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Xiubo Geng

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11 papers

2 author rows

ICLR Conference 2025 Conference Paper

WizardMath: Empowering Mathematical Reasoning for Large Language Models via Reinforced Evol-Instruct

Haipeng Luo
Qingfeng Sun
Can Xu
Pu Zhao 0004
Jian-Guang Lou
Chongyang Tao
Xiubo Geng
Qingwei Lin

Large language models (LLMs), such as GPT-4, have shown remarkable performance in natural language processing (NLP) tasks, including challenging mathematical reasoning. However, most existing open-source models are only pre-trained on large-scale internet data and without math-related optimization. In this paper, we present WizardMath, which enhances the mathematical reasoning abilities of LLMs, by applying our proposed Reinforcement Learning from Evol-Instruct Feedback (RLEIF) method to the domain of math. Through extensive experiments on two mathematical reasoning benchmarks, namely GSM8k and MATH, we reveal the extraordinary capabilities of our model. Remarkably, WizardMath-Mistral 7B surpasses all other open-source LLMs by a substantial margin. Furthermore, WizardMath 70B even outperforms ChatGPT-3.5, Claude Instant, Gemini Pro and Mistral Medium. Additionally, our preliminary exploration highlights the pivotal role of instruction evolution and process supervision in achieving exceptional math performance.

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AAAI Conference 2024 Conference Paper

Fine-Grained Distillation for Long Document Retrieval

Yucheng Zhou
Tao Shen
Xiubo Geng
Chongyang Tao
Jianbing Shen
Guodong Long
Can Xu
Daxin Jiang

Long document retrieval aims to fetch query-relevant documents from a large-scale collection, where knowledge distillation has become de facto to improve a retriever by mimicking a heterogeneous yet powerful cross-encoder. However, in contrast to passages or sentences, retrieval on long documents suffers from the \textit{scope hypothesis} that a long document may cover multiple topics. This maximizes their structure heterogeneity and poses a granular-mismatch issue, leading to an inferior distillation efficacy. In this work, we propose a new learning framework, fine-grained distillation (FGD), for long-document retrievers. While preserving the conventional dense retrieval paradigm, it first produces global-consistent representations crossing different fine granularity and then applies multi-granular aligned distillation merely during training. In experiments, we evaluate our framework on two long-document retrieval benchmarks, which show state-of-the-art performance.

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ICLR Conference 2024 Conference Paper

WizardCoder: Empowering Code Large Language Models with Evol-Instruct

Ziyang Luo
Can Xu
Pu Zhao 0004
Qingfeng Sun
Xiubo Geng
Wenxiang Hu
Chongyang Tao
Jing Ma 0004

Code Large Language Models (Code LLMs), such as StarCoder, have demonstrated remarkable performance in various code-related tasks. However, different from their counterparts in the general language modeling field, the technique of instruction fine-tuning remains relatively under-researched in this domain. In this paper, we present Code Evol-Instruct, a novel approach that adapts the Evol-Instruct method to the realm of code, enhancing Code LLMs to create novel models, WizardCoder. Through comprehensive experiments on five prominent code generation benchmarks, namely HumanEval, HumanEval+, MBPP, DS-1000, and MultiPL-E, our models showcase outstanding performance. They consistently outperform all other open-source Code LLMs by a significant margin. Remarkably, WizardCoder 15B even surpasses the well-known closed-source LLMs, including Anthropic's Claude and Google's Bard, on the HumanEval and HumanEval+ benchmarks. Additionally, WizardCoder 34B not only achieves a HumanEval score comparable to GPT3.5 (ChatGPT) but also surpasses it on the HumanEval+ benchmark. Furthermore, our preliminary exploration highlights the pivotal role of instruction complexity in achieving exceptional coding performance.

