Papers by Hongliang Li
Denoising Enhanced Distantly Supervised Ultrafine Entity Typing (2023.findings-acl)
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| Challenge: | Recent work on distantly supervised (DS) ultra-fine entity typing has received significant attention . however, DS data is noisy and often suffers from missing or wrong labeling issues resulting in low precision and low recall. |
| Approach: | They propose a noise model to estimate unknown labeling noise distribution over input contexts and noisy type labels and a model to train on denoised data. |
| Outcome: | The proposed model outperforms baseline methods on the Ultra-Fine entity typing dataset and OntoNotes dataset. |
Generating Diverse Training Samples for Relation Extraction with Large Language Models (2025.acl-long)
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| Challenge: | Existing models for Relation Extraction (RE) have good results on many benchmarks, but data scarcity is a common problem. |
| Approach: | They propose to use Large Language Models to generate training data for Relation Extraction . they propose to make LLMs produce dissimilar samples by direct instruction . |
| Outcome: | The proposed approach improves the diversity of training samples generated with LLMs while maintaining correctness. |
Multi-Source Multi-Type Knowledge Exploration and Exploitation for Dialogue Generation (2023.emnlp-main)
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| Challenge: | Existing models focus on identifying specific types of dialogue knowledge and utilizing corresponding datasets for training, but lack generalization capabilities and computational resources. |
| Approach: | They propose a framework that explores multi-source multi-type knowledge from LLMs by leveraging diverse datasets and exploits it for response generation. |
| Outcome: | The proposed framework exploits multi-source multi-type knowledge from LLMs to generate coherent, informative, and fluent responses. |
Leveraging Label Semantics and Entity Description Generation for LLM-based Fine-grained Entity Typing (2026.findings-acl)
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| Challenge: | Fine-grained entity typing (FET) aims to assign semantically rich and contextually appropriate types to entity mentions. |
| Approach: | They propose a descriptor-based retrieval-augmented framework that reduces effective label space . they propose to use natural language descriptores as an intermediate semantic representation . |
| Outcome: | The proposed framework outperforms existing methods under noisy supervision. |
A Deep Decomposable Model for Disentangling Syntax and Semantics in Sentence Representation (2021.findings-emnlp)
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| Challenge: | Recent advances in disentanglement work on coarse levels in the disenanglement of closely related properties, such as syntax and semantics in human languages. |
| Approach: | They propose a deep decomposable model based on VAE to disentangle syntax and semantics by using total correlation penalties on KL divergences. |
| Outcome: | The proposed model significantly improves the disentanglement quality between syntactic and semantic representations for semantic similarity tasks and syntaktic similarity task. |
Multi-Stage LLM Fine-Tuning with a Continual Learning Setting (2025.findings-naacl)
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Changhao Guan, Chao Huang, Hongliang Li, You Li, Ning Cheng, Zihe Liu, Yufeng Chen, Jinan Xu, Jian Liu
| Challenge: | Large language models (LLMs) have made significant progress in knowledge-intensive applications, but they may face a multi-stage continuous learning scenario. |
| Approach: | They propose a multi-stage continuous learning paradigm that includes a preference-based learning bias to identify potential knowledge conflicts and a self-distillation-based data augmentation strategy to expand and enrich the training corpus. |
| Outcome: | The proposed learning paradigm achieves a significant improvement in accuracy after 7 stages of fine-tuning compared to previous methods while preserving general knowledge. |
Understanding Client Reactions in Online Mental Health Counseling (2023.acl-long)
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| Challenge: | Communication success relies heavily on reading participants’ reactions, but little research is on how listeners' reactions shape trajectories and outcomes of conversations. |
| Approach: | They propose to use client reactions to predict counseling outcomes by using an annotation framework that encompasses counselors’ strategies and client reaction behaviors. |
| Outcome: | The proposed framework can predict counselors' strategies and client reaction behaviors against a large-scale text-based counseling dataset. |
