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Qiuchi Li

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12 papers

2 author rows

TCS Journal 2026 Journal Article

Quantum-inspired fuzzy matching network with morphology-enhanced word embeddings

Chenchen Zhang
Qiuchi Li
Dawei Song
Prayag Tiwari

Quantum Natural Language Processing (QNLP) has demonstrated significant advantages in addressing the uncertainty and vagueness in language understanding, with many related works relying on quantum-inspired neural networks. However, most existing quantum-inspired neural networks are based on word-level embeddings, which fail to effectively capture the complexity of language and simulate real human cognitive processes. Recognizing that the introduction of morphological information can inject prior semantic and syntactic knowledge, thereby enhancing the quality of the embeddings, and that the incorporation of fuzzy logic can further alleviate the inherent vagueness in language, we propose a quantum-inspired fuzzy matching network with morphology-enhanced word embeddings (QFNMWE). Our proposed model leverages morpheme-level and word-level embeddings to learn a richer multilevel semantic representation, followed by a fuzzy fusion and fuzzy measurement. Experimental results on various benchmarking datasets demonstrate that our QFNMWE outperforms a wide range of state-of-the-art baselines in different downstream tasks.

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AAAI Conference 2025 Conference Paper

Is Sarcasm Detection a Step-by-Step Reasoning Process in Large Language Models?

Ben Yao
Yazhou Zhang
Qiuchi Li
Jing Qin

Elaborating a series of intermediate reasoning steps significantly improves the ability of large language models (LLMs) to solve complex problems, as such steps would evoke LLMs to think sequentially. However, human sarcasm understanding is often considered an intuitive and holistic cognitive process, in which various linguistic, contextual, and emotional cues are integrated to form a comprehensive understanding, in a way that does not necessarily follow a step-by-step fashion. To verify the validity of this argument, we introduce a new prompting framework (called SarcasmCue) containing four sub-methods, viz. chain of contradiction (CoC), graph of cues (GoC), bagging of cues (BoC) and tensor of cues (ToC), which elicits LLMs to detect human sarcasm by considering sequential and non-sequential prompting methods. Through a comprehensive empirical comparison on four benchmarks, we highlight three key findings: (1) CoC and GoC show superior performance with more advanced models like GPT-4 and Claude 3.5, with an improvement of 3.5%. (2) ToC significantly outperforms other methods when smaller LLMs are evaluated, boosting the F1 score by 29.7% over the best baseline. (3) Our proposed framework consistently pushes the state-of-the-art (i.e., ToT) by 4.2%, 2.0%, 29.7%, and 58.2% in F1 scores across four datasets. This demonstrates the effectiveness and stability of the proposed framework.

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ICLR Conference 2025 Conference Paper

Learning LLM-as-a-Judge for Preference Alignment

Ziyi Ye
Xiangsheng Li
Qiuchi Li
Qingyao Ai
Yujia Zhou 0002
Wei Shen
Dong Yan
Yiqun Liu 0001

Learning from preference feedback is a common practice for aligning large language models (LLMs) with human value. Conventionally, preference data is learned and encoded into a scalar reward model that connects a value head with an LLM to produce a scalar score as preference. However, scalar models lack interpretability and are known to be susceptible to biases in datasets. This paper investigates leveraging LLM itself to learn from such preference data and serve as a judge to address both limitations in one shot. Specifically, we prompt the pre-trained LLM to generate initial judgment pairs with contrastive preference in natural language form. The self-generated contrastive judgment pairs are used to train the LLM-as-a-Judge with Direct Preference Optimization (DPO) and incentivize its reasoning capability as a judge. This proposal of learning the LLMas-a-Judge using self-generated Contrastive judgments (Con-J) ensures natural interpretability through the generated rationales supporting the judgments, and demonstrates higher robustness against bias compared to scalar models. Experimental results show that Con-J outperforms the scalar reward model trained on the same collection of preference data, and outperforms a series of open-source and closed-source generative LLMs. We open-source the training process and model weights of Con-J at https://github.com/YeZiyi1998/Con-J.

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EAAI Journal 2025 Journal Article

Quantum-inspired semantic matching based on neural networks with the duality of density matrices

Chenchen Zhang
Qiuchi Li
Dawei Song
Prayag Tiwari

Social media text can be semantically matched in different ways, viz paraphrase identification, answer selection, community question answering, and so on. The performance of the above semantic matching tasks depends largely on the ability of language modeling. Neural network based language models and probabilistic language models are two main streams of language modeling approaches. However, few prior work has managed to unify them in a single framework on the premise of preserving probabilistic features during the neural network learning process. Motivated by recent advances of quantum-inspired neural networks for text representation learning, we fill the gap by resorting to density matrices, a key concept describing a quantum state as well as a quantum probability distribution. The state and probability views of density matrices are mapped respectively to the neural and probabilistic aspects of language models. Concretizing this state-probability duality to the semantic matching task, we build a unified neural-probabilistic language model through a quantum-inspired neural network. Specifically, we take the state view to construct a density matrix representation of sentence, and exploit its probabilistic nature by extracting its main semantics, which form the basis of a legitimate quantum measurement. When matching two sentences, each sentence is measured against the main semantics of the other. Such a process is implemented in a neural structure, facilitating an end-to-end learning of parameters. The learned density matrix representation reflects an authentic probability distribution over the semantic space throughout the training process. Experiments show that our model significantly outperforms a wide range of prominent classical and quantum-inspired baselines.

