (2) We develope FloorPlan-LLaMa, a floor plan generation model based on autoregressive framework. To integrate architects’ professional expertise and preferences, FloorPlan-MPS serves as the reward model during the RLHF (Reinforcement Learning from Human Feedback) process, aligning FP-LLaMa with the needs of the architectural community. (3) Comparative experiments demonstrate that our method outperforms baseline models in both text-conditional and class-conditional tasks. Validation by professional architects confirms that our approach yields more rational plans and aligns better with human preferences.

Soda Marem Lo, Oscar Araque, Rajesh Sharma, Marco Antonio Stranisci

Canceling is a morally-driven phenomenon that hinders the development of safe social media platforms and contributes to ideological polarization. To address this issue we present the Canceling Attitudes Detection (CADE) dataset, an annotated corpus of canceling incidents aimed at exploring the factors of disagreements in evaluating people’s canceling attitudes on social media. Specifically, we study the impact of annotators’ morality in their perception of canceling, showing that morality is an independent axis for the explanation of disagreement on this phenomenon. Annotator’s judgments heavily depend on the type of controversial events and involved celebrities. This shows the need to develop more event-centric datasets to better understand how harms are perpetrated in social media and to develop more aware technologies for their detection.

Santosh T.Y.S.S, Tuan-Quang Vuong

The rapid evolution of legal concepts over time necessitates that legal language models adapt swiftly accounting for the temporal dynamics. However, prior works have largely neglected this crucial dimension, treating legal adaptation as a static problem rather than a continuous process. To address this gap, we pioneer LexTempus, a dynamic mixture of experts model that explicitly models the temporal evolution of legal language in a parameter-efficient online learning framework. LexTempus starts with a single lightweight adapter expert and dynamically expands by adding new experts as significant deviations in the data distribution are detected. This self-expansion strategy allows LexTempus to adapt to new information without forgetting past knowledge, thereby improving temporal generalization. We use a a non-parametric similarity-based router to merge relevant experts into a unified expert for each test instance, ensuring efficient inference without additional overhead. We validate the effectiveness of LexTempus on ECHR and EU case law datasets, demonstrating its superiority in both perplexity and open-ended text generation quality metrics.

Jillian Fisher, Shangbin Feng, Robert Aron, Thomas Richardson, Yejin Choi, Daniel W Fisher, Jennifer Pan, Yulia Tsvetkov, Katharina Reinecke

As modern large language models (LLMs) become integral to everyday tasks, concerns about their inherent biases and their potential impact on human decision-making have emerged. While bias in models are well-documented, less is known about how these biases influence human decisions. This paper presents two interactive experiments investigating the effects of partisan bias in LLMs on political opinions and decision-making. Participants interacted freely with either a biased liberal, biased conservative, or unbiased control model while completing these tasks. We found that participants exposed to partisan biased models were significantly more likely to adopt opinions and make decisions which matched the LLM’s bias. Even more surprising, this influence was seen when the model bias and personal political partisanship of the participant were opposite. However, we also discovered that prior knowledge of AI was weakly correlated with a reduction of the impact of the bias, highlighting the possible importance of AI education for robust mitigation of bias effects. Our findings not only highlight the critical effects of interacting with biased LLMs and its ability to impact public discourse and political conduct, but also highlights potential techniques for mitigating these risks in the future.

Yuhang Wu, Wenmeng Yu, Yean Cheng, Yan Wang, Xiaohan Zhang, Jiazheng Xu, Ming Ding, Yuxiao Dong

Evaluating the alignment capabilities of large Vision-Language Models (VLMs) is essential for determining their effectiveness as helpful assistants. However, existing benchmarks primarily focus on basic abilities using nonverbal methods, such as yes-no and multiple-choice questions. In this paper, we address this gap by introducing AlignMMBench, which provides more nuanced evaluations of alignment capabilities and is the first benchmark specifically designed for Chinese visual contexts. This benchmark is meticulously curated from real-world scenarios and internet sources, encompassing thirteen specific tasks across three categories, and includes both single-turn and multi-turn dialogue scenarios. Incorporating a prompt rewrite strategy, AlignMMBench encompasses 1,054 images and 4,978 question-answer pairs. To facilitate the evaluation pipeline, we develop CritiqueVLM, a rule-calibrated evaluator that exceeds GPT-4’s evaluation ability. Additionally, we measure the “alignment score”, a quantitative metric designed to assess the robustness and stability of models across diverse prompts. Finally, we evaluate the performance of representative VLMs on AlignMMBench, offering insights into the capabilities and limitations of different VLM architectures. The evaluation code and data are available at https://github.com/THUDM/AlignMMBench.