CRISP: Compressing Redundancy in Chain-of-Thought via Intrinsic Saliency Pruning (2026.findings-acl)
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| Challenge: | Existing approaches to compress CoT with external compressors fail to align with the model’s internal reasoning dynamics, resulting in the loss of critical logical steps. |
| Approach: | They propose a framework that exploits the model’s intrinsic saliency to compress CoT by exploiting its reasoning termination token . |
| Outcome: | The proposed framework reduces redundancy in reasoning chain by exploiting the model’s intrinsic saliency. |
A Chinese Corpus for Fine-grained Entity Typing (2020.lrec-1)
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| Challenge: | Existing datasets for fine-grained entity typing are limited to English . a corpus of 4,800 mentions is manually labeled with free-form entity types . |
| Approach: | They propose a Chinese fine-grained entity typing task that uses crowdsourcing . they categorize each mention into 10 general types and use a large tag set to predict open set of types . |
| Outcome: | The proposed dataset contains 4,800 mentions manually labeled in Chinese . it also categorizes all the fine-grained types into 10 general types . |
Language-Coupled Reinforcement Learning for Multilingual Retrieval-Augmented Generation (2026.findings-acl)
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Rui Qi, Fengran Mo, Yufeng Chen, Xue Zhang, Shuo Wang, Hongliang Li, Xu Jinan, Meng Jiang, Jian-Yun Nie, Kaiyu Huang
| Challenge: | Existing approaches to multilingual retrieval-augmented generation (MRAG) use a single-turn retrieval and subsequent optimization to acquire and integrate beneficial external knowledge from multilingual collections. |
| Approach: | They propose a multilingual search-augmented reinforcement learning framework that integrates a language-coupled Group Relative Policy Optimization into the policy and reward models. |
| Outcome: | The proposed framework achieves competitive performance and is appropriate for various practical scenarios such as constrained training data and retrieval over collections encompassing a large number of languages. |
End-to-end Deep Reinforcement Learning Based Coreference Resolution (P19-1)
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| Challenge: | Recent neural network models for coreference resolution are usually trained with heuristic loss functions that are computed over a sequence of local decisions. |
| Approach: | They propose an end-to-end reinforcement learning based coreference resolution model to directly optimize coreference evaluation metrics. |
| Outcome: | The proposed model achieves new state-of-the-art performance on the English OntoNotes v5.0 benchmark. |
Improving Fine-grained Entity Typing with Entity Linking (D19-1)
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| Challenge: | Existing methods for fine-grained entity typing require a large tag set and knowledge of the context. |
| Approach: | They propose a deep neural model that uses context and information from entity linking to improve fine-grained entity typing. |
| Outcome: | The proposed model achieves 5% absolute strict accuracy improvement over the state of the art on two datasets. |
Novel Slot Detection With an Incremental Setting (2023.findings-emnlp)
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| Challenge: | Current dialogue systems face diverse user requests and rapid change domains, making quickly adapt to scenarios with previous unseen slot types becomes a major challenge. |
| Approach: | They propose an incremental novel slot detection task which separates the dialogue system to deal with novel types as two major phrases: 1) model discovers unknown slots; 2) training model to possess the capability to handle new classes. |
| Outcome: | The proposed approach overcomes catastrophic forgetting during the process of INSD and is highly effective. |
PromptGen: Automatically Generate Prompts using Generative Models (2022.findings-naacl)
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| Challenge: | Recent prompt learning has received significant attention, where downstream tasks are reformulated to the mask-filling task with the help of a textual prompt. |
| Approach: | They propose a model PromptGen which can automatically generate prompts conditional on the input sentence. |
| Outcome: | The proposed model outperforms baseline models on the knowledge probing LAMA benchmark. |
Cross-lingual Cross-modal Pretraining for Multimodal Retrieval (2021.naacl-main)
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| Challenge: | Recent pretrained vision-language models have achieved impressive performance on cross-modal retrieval tasks in English. |
