Papers by Yixin Xiang
What Can We Learn from Collective Human Opinions on Natural Language Inference Data? (2020.emnlp-main)
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| Challenge: | Despite the subjective nature of many NLU evaluations, little attention has been paid to the distribution of human opinions. |
| Approach: | They use a dataset with 464,500 annotations to study Collective HumAn OpinionS . they argue that models lack the ability to recover the distribution over human labels . |
| Outcome: | The proposed dataset examines the distribution of human opinions in NLU evaluation datasets. |
MolCA: Molecular Graph-Language Modeling with Cross-Modal Projector and Uni-Modal Adapter (2023.emnlp-main)
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| Challenge: | Language Models (LMs) have demonstrated impressive molecule understanding ability on 1D text-related tasks, but lack 2D graph perception, a critical ability of human professionals in comprehending molecules’ topological structures. |
| Approach: | They propose to combine a cross-modal projector and a uni-modal adapter to enable an LM to understand both text- and graph-based molecular contents via a Q-Former. |
| Outcome: | The proposed model outperforms the baselines on tasks such as molecule captioning, IUPAC name prediction, and molecule-text retrieval. |
The Curse of Performance Instability in Analysis Datasets: Consequences, Source, and Suggestions (2020.emnlp-main)
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| Challenge: | Neural network models have significantly pushed forward performance on natural language processing benchmarks with the development of largescale language model pre-training. |
| Approach: | They find that models on natural language inference and reading comprehension are unstable . they propose to use a model-selection routine to analyze the model's instability . |
| Outcome: | The proposed models can perform poorly on two language-related tasks, the authors show . they also show that the model selection routine is unstable, and that it is not reliable . |
PRISM: Probabilistic Reward Model with Inherent Structural Modeling (2026.acl-long)
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| Challenge: | Existing evaluators compress diverse human judgments into a single scalar, leading to brittle alignment and reward hacking. |
| Approach: | They propose a Gaussian-based reinterpretation of reward evaluation as a conditional distribution and a mixture of Gaussians to capture conflicting preference dimensions. |
| Outcome: | The proposed model outperforms scalar baselines in accuracy and generalization. |
Diagnosing Memorization in Chain-of-Thought Reasoning, One Token at a Time (2025.emnlp-main)
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| Challenge: | Large Language Models (LLMs) perform well on reasoning benchmarks but often fail when inputs alter slightly, raising concerns about overreliance on memorization. |
| Approach: | They propose a framework for Source-aware Token-level Identification of Memorization which attributes each token in a reasoning chain to one of multiple memorization sources based on their statistical co-occurrence with the token in the pretraining corpus. |
| Outcome: | The proposed framework attributes each token in a reasoning chain to one of multiple memorization sources based on their statistical co-occurrence with the token in the pretraining corpus. |
MAB-DQA: Addressing Query Aspect Importance in Document Question Answering with Multi-Armed Bandits (2026.acl-long)
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| Challenge: | Document Question Answering (DQA) requires interpreting visual layouts, which has prompted recent studies to adopt multimodal Retrieval-Augmented Generation (RAG) that processes page images for answer generation. |
| Approach: | They propose a multi-armed bandit-based DQA framework that explicitly models the varying importance of multiple implicit aspects in a query. |
| Outcome: | The proposed framework shows an improvement of 5%-18% over the state-of-the-art method on four benchmarks. |
DialCoT Meets PPO: Decomposing and Exploring Reasoning Paths in Smaller Language Models (2023.emnlp-main)
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| Challenge: | Chain-of-Thought prompting has improved the reasoning capabilities of Large Language Models (LLMs) but it is ineffective or detrimental to the performance on reasoning tasks in Smaller Language Model (SLMs) with less than 10 billion parameters. |
| Approach: | They propose a Dialogue-guided Chain-of-Thought method to improve the reasoning capabilities of Large Language Models (LLMs) by generating intermediate reasoning steps in a dialogue format to guide the model to the final answer. |
| Outcome: | The proposed method can achieve significant performance gains over state-of-the-art competitors on four arithmetic reasoning datasets. |
