Papers by Xiaonan Li
LLatrieval: LLM-Verified Retrieval for Verifiable Generation (2024.naacl-long)
Copied to clipboard
| Challenge: | Large language models struggle with factual errors and often produce non-factual and fabricated content. |
| Approach: | They propose to use large language models to generate text with supporting documents to enable the user to flexibly verify the answer. |
| Outcome: | Experiments on ALCE show that LLatrieval significantly outperforms extensive baselines and achieves state-of-the-art results. |
BERT for Monolingual and Cross-Lingual Reverse Dictionary (2020.findings-emnlp)
Copied to clipboard
| Challenge: | Existing methods to find the proper word for a word are based on the surface form of words, but they cannot extract the semantic meaning. |
| Approach: | They propose a method to make BERT generate the target word for this task . cross-lingual reverse dictionary is the task to find the proper target word . |
| Outcome: | The proposed method can generate the target word for cross-lingual reverse dictionary task even without the parallel corpus. |
Finding Support Examples for In-Context Learning (2023.findings-emnlp)
Copied to clipboard
| Challenge: | In-context learning is a new learning paradigm where a language model observes a few examples and directly outputs the test input’s prediction. |
| Approach: | They propose a method to find “support examples” for in-context learning by filtering a training dataset and a progressive filtering process to filter out uninformative examples. |
| Outcome: | The proposed method outperforms baselines and shows that each component contributes critically to the improvements. |
Unified Demonstration Retriever for In-Context Learning (2023.acl-long)
Copied to clipboard
Xiaonan Li, Kai Lv, Hang Yan, Tianyang Lin, Wei Zhu, Yuan Ni, Guotong Xie, Xiaoling Wang, Xipeng Qiu
| Challenge: | In-context learning is a new learning paradigm where a language model conditions on a few input-output pairs (demonstrations) and a test input, and directly outputs the prediction. |
| Approach: | They propose a single model to retrieve demonstrations for a wide range of tasks by combining training signals from various tasks into a unified list-wise ranking formulation by language model’s feedback. |
| Outcome: | The proposed model outperforms baselines on 30+ tasks across 13 task families and multiple data domains. |
An Embarrassingly Easy but Strong Baseline for Nested Named Entity Recognition (2023.acl-short)
Copied to clipboard
| Challenge: | Named entity recognition (NER) is the task to detect and classify entity spans in text. |
| Approach: | They propose to use Convolutional Neural Network to model spatial relations in NER . they use three commonly used nested NER datasets to compare their results . |
| Outcome: | The proposed model outperforms several proposed methods with the same pre-trained encoders in three nested NER datasets. |
Inference-Time Decontamination: Reusing Leaked Benchmarks for Large Language Model Evaluation (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Current LLMs are achieving better performance on various benchmarks, but their performance in practical applications does not always match their benchmark results. |
| Approach: | They propose to detect and rewrite leaked benchmarks without altering their difficulties by using Inference-Time Decontamination (ITD) to mitigate performance inflation caused by memorizing leaked samples. |
| Outcome: | The proposed method reduces inflated accuracy by 22.9% on GSM8K and 19.0% on MMLU. |
Reasoning in Flux: Enhancing Large Language Models Reasoning through Uncertainty-aware Adaptive Guidance (2024.acl-long)
Copied to clipboard
Zhangyue Yin, Qiushi Sun, Qipeng Guo, Zhiyuan Zeng, Xiaonan Li, Junqi Dai, Qinyuan Cheng, Xuanjing Huang, Xipeng Qiu
| Challenge: | Extensive experiments across various reasoning tasks demonstrate that UAG not only enhances the reasoning abilities of LLMs but consistently outperforms several strong baselines with minimal computational overhead. |
| Approach: | They propose an approach to guide LLMs onto an accurate and reliable trajectory by identifying and adjusting uncertainty signals within each step of the reasoning chain. |
| Outcome: | The proposed approach outperforms strong baselines and outperformed strong models with minimal computational overhead. |