| Approach: | They propose a new approach to learn cross-lingual cross-modal representations for matching images and captions in multiple languages using an annotated corpus. |
| Outcome: | The proposed model achieves impressive performance on two multimodal multilingual image caption benchmarks: Multi30k with German captions and MSCOCO with Japanese captions. |
Multilingual Collaborative Defense for Large Language Models (2025.findings-emnlp)
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| Challenge: | Existing safeguards for Large Language Models are vulnerable to "jailbreaking" harmful queries. |
| Approach: | They propose a learning method that optimizes a continuous soft safety prompt automatically to facilitate multilingual safeguarding of LLMs. |
| Outcome: | The proposed method outperforms previous approaches in multilingual jailbreak defense while exhibiting strong cross-lingual generalization. |
DUD: Decoupled Update Dynamics for Reliable Uncertainty Quantification in Large Language Models (2026.acl-long)
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| Challenge: | Accurate Uncertainty Quantification (UQ) is critical for reliable deployment of Large Language Models (LLMs). |
| Approach: | They propose a framework that explicitly decouples FFN and Attention contributions via noise-induced causal interventions to capture model's internal fragility. |
| Outcome: | The proposed framework outperforms state-of-the-art baselines in both uncertainty estimation and calibration while exhibiting superior cross-dataset generalization. |
M-BRe: Discovering Training Samples for Relation Extraction from Unlabeled Texts with Large Language Models (2025.emnlp-main)
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| Challenge: | Existing methods to extract training instances from unlabeled texts are expensive . sentences that contain the target relations in texts can be scarce and difficult to find . |
| Approach: | They propose a framework that can automatically extract training instances from unlabeled texts for RE. |
| Outcome: | The proposed method can extract training instances from unlabeled texts for RE. |
Cross-Lingual Unsupervised Sentiment Classification with Multi-View Transfer Learning (2020.acl-main)
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| Challenge: | Recent neural network models have achieved impressive performance on sentiment classification in English and other languages. |
| Approach: | They propose an unsupervised sentiment classification model that leverages an uncontrolled machine translation system and a language discriminator to learn a shared representation. |
| Outcome: | The proposed model outperforms other models on five language pairs. |
RedundancyLens: Revealing and Exploiting Visual Token Processing Redundancy for Efficient Decoder-Only MLLMs (2025.findings-acl)
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| Challenge: | Current decoder-only architectures achieve higher performance but lower efficiency . cross-attention-based architectures skip visual token computations . |
| Approach: | They propose a training-free framework for analyzing trained MLLMs to investigate redundancy . they propose 'probe-activated Dynamic FFN and Hollow Attention' algorithms for visual token reductions and a layer ranking algorithm for inference acceleration. |
| Outcome: | The proposed framework achieves comparable performance to or better than state-of-the-art methods while remaining compatible with them. |
Characteristic AI Agents via Large Language Models (2024.lrec-main)
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| Challenge: | Commercial products have been devoted to creating character-driven chatbots using large language models, but academic research in this area remains relatively scarce. |
| Approach: | They investigate the performance of LLMs in constructing characteristic AI agents by simulating real-life individuals across different settings. |
| Outcome: | The proposed benchmark compared LLMs with real-life individuals in different settings and includes evaluation metrics. |
SMILE: Single-turn to Multi-turn Inclusive Language Expansion via ChatGPT for Mental Health Support (2024.findings-emnlp)
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| Challenge: | Developing specialized dialogue systems for mental health support requires multi-turn conversation data . data privacy protection, time and cost involved in crowdsourcing are challenges . a new method for rewriting public single-turn dialogues into multi-turned ones is needed . |
| Approach: | They propose a single-turn to multi-turn inclusive language expansion technique that prompts ChatGPT to rewrite public single-turned dialogues into multi-turned ones. |
| Outcome: | The proposed method generates a large-scale, lifelike, and diverse dialogue dataset . it also develops SMILECHAT, a mental health chatbot . |