Papers by Siyang Liu
Task-Adaptive Tokenization: Enhancing Long-Form Text Generation Efficacy in Mental Health and Beyond (2023.emnlp-main)
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| Challenge: | Existing methods to generate text in mental health are limiting, but they are effective for many tasks. |
| Approach: | They propose a task-adaptive tokenizer that allows for the integration of task-specific tokens into the pre-trained model's tokenization step. |
| Outcome: | The proposed tokenization approach improves generation performance on psychological question-answering tasks in Chinese and English while using 60% fewer tokens. |
Eeyore: Realistic Depression Simulation via Expert-in-the-Loop Supervised and Preference Optimization (2025.findings-acl)
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| Challenge: | Large Language Models (LLMs) have been explored for mental healthcare training and therapy client simulation, but they fail to authentically capture diverse client traits and psychological conditions. |
| Approach: | They propose an 8B model optimized for realistic depression simulation with expert input at every stage. |
| Outcome: | The model outperforms GPT-4o in linguistic authenticity and profile adherence. |
The Generation Gap: Exploring Age Bias in the Value Systems of Large Language Models (2024.emnlp-main)
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| Challenge: | Using the World Value Survey, we find a general inclination of LLM values towards younger demographics, especially when compared to the US population. |
| Approach: | They use data from the World Value Survey to examine the alignment of LLM values with specific age groups. |
| Outcome: | The proposed model can be used to predict the value of a large language model and to assess its performance on 13 categories. |
Towards Emotional Support Dialog Systems (2021.acl-long)
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| Challenge: | Emotional support is a crucial ability for many conversation scenarios, including social interactions, mental health support, and customer service chats. |
| Approach: | They propose an Emotional Support Conversation task and an ESC Framework to train emotional support into dialog systems. |
| Outcome: | The proposed framework provides an example of an Emotional Support Conversation task and shows that it is more effective than existing models. |
Rethinking and Refining the Distinct Metric (2022.acl-short)
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| Challenge: | Existing methods for calculating distinct scores have evident biases that assign higher penalties to longer sequences. |
| Approach: | They propose to scale the number of distinct tokens based on their expectations. |
| Outcome: | The proposed metric removes evident biases in the original distinct score . the proposed meter correlates better with human judgment in evaluating response diversity . |
EmoBench: Evaluating the Emotional Intelligence of Large Language Models (2024.acl-long)
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Sahand Sabour, Siyang Liu, Zheyuan Zhang, June Liu, Jinfeng Zhou, Alvionna Sunaryo, Tatia Lee, Rada Mihalcea, Minlie Huang
| Challenge: | Existing benchmarks for Emotional Intelligence (EI) focus on emotion recognition, neglecting essential EI capabilities. |
| Approach: | They propose a benchmark that proposes a comprehensive definition for machine EI . they propose 400 hand-crafted questions in English and Chinese to evaluate EI. |
| Outcome: | The proposed benchmarks focus on emotion recognition, neglecting EI capabilities . they are constructed from existing datasets, which include frequent patterns and errors . the proposed benchmark includes questions in English and Chinese that require thorough reasoning and understanding . |
Distance between Relevant Information Pieces Causes Bias in Long-Context LLMs (2025.findings-acl)
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Runchu Tian, Yanghao Li, Yuepeng Fu, Siyang Deng, Qinyu Luo, Cheng Qian, Shuo Wang, Xin Cong, Zhong Zhang, Yesai Wu, Yankai Lin, Huadong Wang, Xiaojiang Liu
| Challenge: | Positional biases in large language models hinder their ability to process long inputs. |
| Approach: | They propose a benchmark to assess positional bias in large language models involving multiple pieces of relevant information. |
| Outcome: | The proposed benchmark assesses the performance of long-context language models by examining their models with different input lengths and tasks. |
An Explicit-Joint and Supervised-Contrastive Learning Framework for Few-Shot Intent Classification and Slot Filling (2021.findings-emnlp)
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| Challenge: | Intent classification and slot filling are key building blocks in task-oriented dialogue systems. |
| Approach: | They propose an explicit-joint and supervised-contrastive learning framework for few-shot intent classification and slot filling. |
| Outcome: | The proposed model extracts intent and slot representations via bidirectional interactions and extends prototypical network to achieve explicit-joint learning. |
SentiLARE: Sentiment-Aware Language Representation Learning with Linguistic Knowledge (2020.emnlp-main)
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| Challenge: | Existing pre-trained models neglect to consider linguistic knowledge of texts . existing models neglect linguistic information, which is important for sentiment analysis . |
| Approach: | They propose a model that introduces word-level linguistic knowledge into pre-trained models to enhance sentiment analysis by querying SentiWordNet to acquire sentiment polarity. |
| Outcome: | The proposed model obtains state-of-the-art performance on a variety of sentiment analysis tasks. |
PsyQA: A Chinese Dataset for Generating Long Counseling Text for Mental Health Support (2021.findings-acl)
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| Challenge: | Existing research on text-based mental health counseling is limited due to the lack of relevant corpora in Chinese language. |
| Approach: | They propose a Chinese dataset of psychological health support in the form of question and answer pair that is crawled from a mental health service platform and contains 22K questions and 56K long and wellstructured answers. |
| Outcome: | The proposed dataset contains 22K questions and 56K long and wellstructured answers. |
Has It All Been Solved? Open NLP Research Questions Not Solved by Large Language Models (2024.lrec-main)
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Oana Ignat, Zhijing Jin, Artem Abzaliev, Laura Biester, Santiago Castro, Naihao Deng, Xinyi Gao, Aylin Ece Gunal, Jacky He, Ashkan Kazemi, Muhammad Khalifa, Namho Koh, Andrew Lee, Siyang Liu, Do June Min, Shinka Mori, Joan C. Nwatu, Veronica Perez-Rosas, Siqi Shen, Zekun Wang, Winston Wu, Rada Mihalcea
| Challenge: | Recent advances in large language models have led to misleading public discourse that “it’s all been solved.” |
| Approach: | They identify 14 research areas encompassing 45 research directions that require new research and are not directly solvable by LLMs. |
| Outcome: | The research areas identified are 45 research directions that require new research and are not directly solvable by LLMs. |
You Are What You Annotate: Towards Better Models through Annotator Representations (2023.findings-emnlp)
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| Challenge: | Annotator disagreement is ubiquitous in natural language processing tasks. |
| Approach: | They propose to model annotators' idiosyncrasies and account for their idioms by creating representations for each annotator and their annotations. |
| Outcome: | The proposed model improves on an existing dataset with eight annotators with inherent disagreements while increasing model size by 1%. |