Are Missing Links Predictable? An Inferential Benchmark for Knowledge Graph Completion (2021.acl-long)
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| Challenge: | Existing benchmarks for Knowledge Graph Completion (KGC) are unsatisfactory . |
| Approach: | They propose to use rule-guided train/test generation instead of conventional random split to ensure that each testing sample is predictable with supportive data in the training set. |
| Outcome: | The proposed model improves on existing benchmarks in inferential ability, assumptions, and patterns. |
Adaptive Feature-based Low-Rank Compression of Large Language Models via Bayesian Optimization (2024.findings-emnlp)
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Yixin Ji, Yang Xiang, Juntao Li, Qingrong Xia, Zi Ye, Xinyu Duan, Zhefeng Wang, Kehai Chen, Min Zhang
| Challenge: | Large language models require a balance between efficiency and performance. |
| Approach: | They propose a low-rank compression technique that reduces non-essential parameters by decomposing weight matrices into products of two low-ranked matrici. |
| Outcome: | The proposed method outperforms existing pruning and low-rank compression techniques in maintaining model performance at the same compression ratio. |
When Is Thinking Enough? Early Exit via Sufficiency Assessment for Efficient Reasoning (2026.acl-long)
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| Challenge: | Existing approaches to early exit reasoning often rely on handcrafted or empirical indicators that are unreliable and impractical. |
| Approach: | They propose a framework that allows LRMs to assess the sufficiency of its chain-of-thought and determine the optimal point for early exit. |
| Outcome: | The proposed framework reduces reasoning length by 28.9%–34.9% with minimal performance loss, effectively mitigating overthinking. |
Reasoning with Language Model Prompting: A Survey (2023.acl-long)
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Shuofei Qiao, Yixin Ou, Ningyu Zhang, Xiang Chen, Yunzhi Yao, Shumin Deng, Chuanqi Tan, Fei Huang, Huajun Chen
| Challenge: | Reasoning is an essential ability for complex problem-solving and can provide back-end support for various real-world applications. |
| Approach: | They present cutting-edge research on reasoning with language model prompting and provide systematic resources to help beginners. |
| Outcome: | The proposed approaches have not been systematically reviewed and analyzed. |
Distributed NLI: Learning to Predict Human Opinion Distributions for Language Reasoning (2022.findings-acl)
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| Challenge: | Using distributed NLI, we show that models can capture human judgement distribution more effectively than the softmax baseline. |
| Approach: | They propose a new NLU task to predict the distribution of human judgements . they propose Monte Carlo, Deep Ensemble, Re-Calibration and Distribution Distillation methods to capture human judgement distributions. |
| Outcome: | The proposed methods perform better than the softmax baseline, but the results are still far below the estimated human upper-bound. |
Improving the Adversarial Robustness of NLP Models by Information Bottleneck (2022.findings-acl)
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| Challenge: | Existing studies have shown that adversarial examples can be directly attributed to the presence of non-robust features. |
| Approach: | They propose to capture task-specific robust features while eliminating non-robust ones . they show that models can achieve significant improvement in robust accuracy . |
| Outcome: | The proposed method outperforms all defense methods on SST-2, AGNEWS and IMDB datasets while achieving no performance drop. |
A Comprehensive Evaluation of Large Language Models on Legal Judgment Prediction (2023.findings-emnlp)
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| Challenge: | Large language models (LLMs) have demonstrated great potential for domain-specific applications, such as the law domain. |
| Approach: | They propose a framework to investigate LLMs' competence in the law domain by using similar cases and multi-choice options. |
| Outcome: | The proposed solutions can be extended to other domains to facilitate evaluations in other domain. |
Are LLMs Rational Investors? A Study on the Financial Bias in LLMs (2025.findings-acl)
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Yuhang Zhou, Yuchen Ni, Zhiheng Xi, Zhangyue Yin, Yu He, Gan Yunhui, Xiang Liu, Zhang Jian, Sen Liu, Xipeng Qiu, Yixin Cao, Guangnan Ye, Hongfeng Chai
| Challenge: | Existing studies on biases within specific domains, such as finance, remain limited. |
| Approach: | They propose a framework to detect, detect, analyze and mitigate financial biases in large language models. |
| Outcome: | The proposed framework reduces bias by 68% for the most biased model, according to key metrics. |