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ICLR Conference 2024 Conference Paper

WizardLM: Empowering Large Pre-Trained Language Models to Follow Complex Instructions

Can Xu
Qingfeng Sun
Kai Zheng 0021
Xiubo Geng
Pu Zhao 0004
Jiazhan Feng
Chongyang Tao
Qingwei Lin

Training large language models (LLMs) with open-domain instruction following data brings colossal success. However, manually creating such instruction data is very time-consuming and labor-intensive. Moreover, humans may struggle to produce high-complexity instructions. In this paper, we show an avenue for creating large amounts of instruction data with varying levels of complexity using LLM instead of humans. Starting with an initial set of instructions, we use our proposed Evol-Instruct to rewrite them step by step into more complex instructions. Then, we mix all generated instruction data to fine-tune LLaMA. We call the resulting model WizardLM. Both automatic and human evaluations consistently indicate that WizardLM outperforms baselines such as Alpaca (trained from Self-Instruct) and Vicuna (trained from human-created instructions). The experimental results demonstrate that the quality of instruction-following dataset crafted by Evol-Instruct can significantly improve the performance of LLMs.

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ICLR Conference 2023 Conference Paper

HypeR: Multitask Hyper-Prompted Training Enables Large-Scale Retrieval Generalization

Zefeng Cai
Chongyang Tao
Tao Shen 0001
Can Xu
Xiubo Geng
Xin Alex Lin
Liang He 0001
Daxin Jiang

Recently, large-scale text retrieval has made impressive progress, facilitating both information retrieval and downstream knowledge-intensive tasks (e.g., open-domain QA and dialogue). With a moderate amount of data, a neural text retriever can outperform traditional methods such as BM25 by a large step. However, while being applied to out-of-domain data, the performance of a neural retriever degrades considerably. Therefore, how to enable a retriever to perform more robustly across different domains or tasks and even show strong zero-shot transfer ability is critical for building scalable IR systems. To this end, we propose HypeR, a hyper-prompted training mechanism to enable uniform retrieval across tasks of different domains. Specifically, our approach jointly trains the query encoder with a shared prompt-based parameter pool and a prompt synthesizer that dynamically composes hyper-prompt for encoding each query from different tasks or domains. Besides, to avoid the mode collapse of prompt attention distribution for different queries, we design a contrastive prompt regularization that promotes the mode of prompt attention to be aligned and uniform. Through multi-task hyper-prompted training, our retriever can master the ability to dynamically represent different types of queries and transfer knowledge across different domains and tasks. Extensive experiments show our model attains better retrieval performance across different tasks and better zero-shot transfer ability compared with various previous methods.

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ECAI Conference 2023 Conference Paper

Investigating the Learning Behaviour of In-Context Learning: A Comparison with Supervised Learning

Xindi Wang 0001
Yufei Wang 0003
Can Xu
Xiubo Geng
Bowen Zhang
Chongyang Tao
Frank Rudzicz
Robert E. Mercer

Large language models (LLMs) have shown remarkable capacity for in-context learning (ICL), where learning a new task from just a few training examples is done without being explicitly pre-trained. However, despite the success of LLMs, there has been little understanding of how ICL learns the knowledge from the given prompts. In this paper, to make progress toward understanding the learning behaviour of ICL, we train the same LLMs with the same demonstration examples via ICL and supervised learning (SL), respectively, and investigate their performance under label perturbations (i. e. , noisy labels and label imbalance) on a range of classification tasks. First, via extensive experiments, we find that gold labels have significant impacts on the downstream in-context performance, especially for large language models; however, imbalanced labels matter little to ICL across all model sizes. Second, when comparing with SL, we show empirically that ICL is less sensitive to label perturbations than SL, and ICL gradually attains comparable performance to SL as the model size increases.

