Jiayu Liu, Cheng Qian, Zhaochen Su, Qing Zong, Shijue Huang, Bingxiang He, Yi R. Fung. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.

Self-learning of Large Language Models (LLMs) facilitates their advancement towards super-intelligence by training with self-synthesized experiences. However, a critical challenge is the amplification of hallucinations in generated data during iterative self-learning, underscoring the need for reliable data selection. To address this, we investigate the mechanism of Inner Knowledge Explicitation, which involves explicitly extracting the inner knowledge from memory of LLMs, to concurrently improves reasoning, and enables reliable self-learning data selection. This paper introduces a Self Knowledge Explicitation Learning (SKE-Learn) framework, which equips the LLMs with meta-skills to explicitly extract, verify and utilize inner knowledge for reasoning. By leveraging these meta-skills, SKE-Learn establishes a self-learning approach that ensures reliable selection of self-synthetic data. This approach enhances performance through iterative self-learning while mitigating the problem of hallucinations. Empirical results from six benchmarks demonstrate that Inner Knowledge Explicitation improves reasoning by serving as a more effective prompting method. Additionally, SKE-Learn, based on the verifiability of explicit knowledge, shows consistent performance improvements over multiple self-training iterations, with an average performance increase from 52.79% to 56.54% across all benchmarks. Furthermore, Inner Knowledge Explicitation provides explanation and intervention space during LLM's generation process.

Slot filling and intent detection are two highly correlated tasks in spoken language understanding (SLU). Recent SLU research attempts to explore zero-shot prompting techniques in large language models to alleviate the data scarcity problem. Nevertheless, the existing prompting work ignores the cross-task interaction information for SLU, which leads to sub-optimal performance. To solve this problem, we present the pioneering work of Cross-task Interactive Prompting (CroPrompt) for SLU, which enables the model to interactively leverage the information exchange across the correlated tasks in SLU. Additionally, we further introduce a multi-task self-consistency mechanism to mitigate the error propagation caused by the intent information injection. We conduct extensive experiments on the standard SLU benchmark and the results reveal that CroPrompt consistently outperforms the existing prompting approaches. In addition, the multi-task self-consistency mechanism can effectively ease the error propagation issue, thereby enhancing the performance. We hope this work can inspire more research on cross-task prompting for SLU.

Multi-modal intent detection aims to utilize various modalities to understand the user’s intentions, which is essential for the deployment of dialogue systems in real-world scenarios. The two core challenges for multi-modal intent detection are (1) how to effectively align and fuse different features of modalities and (2) the limited labeled multi-modal intent training data. In this work, we introduce a shallow-to-deep interaction framework with data augmentation (SDIF-DA) to address the above challenges. Firstly, SDIF-DA leverages a shallow-to-deep interaction module to progressively and effectively align and fuse features across text, video, and audio modalities. Secondly, we propose a ChatGPT-based data augmentation approach to automatically augment sufficient training data. Experimental results demonstrate that SDIF-DA can effectively align and fuse multi-modal features by achieving state-of-the-art performance. In addition, extensive analyses show that the introduced data augmentation approach can successfully distill knowledge from the large language model.

Fine-grained meme understanding aims to explore and comprehend the meanings of memes from multiple perspectives by performing various tasks, such as sentiment analysis, intention detection, and offensiveness detection. Existing approaches primarily focus on simple multi-modality fusion and individual task analysis. However, there remain several limitations that need to be addressed: (1) the neglect of incongruous features within and across modalities, and (2) the lack of consideration for correlations among different tasks. To this end, we leverage metaphorical information as text modality and propose a Metaphor-aware Multi-modal Multi-task Framework (M3F) for fine-grained meme understanding. Specifically, we create inter-modality attention enlightened by the Transformer to capture inter-modality interaction between text and image. Moreover, intra-modality attention is applied to model the contradiction between the text and metaphorical information. To learn the implicit interaction among different tasks, we introduce a multi-interactive decoder that exploits gating networks to establish the relationship between various subtasks. Experimental results on the MET-Meme dataset show that the proposed framework outperforms the state-of-the-art baselines in fine-grained meme understanding.