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TMLR Journal 2024 Journal Article

Mixture of Latent Experts Using Tensor Products

Zhan Su
Fengran Mo
Prayag Tiwari
Benyou Wang
Qiuchi Li
Jian-Yun Nie
Jakob Grue Simonsen

In multi-task learning, the conventional approach involves training a model on multiple tasks simultaneously. However, the training signals from different tasks can interfere with one another, potentially leading to \textit{negative transfer}. To mitigate this, we propose a novel \textit{latent-expert} approach (\texttt{TensorPoly}), that balances parameter efficiency with nuanced routing methods. For \textit{experts}, we reparameterize Low-Rank Adaptation (\texttt{LoRA}) by employing an entangled tensor through the use of tensor product operations and name the resulting approach \texttt{TLoRA}. For \textit{routing function}, we tailor two innovative routing functions according to the granularity: \texttt{TensorPoly-I} which directs to each rank within the entangled tensor while \texttt{TensorPoly-II} offers a finer-grained routing approach targeting each order of the entangled tensor. The experimental results from the multi-task T0-benchmark demonstrate that: 1) all latent-expert approaches surpass the corresponding dense approaches, highlighting the potential of modular language models to mitigate negative inference in multi-task learning and deliver superior outcomes. 2) \texttt{TensorPoly-I} achieves higher parameter efficiency in adaptation and outperforms other modular LMs, which shows the potential of our approach in multi-task transfer learning \footnote{The code is released: \url{https://github.com/microsoft/mttl}}.

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NeurIPS Conference 2023 Conference Paper

CMMA: Benchmarking Multi-Affection Detection in Chinese Multi-Modal Conversations

Yazhou Zhang
Yang Yu
Qing Guo
Benyou Wang
Dongming Zhao
Sagar Uprety
Dawei Song
Qiuchi Li

Human communication has a multi-modal and multi-affection nature. The inter-relatedness of different emotions and sentiments poses a challenge to jointly detect multiple human affections with multi-modal clues. Recent advances in this field employed multi-task learning paradigms to render the inter-relatedness across tasks, but the scarcity of publicly available resources sets a limit to the potential of works. To fill this gap, we build the first Chinese Multi-modal Multi-Affection conversation (CMMA) dataset, which contains 3, 000 multi-party conversations and 21, 795 multi-modal utterances collected from various styles of TV-series. CMMA contains a wide variety of affection labels, including sentiment, emotion, sarcasm and humor, as well as the novel inter-correlations values between certain pairs of tasks. Moreover, it provides the topic and speaker information in conversations, which promotes better modeling of conversational context. On the dataset, we empirically analyze the influence of different data modalities and conversational contexts on different affection analysis tasks, and exhibit the practical benefit of inter-task correlations. The full dataset will be publicly available for research\footnote{https: //github. com/annoymity2022/Chinese-Dataset}

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IJCAI Conference 2021 Conference Paper

An Entanglement-driven Fusion Neural Network for Video Sentiment Analysis

Dimitris Gkoumas
Qiuchi Li
Yijun Yu
Dawei Song

Video data is multimodal in its nature, where an utterance can involve linguistic, visual and acoustic information. Therefore, a key challenge for video sentiment analysis is how to combine different modalities for sentiment recognition effectively. The latest neural network approaches achieve state-of-the-art performance, but they neglect to a large degree of how humans understand and reason about sentiment states. By contrast, recent advances in quantum probabilistic neural models have achieved comparable performance to the state-of-the-art, yet with better transparency and increased level of interpretability. However, the existing quantum-inspired models treat quantum states as either a classical mixture or as a separable tensor product across modalities, without triggering their interactions in a way that they are correlated or non-separable (i. e. , entangled). This means that the current models have not fully exploited the expressive power of quantum probabilities. To fill this gap, we propose a transparent quantum probabilistic neural model. The model induces different modalities to interact in such a way that they may not be separable, encoding crossmodal information in the form of non-classical correlations. Comprehensive evaluation on two benchmarking datasets for video sentiment analysis shows that the model achieves significant performance improvement. We also show that the degree of non-separability between modalities optimizes the post-hoc interpretability.

