Papers by Yanzheng Xiang
PECAN: LLM-Guided Dynamic Progress Control with Attention-Guided Hierarchical Weighted Graph for Long-Document QA (2025.findings-acl)
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| Challenge: | Long-document Question Answering (QA) challenges with large-scale text and long-distance dependencies. |
| Approach: | They propose a method that leverages large language models to control retrieval process . they propose 'attention-based' retrieval methods that construct hierarchical graphs . |
| Outcome: | The proposed method achieves LLM-level performance while maintaining computational complexity comparable to RAG methods. |
The Mystery of In-Context Learning: A Comprehensive Survey on Interpretation and Analysis (2024.emnlp-main)
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| Challenge: | In-context learning (ICL) is a capability that enables large language models to excel in proficiency through demonstration examples. |
| Approach: | They present a survey on the interpretation and analysis of in-context learning . they focus on theoretical and empirical perspectives on the concept . |
| Outcome: | The proposed model can perform tasks with minimal examples without re-training and has demonstrated proficiency across various tasks with a minimal set of task-oriented examples. |
Code Generation From Flowcharts with Texts: A Benchmark Dataset and An Approach (2022.findings-emnlp)
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| Challenge: | Currently, researchers focus on generating codes from requirement documents. |
| Approach: | They propose to generate source code from flowcharts with texts instead of directly translating requirements into codes. |
| Outcome: | The proposed model improves on the baselines by transforming flowcharts into pseudo-code . the proposed model is based on 320 flowchartes with their corresponding source codes . |
Encourage or Inhibit Monosemanticity? Revisit Monosemanticity from a Feature Decorrelation Perspective (2024.emnlp-main)
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| Challenge: | Recent studies focus on monosemanticity on its basic units. |
| Approach: | They propose to revisit monosemanticity from the feature decorrelation perspective and advocate for its encouragement. |
| Outcome: | The proposed method improves representation diversity and activation sparsity and improves preference alignment performance. |
Addressing Order Sensitivity of In-Context Demonstration Examples in Causal Language Models (2024.findings-acl)
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| Challenge: | In-context learning is a popular paradigm in natural language processing, but its performance can be significantly influenced by the order of in-concept demonstration examples. |
| Approach: | They propose an unsupervised fine-tuning method to reduce the sensitivity of causal language models to the order of in-context demonstration examples. |
| Outcome: | The proposed method reduces the sensitivity of CausalLMs to the order of in-context examples and exhibits robust generalizability. |
Complicate Then Simplify: A Novel Way to Explore Pre-trained Models for Text Classification (2022.coling-1)
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| Challenge: | Existing frameworks for text classification employing pre-trained models are constrained by the difficulty of the task. |
| Approach: | They propose a framework which implements a two-stage training strategy to fully exploit the knowledge in pre-trained models. |
| Outcome: | The proposed framework outperforms state-of-the-art classification models on six text classification corpora. |
A Divide-And-Conquer Approach for Multi-label Multi-hop Relation Detection in Knowledge Base Question Answering (2021.findings-emnlp)
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| Challenge: | Existing methods for relation detection only detect one path to obtain the answer without considering other correct paths. |
| Approach: | They propose a divide-and-conquer approach for multi-label multi-hop relation detection . they propose 'path sampling mechanism' to generate diverse relation paths . |
| Outcome: | The proposed approach outperforms other competitive approaches on the FreebaseQA benchmark dataset. |
G3R: A Graph-Guided Generate-and-Rerank Framework for Complex and Cross-domain Text-to-SQL Generation (2023.findings-acl)
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| Challenge: | Existing approaches to complex and cross-domain Text-to-SQL generation lack domain knowledge . domain knowledge is not incorporated to enhance their ability to generalise to unseen databases. |
| Approach: | They propose a framework called G3R for complex and cross-domain Text-to-SQL generation . they propose re-ranking SQL queries based on domain knowledge and a graph-guided SQL generator . |
| Outcome: | The proposed framework achieves state-of-the-art results on the Spider and Spider-DK benchmarks. |