Chain-of-thought (CoT) is capable of eliciting models to explicitly generate reasoning paths, thus promoting reasoning accuracy and attracting increasing attention.Specifically, zeroshot CoT achieves remarkable improvements in a wide range of reasoning tasks by simply instructing the LLM with the prompt "Let's think step by step!".Despite the success of zero-shot CoT, the existing zero-shot prompting techniques remain limited to a single language, making it challenging to generalize to other languages and hindering global development.In this work, we introduce cross-lingual prompting (CLP), aiming to improve zero-shot CoT reasoning across languages.Specifically, CLP consists of two main components: (1) cross-lingual alignment prompting and (2) taskspecific solver prompting.The cross-lingual alignment prompting is responsible for aligning representations across different languages, whereas the task-specific solver prompting is used to generate the final chain of thoughts and results for the reasoning task.In addition, we further introduce cross-lingual self-consistent prompting (CLSP) to ensemble different reasoning paths across languages.Our experimental evaluations on several benchmarks demonstrate that CLP and CLSP significantly outperform the existing prompting methods and achieve state-of-the-art performance.We hope this work will inspire further breakthroughs in cross-lingual CoT.Answer: 48.After understanding, you should act as an expert in arithmetic reasoning in German… Verstanden! Hier ist die Lösung für die gegebene Aufgabe: 1. Da Jiesha 20 Jahre alt ist, ist Qioung 20-5... 2. Da Mary zwei Jahre jünger als Qioung ist... Antwort: 48.Sorry, I am an AI language model and I can only communicate in English.May I assist you with anything else?Let's understand the task in German step-by-step!German Prompting Please act as an expert in multi-lingual understanding in Chinese.Request:玛丽比琼小两岁，琼比杰莎大五岁。如果杰莎 20 岁，这 三个女孩的年龄总和是多少？ Let's understand the task in English step-by-step!After understanding, you should act as an expert in arithmetic reasoning in English.Let's resolve the task you understand above step-by-step!Finally, you should format your answer as 'Answer: [num]'.Sure! Let's solve the task step-by-step: 1.According to the first sentence, Mary is two years younger than Joan.Let's suppose Joan's age is J, then Mary's age is J -2. 2. According to the second sentence, Joan is five years older than Jessie.If Jessie's age is x, then Joan's age is x + 5... Answer: 68.

Consistency identification in task-oriented dialog (CI-ToD) usually consists of three subtasks, aiming to identify inconsistency between current system response and current user response, dialog history and the corresponding knowledge base. This work aims to solve CI-ToD task by introducing an explicit interaction paradigm, Cycle Guided Interactive learning Model (CGIM), which achieves to make information exchange explicitly from all the three tasks. Specifically, CGIM relies on two core insights, referred to as guided multi-head attention module and cycle interactive mechanism, that collaborate from each other. On the one hand, each two tasks are linked with the guided multi-head attention module, aiming to explicitly model the interaction across two related tasks. On the other hand, we further introduce cycle interactive mechanism that focuses on facilitating model to exchange information among the three correlated sub-tasks via a cycle interaction manner. Experimental results on CI-ToD benchmark show that our model achieves the state-of-the-art performance, pushing the overall score to 56.3% (5.0% point absolute improvement). In addition, we find that CGIM is robust to the initial task flow order.

Consistency Identification has obtained remarkable success on open-domain dialogue, which can be used for preventing inconsistent response generation.However, in contrast to the rapid development in open-domain dialogue, few efforts have been made to the task-oriented dialogue direction.In this paper, we argue that consistency problem is more urgent in task-oriented domain.To facilitate the research, we introduce CI-ToD, a novel dataset for Consistency Identification in Taskoriented Dialog system.In addition, we not only annotate the single label to enable the model to judge whether the system response is contradictory, but also provide more finegrained labels (i.e., Dialogue History Inconsistency, User Query Inconsistency and Knowledge Base Inconsistency) to encourage model to know what inconsistent sources lead to it.Empirical results show that state-of-the-art methods only achieve 51.3%, which is far behind the human performance of 93.2%, indicating that there is ample room for improving consistency identification ability.Finally, we conduct exhaustive experiments and qualitative analysis to comprehend key challenges and provide guidance for future directions.All datasets and models are publicly available at https://github.com/yizhen20133868/CI-ToD. * Email corresponding. User: Give me directions to the closest grocery store.System: There is a whole foods 2 miles away and their address is 880_ames_ct.User: I need a route that avoids all heavy traffic.System: mandarin roots is located at 271 springer street.