Papers by Xin Sheng
Enhancing Chain-of-Thought Reasoning via Neuron Activation Differential Analysis (2025.emnlp-main)
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| Challenge: | Existing studies focus on optimizing external components of CoT, but lack internal explanations for the quality of the model's outputs. |
| Approach: | They propose an efficient method to identify reasoning-critical neurons by analyzing their activation patterns under reasoning chains of varying quality. |
| Outcome: | The proposed method shows that neurons in the feed-forward layers are critical in the generation of high-quality reasoning chains. |
Can Language Models Capture Human Writing Preferences for Domain-Specific Text Summarization? (2025.findings-acl)
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| Challenge: | Recent studies employ large language models as auxiliary tools for humancentered NLP. |
| Approach: | They construct a model to capture human writing preferences by fine-tuning pre-trained models with data and designing prompts to optimize the output of large language models. |
| Outcome: | The proposed model captures human writing preferences through the dimensions of length, content depth, tone & style, and summary format. |
Event Causality Extraction with Event Argument Correlations (2022.coling-1)
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| Challenge: | Event Causality Identification (ECI) ignores crucial event structure and cause-effect component information, making it struggle for downstream applications. |
| Approach: | They propose a task to extract event causality pairs with their structured event information from plain text. |
| Outcome: | The proposed method captures the intra- and inter-event argument correlations for ECE and provides several future directions. |
Semantic-Preserving Abstractive Text Summarization with Siamese Generative Adversarial Net (2022.findings-naacl)
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| Challenge: | Existing methods focus on attention mechanism, but they are not suitable for abstractive text summarization. |
| Approach: | They propose a siamese generative adversarial net for abstractive text summarization which preserves the main semantics of the source text and the target summary. |
| Outcome: | The proposed model can preserve the main semantics of the source text and target summary. |
RGL: A Simple yet Effective Relation Graph Augmented Prompt-based Tuning Approach for Few-Shot Learning (2022.findings-naacl)
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| Challenge: | Pre-trained language models (PLMs) are a good starting point for downstream applications, but it is difficult to generalize them to new tasks given a few labeled samples. |
| Approach: | They propose to use Relation Graph augmented learning to improve the performance of few-shot natural language understanding tasks by rewriting the input sequence into a cloze question with masks. |
| Outcome: | Extensive experiments show that Relation Graph augmented learning (RGL) improves performance of prompt-based tuning strategies. |
MMAD:Multi-modal Movie Audio Description (2024.lrec-main)
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| Challenge: | Current methods of creating accessible movies rely on manual work, resulting in high costs and limited scalability. |
| Approach: | They propose a multi-modal movie audio description pipeline that generates narrations of information that is not accessible through unimodal hearing in movies. |
| Outcome: | The proposed pipeline surpasses existing baselines in performance on widely used datasets. |
RecMem: Recurrence-based Memory Consolidation for Efficient and Effective Long-Running LLM Agents (2026.findings-acl)
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| Challenge: | Existing memory systems invoke LLMs to extract episodic and semantic memory, and this leads to substantial token consumption. |
| Approach: | They propose a method that stores incoming interactions in a subconscious memory layer and encodes them using lightweight embedding models for retrieval. |
| Outcome: | Experiments show that RecMem reduces the memory construction token cost of three SOTA memory systems by up to 87% while exceeding their accuracy. |
Retrieved Sequence Augmentation for Protein Representation Learning (2024.emnlp-main)
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Chang Ma, Haiteng Zhao, Lin Zheng, Jiayi Xin, Qintong Li, Lijun Wu, Zhihong Deng, Yang Lu, Qi Liu, Sheng Wang, Lingpeng Kong
| Challenge: | Using multiple sequence alignments (MSA) to extract evolutionary knowledge is limited. |
| Approach: | They propose to use multiple sequence alignments to augment protein representations . they propose to employ Retrieved Sequence Augmentation to enhance protein representation learning . |
| Outcome: | The proposed method surpasses MSA Transformer by 5% in structural and property prediction tasks while being 373 times faster. |
Sociolectal Analysis of Pretrained Language Models (2021.emnlp-main)
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| Challenge: | Using data from English cloze tests, we demonstrate wide performance gaps across demographic groups and show that pretrained language models disfavor young non-white male speakers. |
| Approach: | They use data from English cloze tests to examine performance differences of pretrained language models across demographic groups. |
| Outcome: | The models disfavor young non-white male speakers, but larger models reduce performance gaps between majority and minority groups. |
CoCGAN: Contrastive Learning for Adversarial Category Text Generation (2022.coling-1)
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| Challenge: | Experimental results on synthetic and real category text generation datasets demonstrate that CoCGAN can achieve significant improvements over the baseline category text generators. |
| Approach: | They propose to incorporate contrastive learning into adversarial category text generation by using a discriminator to optimize a contrastive learn objective to capture more flexible data-to-class relations and data- to-data relations among training samples. |
| Outcome: | The proposed model improves on synthetic and real category text generation datasets. |