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AAAI Conference 2021 Conference Paper

Quantum Cognitively Motivated Decision Fusion for Video Sentiment Analysis

Dimitris Gkoumas
Qiuchi Li
Shahram Dehdashti
Massimo Melucci
Yijun Yu
Dawei Song

Video sentiment analysis as a decision-making process is inherently complex, involving the fusion of decisions from multiple modalities and the so-caused cognitive biases. Inspired by recent advances in quantum cognition, we show that the sentiment judgment from one modality could be incompatible with the judgment from another, i. e. , the order matters and they cannot be jointly measured to produce a final decision. Thus the cognitive process exhibits “quantum-like” biases that cannot be captured by classical probability theories. Accordingly, we propose a fundamentally new, quantum cognitively motivated fusion strategy for predicting sentiment judgments. In particular, we formulate utterances as quantum superposition states of positive and negative sentiment judgments, and uni-modal classifiers as mutually incompatible observables, on a complex-valued Hilbert space with positive-operator valued measures. Experiments on two benchmarking datasets illustrate that our model significantly outperforms various existing decision level and a range of state-of-the-art content-level fusion approaches. The results also show that the concept of incompatibility allows effective handling of all combination patterns, including those extreme cases that are wrongly predicted by all uni-modal classifiers.

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AAAI Conference 2021 Conference Paper

Quantum-inspired Neural Network for Conversational Emotion Recognition

Qiuchi Li
Dimitris Gkoumas
Alessandro Sordoni
Jian-Yun Nie
Massimo Melucci

We provide a novel perspective on conversational emotion recognition by drawing an analogy between the task and a complete span of quantum measurement. We characterize different steps of quantum measurement in the process of recognizing speakers’ emotions in conversation, and stitch them up with a quantum-like neural network. The quantum-like layers are implemented by complex-valued operations to ensure an authentic adoption of quantum concepts, which naturally enables conversational context modeling and multimodal fusion. We borrow an existing algorithm to learn the complexvalued network weights, so that the quantum-like procedure is conducted in a data-driven manner. Our model is comparable to state-of-the-art approaches on two benchmarking datasets, and provide a quantum view to understand conversational emotion recognition.

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ECAI Conference 2020 Conference Paper

Assessing the Memory Ability of Recurrent Neural Networks

Cheng Zhang 0019
Qiuchi Li
Lingyu Hua
Dawei Song 0001

It is known that Recurrent Neural Networks (RNNs) can remember, in their hidden layers, part of the semantic information expressed by a sequence (e. g. , a sentence) that is being processed. Different types of recurrent units have been designed to enable RNNs to remember information over longer time spans. However, the memory abilities of different recurrent units are still theoretically and empirically unclear, thus limiting the development of more effective and explainable RNNs. To tackle the problem, in this paper, we identify and analyze the internal and external factors that affect the memory ability of RNNs, and propose a Semantic Euclidean Space to represent the semantics expressed by a sequence. Based on the Semantic Euclidean Space, a series of evaluation indicators are defined to measure the memory abilities of different recurrent units and analyze their limitations (Code is available at https: //github. com/chzhang/Assessing_the_Memory_Ability_of_RNNs). These evaluation indicators also provide a useful guidance to select suitable sequence lengths for different RNNs during training.

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ICLR Conference 2020 Conference Paper

Encoding word order in complex embeddings

Benyou Wang
Donghao Zhao
Christina Lioma
Qiuchi Li
Peng Zhang 0002
Jakob Grue Simonsen

Sequential word order is important when processing text. Currently, neural networks (NNs) address this by modeling word position using position embeddings. The problem is that position embeddings capture the position of individual words, but not the ordered relationship (e.g., adjacency or precedence) between individual word positions. We present a novel and principled solution for modeling both the global absolute positions of words and their order relationships. Our solution generalizes word embeddings, previously defined as independent vectors, to continuous word functions over a variable (position). The benefit of continuous functions over variable positions is that word representations shift smoothly with increasing positions. Hence, word representations in different positions can correlate with each other in a continuous function. The general solution of these functions can be extended to complex-valued variants. We extend CNN, RNN and Transformer NNs to complex-valued versions to incorporate our complex embedding (we make all code available). Experiments on text classification, machine translation and language modeling show gains over both classical word embeddings and position-enriched word embeddings. To our knowledge, this is the first work in NLP to link imaginary numbers in complex-valued representations to concrete meanings (i.e., word order).

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IJCAI Conference 2019 Conference Paper

Quantum-Inspired Interactive Networks for Conversational Sentiment Analysis

Yazhou Zhang
Qiuchi Li
Dawei Song
Peng Zhang
Panpan Wang

Conversational sentiment analysis is an emerging, yet challenging Artificial Intelligence (AI) subtask. It aims to discover the affective state of each participant in a conversation. There exists a wealth of interaction information that affects the sentiment of speakers. However, the existing sentiment analysis approaches are insufficient in dealing with this task due to ignoring the interactions and dependency relationships between utterances. In this paper, we aim to address this issue by modeling intrautterance and inter-utterance interaction dynamics. We propose an approach called quantum-inspired interactive networks (QIN), which leverages the mathematical formalism of quantum theory (QT) and the long short term memory (LSTM) network, to learn such interaction dynamics. Specifically, a density matrix based convolutional neural network (DM-CNN) is proposed to capture the interactions within each utterance (i. e. , the correlations between words), and a strong-weak influence model inspired by quantum measurement theory is developed to learn the interactions between adjacent utterances (i. e. , how one speaker influences another). Extensive experiments are conducted on the MELD and IEMOCAP datasets. The experimental results demonstrate the effectiveness of the QIN model.

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