Long Chain-of-Thought Fine-tuning via Understanding-to-Reasoning Transition (2025.emnlp-main)
Copied to clipboard
Chenxin An, Zhihui Xie, Xiaonan Li, Ming Zhong, Shansan Gong, Lei Li, Jun Zhang, Jingjing Xu, Lingpeng Kong
| Challenge: | Existing research on long-context scaling in language models has focused on managing lengthy input prompts instead of producing long outputs. |
| Approach: | They propose a sequence-level curriculum learning framework that shifts a model’s focus from interpreting long chain-of-thoughts to generating them. |
| Outcome: | Experiments on rigorous reasoning benchmarks, including AIME24 and GPQA Diamond, show that the proposed approach surpasses standard fine-tuning by over 10% while maintaining robust performance on understanding tasks. |
Accelerating BERT Inference for Sequence Labeling via Early-Exit (2021.acl-long)
Copied to clipboard
| Challenge: | Existing early-exit mechanisms are designed for sequence-level tasks, rather than sequence labeling. |
| Approach: | They propose to extend sentence-level early-exit to accelerate inference of PTMs . they propose a token-level mechanism that allows partial tokens to exit early at different layers . |
| Outcome: | The proposed approach can save up to 66%75% inference cost with minimal performance degradation. |
Does it Make Sense? And Why? A Pilot Study for Sense Making and Explanation (P19-1)
Copied to clipboard
| Challenge: | Existing benchmarks measure common sense knowledge indirectly or without reasoning. |
| Approach: | They propose a benchmark to test whether a system can differentiate natural language statements that make sense from those that do not make sense. |
| Outcome: | The proposed benchmarks show that models trained on large corpora perform better than humans on some benchmarks. |
MoT: Memory-of-Thought Enables ChatGPT to Self-Improve (2023.emnlp-main)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) have shown impressive abilities on various tasks. however, fundamentally improving them depends on high-quality datasets or computationally expensive fine-tuning. |
| Approach: | They propose a framework to let Large Language Models self-improve without annotated datasets or parameter updates. |
| Outcome: | The proposed framework can help ChatGPT significantly improve its abilities in arithmetic reasoning, commonsense reasoning, factual reasoning, and natural language inference. |
R3-RAG: Learning Step-by-Step Reasoning and Retrieval for LLMs via Reinforcement Learning (2025.findings-emnlp)
Copied to clipboard
Yuan Li, Qi Luo, Xiaonan Li, Bufan Li, Qinyuan Cheng, Bo Wang, Yining Zheng, Yuxin Wang, Zhangyue Yin, Xipeng Qiu
| Challenge: | RAG systems that integrate external knowledge with Large Language Models often become bottlenecks due to their limited parameters compared to LLMs and their inability to perform step-by-step reasoning. |
| Approach: | They propose a model that integrates external knowledge with Large Language Models to enhance factual correctness and mitigate hallucination. |
| Outcome: | The proposed model outperforms baselines and can transfer well to different retrievers. |
CodeRetriever: A Large Scale Contrastive Pre-Training Method for Code Search (2022.emnlp-main)
Copied to clipboard
Xiaonan Li, Yeyun Gong, Yelong Shen, Xipeng Qiu, Hang Zhang, Bolun Yao, Weizhen Qi, Daxin Jiang, Weizhu Chen, Nan Duan
| Challenge: | Existing code pre-training approaches often adopt (masked) language modeling as the training objective which targets on learning to predict (macked) tokens in a given code context. |
| Approach: | They propose a code-text contrastive learning model which learns function-level code semantic representations through large-scale code corpus. |
| Outcome: | The proposed model achieves new state-of-the-art with significant improvement over existing pre-trained models on eleven domain/language-specific code search tasks with six programming languages in different code granularity. |
Soft-Labeled Contrastive Pre-Training for Function-Level Code Representation (2022.findings-emnlp)
Copied to clipboard
Xiaonan Li, Daya Guo, Yeyun Gong, Yun Lin, Yelong Shen, Xipeng Qiu, Daxin Jiang, Weizhu Chen, Nan Duan
| Challenge: | Existing methods for contrastive pre-training ignore the relevance between codes in large code corpus. |
