Is this you? Claim this profile to correct it, add a bio and choose the work people see first.
Claim this profileWorks88 from public data
- Task-oriented Dialogue System for Automatic Diagnosis255
Experimental results on this dialogue system show that additional symptoms extracted from conversation can greatly improve the accuracy for disease identification and the dialogue system is able to collect these symptoms automatically and make a better diagnosis.
- Template-free Prompt Tuning for Few-shot NER210
This work proposes a more elegant method to reformulate NER tasks as LM problems without any templates by discarding the template construction process while maintaining the word prediction paradigm of pre-training models to predict a class-related pivot word (or label word) at the entity position.
- LoRAMoE: Alleviating World Knowledge Forgetting in Large Language Models via MoE-Style Plugin186
Experimental results show that, as the instruction data increases, Lo-RAMoE can significantly improve the ability to process downstream tasks, while maintaining the world knowledge stored in the LLM.
- Cached Long Short-Term Memory Neural Networks for Document-Level Sentiment Classification185
A Cached Long Short-Term Memory neural networks (CLSTM) to capture the overall semantic information in long texts and introduces a cache mechanism, which divides memory into several groups with different forgetting rates and thus enables the network to keep sentiment information better within a recurrent unit.
- DISC-MedLLM: Bridging General Large Language Models and Real-World Medical Consultation136
The proposed DISC-MedLLM is a comprehensive solution that leverages Large Language Models (LLMs) to provide accurate and truthful medical response in end-to-end conversational healthcare services, surpassing existing medical LLMs in both single-turn and multi-turn consultation scenarios.
- Certified Robustness to Text Adversarial Attacks by Randomized [MASK]116
This study proposes a certifiably robust defense method by randomly masking a certain proportion of the words in an input text, which can defend against not only word substitution-based attacks, but also character-level perturbations.
- Benchmark Self-Evolving: A Multi-Agent Framework for Dynamic LLM Evaluation94
A benchmark self-evolving framework to dynamically evaluate rapidly advancing Large Language Models, utilizing a multi-agent system to manipulate the context or question of original instances, reframing new evolving instances with high confidence that dynamically extend existing benchmarks.
- Training Large Language Models for Reasoning through Reverse Curriculum Reinforcement Learning88
R$^3$ progressively slides the start state of reasoning from a demonstration's end to its beginning, facilitating easier model exploration at all stages, allowing outcome supervision to offer step-level signals and precisely pinpoint errors.
- Mask Attention Networks: Rethinking and Strengthen Transformer83
A novel understanding of SAN and FFN are presented as Mask Attention Networks (MANs) and it is shown that they are two special cases of MANs with static mask matrices, which limit the capability for localness modeling in text representation learning.
- A Knowledge-Aware Sequence-to-Tree Network for Math Word Problem Solving81
Experimental results revealed that the KA-S2T model can achieve better performance than previously reported best results and use a tree-structured decoder with a state aggregation mechanism to capture the long-distance dependency and global expression information.
- 79
- SocioVerse: A World Model for Social Simulation Powered by LLM Agents and A Pool of 10 Million Real-World Users70
SocioVerse is introduced, an LLM-agent-driven world model for social simulation that can reflect large-scale population dynamics while ensuring diversity, credibility, and representativeness through standardized procedures and minimal manual adjustments.
- Towards Efficient NLP: A Standard Evaluation and A Strong Baseline61
The proposed ELUE has a strong Pareto Frontier and makes a better evaluation for efficient NLP models, and a strong baseline, ElasticBERT, which allows BERT to exit at any layer in both static and dynamic ways is released.
- Task-oriented Dialogue System for Automatic Disease Diagnosis via Hierarchical Reinforcement Learning.58
Experimental results on both self-constructed real-world and synthetic datasets demonstrate that the proposed hierarchical framework achieves higher accuracy in disease diagnosis compared with existing systems.
- Tasty Burgers, Soggy Fries: Probing Aspect Robustness in Aspect-Based Sentiment Analysis56
A simple but effective approach to enrich ABSA test sets is developed to disentangle the confounding sentiments of the non-target aspects from the target aspect's sentiment, and several ways to improve aspect robustness are explored.
- Enhancing LLM Reasoning via Critique Models with Test-Time and Training-Time Supervision54
This paper delves into a two-player paradigm that separates the roles of reasoning and critique models, where the critique model provides step-level feedback to supervise the reasoning (actor) model during both test-time and train-time, and proposes a critique-in-the-loop self-improvement method.
- 49
- Are Large Language Models Good Prompt Optimizers?46
This work conducts a comprehensive study to uncover the actual mechanism of LLM-based Prompt Optimization, and introduces a new "Automatic Behavior Optimization" paradigm, which directly optimizes the target model's behavior in a more controllable manner.
- 46
- ElectionSim: Massive Population Election Simulation Powered by Large Language Model Driven Agents42
ElectionSim, an innovative election simulation framework based on large language models, designed to support accurate voter simulations and customized distributions, is introduced, together with an interactive platform to dialogue with simulated voters.
- TextObfuscator: Making Pre-trained Language Model a Privacy Protector via Obfuscating Word Representations41
This paper proposes TextObfuscator, a novel framework for preserving inference privacy by applying random perturbations to clustered representations that obscures word information while retaining the original word functionality.
- Hierarchical reinforcement learning for automatic disease diagnosis32
Inspired by the offline consultation process, this work proposes to integrate a hierarchical policy structure of two levels into the dialogue system for policy learning that achieves higher accuracy and symptom recall in disease diagnosis compared with existing systems.
- Safe Inputs but Unsafe Output: Benchmarking Cross-modality Safety Alignment of Large Vision-Language Models29
The SIUO is developed, a cross-modality benchmark encompassing 9 critical safety domains, such as self-harm, illegal activities, and privacy violations, that reveals substantial safety vulnerabilities in both closed- and open-source LVLMs, underscoring the inadequacy of current models to reliably interpret and respond to complex, real-world scenarios.
- End-to-End Neural Text Classification for Tibetan29
Three end-to-end neural models for Tibetan text classification are investigated and the experimental results show that the end- to-end models outperform the traditional TibetanText classification methods.
- Transferring from Formal Newswire Domain with Hypernet for Twitter POS Tagging26
This work proposes a hypernetwork-based method to generate different parameters to separately model contexts with different expression styles for POS tagging for Twitter, and shows that this approach achieves better performance than state-of-the-art methods in most cases.
Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-14. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
Report an error
Wrong papers, two people merged into one, or a profile that should not be here? Tell us and we will fix or hide it.