| Approach: | They propose a Soft-labeled contrastive pre-training framework with positive sample construction methods to learn functional-level code representation. |
| Outcome: | The proposed framework can obtain fine-grained soft-labels through an iterative adversarial manner and use them to learn better code representation. |
Unified Active Retrieval for Retrieval Augmented Generation (2024.findings-emnlp)
Copied to clipboard
Qinyuan Cheng, Xiaonan Li, Shimin Li, Qin Zhu, Zhangyue Yin, Yunfan Shao, Linyang Li, Tianxiang Sun, Hang Yan, Xipeng Qiu
| Challenge: | Existing active retrieval methods struggle with handling various types of instructions. |
| Approach: | They propose a unified active retrieval framework for retrieval-augmented generation . they propose to combine four orthogonal criteria into plug-and-play classification tasks . |
| Outcome: | The proposed framework outperforms existing methods on four representative types of user instructions on four types of instructions. |
UTC-IE: A Unified Token-pair Classification Architecture for Information Extraction (2023.acl-long)
Copied to clipboard
| Challenge: | Information Extraction (IE) tasks have been solved with different models because of their output structures. |
| Approach: | They propose a Unified Token-pair Classification architecture for Information Extraction that introduces Plusformer on top of the token-pear feature matrix. |
| Outcome: | The proposed approach outperforms task-specific and unified models on all tasks in 10 datasets and achieves better results on 2 joint IE datasets. |
Backdoor Attacks on Pre-trained Models by Layerwise Weight Poisoning (2021.emnlp-main)
Copied to clipboard
| Challenge: | Pre-trained models can be maliciously poisoned with certain triggers, causing a security threat. |
| Approach: | They propose a stronger weight poisoning attack method that introduces a layerwise weight poison strategy to plant deeper backdoors. |
| Outcome: | The proposed method can be widely applied and provide hints for future models robustness studies. |
Scaling Laws for Fact Memorization of Large Language Models (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Fact knowledge memorization is crucial for Large Language Models (LLMs) to generate factual and reliable responses. |
| Approach: | They analyze scaling laws for LLM’s fact knowledge and LLMs’ behaviors of memorizing different types of facts. |
| Outcome: | The proposed model can generalize on unseen facts and its scaling law is similar to general pre-training. |
Aggregation of Reasoning: A Hierarchical Framework for Enhancing Answer Selection in Large Language Models (2024.lrec-main)
Copied to clipboard
Zhangyue Yin, Qiushi Sun, Qipeng Guo, Zhiyuan Zeng, Xiaonan Li, Tianxiang Sun, Cheng Chang, Qinyuan Cheng, Ding Wang, Xiaofeng Mou, Xipeng Qiu, Xuanjing Huang
| Challenge: | Recent advances in Chain-of-Thought prompting have facilitated significant breakthroughs for Large Language Models (LLMs) in complex reasoning tasks. |
| Approach: | They propose a hierarchical reasoning aggregation framework to address this problem . they propose dynamic sampling to adjust the number of reasoning chains . |
| Outcome: | The proposed framework outperforms existing ensemble methods on complex reasoning tasks. |
Case2Code: Scalable Synthetic Data for Code Generation (2025.coling-main)
Copied to clipboard
Yunfan Shao, Linyang Li, Yichuan Ma, Peiji Li, Demin Song, Qinyuan Cheng, Shimin Li, Xiaonan Li, Pengyu Wang, Qipeng Guo, Hang Yan, Xipeng Qiu, Xuanjing Huang, Dahua Lin
| Challenge: | Large Language Models (LLMs) have shown outstanding breakthroughs in code generation. |
| Approach: | They propose a case-to-code induction task that exploits the expressiveness and correctness of programs by incorporating LLMs into their training. |
| Outcome: | The proposed task improves distribution case-to-code induction and various coding generation tasks. |
FLAT: Chinese NER Using Flat-Lattice Transformer (2020.acl-main)
Copied to clipboard
| Challenge: | Named entity recognition (NER) models are difficult to use because of the complex nature of the lattice structure and the low inference speed. |
| Approach: | They propose a character-word lattice structure that converts lattics into flat structures consisting of spans. |
| Outcome: | The proposed model outperforms other lexicon-based models on four datasets and is highly parallel. |