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ICLR Conference 2023 Conference Paper

KnowDA: All-in-One Knowledge Mixture Model for Data Augmentation in Low-Resource NLP

Yufei Wang 0003
Jiayi Zheng
Can Xu
Xiubo Geng
Tao Shen 0001
Chongyang Tao
Daxin Jiang

This paper focuses on data augmentation for low-resource NLP tasks where the training set is limited. The existing solutions either leverage task-independent heuristic rules (e.g., Synonym Replacement) or fine-tune general-purpose pre-trained language models (e.g., GPT2) using the limited training instances to produce new synthetic data. Consequently, they have trivial task-specific knowledge and are limited to yielding low-quality synthetic data. To combat this issue, we propose Knowledge Mixture Data Augmentation Model (KnowDA), a Seq2Seq language model pretrained on a mixture of diverse NLP tasks under a novel framework of Knowledge Mixture Training (KoMT). The goal of KoMT is to condense diverse NLP task-specific knowledge into the single KnowDA model (i.e., all-in-one). The resulting KnowDA could utilize these knowledge to quickly grasp the inherent synthesis law of the target task through limited training instances. Specifically, KoMT reformulates input examples from various heterogeneous NLP tasks into a unified text-to-text format and employs denoising training objectives in different granularity to learn to reconstruct partial or complete samples. To the best of our knowledge, we are the first to attempt to apply 100+ NLP multi-task training for data augmentation. Extensive experiments show that i) the synthetic data produced by KnowDA successfully improves the performance of the strong pre-trained language models (i.e., Bert, ALBert and Deberta) by a large margin on the low-resource NLP benchmark FewGLUE, CoNLL’03 and WikiAnn; ii) KnowDA successful transfer the task knowledge to NLP tasks whose types are seen and unseen in KoMT.

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ICLR Conference 2023 Conference Paper

LexMAE: Lexicon-Bottlenecked Pretraining for Large-Scale Retrieval

Tao Shen 0001
Xiubo Geng
Chongyang Tao
Can Xu
Xiaolong Huang
Binxing Jiao
Linjun Yang
Daxin Jiang

In large-scale retrieval, the lexicon-weighting paradigm, learning weighted sparse representations in vocabulary space, has shown promising results with high quality and low latency. Despite it deeply exploiting the lexicon-representing capability of pre-trained language models, a crucial gap remains between language modeling and lexicon-weighting retrieval -- the former preferring certain or low-entropy words whereas the latter favoring pivot or high-entropy words -- becoming the main barrier to lexicon-weighting performance for large-scale retrieval. To bridge this gap, we propose a brand-new pre-training framework, lexicon-bottlenecked masked autoencoder (LexMAE), to learn importance-aware lexicon representations. Essentially, we present a lexicon-bottlenecked module between a normal language modeling encoder and a weakened decoder, where a continuous bag-of-words bottleneck is constructed to learn a lexicon-importance distribution in an unsupervised fashion. The pre-trained LexMAE is readily transferred to the lexicon-weighting retrieval via fine-tuning. On the ad-hoc retrieval benchmark, MS-Marco, it achieves 42.6% MRR@10 with 45.8 QPS for the passage dataset and 44.4% MRR@100 with 134.8 QPS for the document dataset, by a CPU machine. And LexMAE shows state-of-the-art zero-shot transfer capability on BEIR benchmark with 12 datasets.

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IJCAI Conference 2020 Conference Paper

Effective Search of Logical Forms for Weakly Supervised Knowledge-Based Question Answering

Tao Shen
Xiubo Geng
Guodong Long
Jing Jiang
Chengqi Zhang
Daxin Jiang

Many algorithms for Knowledge-Based Question Answering (KBQA) depend on semantic parsing, which translates a question to its logical form. When only weak supervision is provided, it is usually necessary to search valid logical forms for model training. However, a complex question typically involves a huge search space, which creates two main problems: 1) the solutions limited by computation time and memory usually reduce the success rate of the search, and 2) spurious logical forms in the search results degrade the quality of training data. These two problems lead to a poorly-trained semantic parsing model. In this work, we propose an effective search method for weakly supervised KBQA based on operator prediction for questions. With search space constrained by predicted operators, sufficient search paths can be explored, more valid logical forms can be derived, and operators possibly causing spurious logical forms can be avoided. As a result, a larger proportion of questions in a weakly supervised training set are equipped with logical forms, and fewer spurious logical forms are generated. Such high-quality training data directly contributes to a better semantic parsing model. Experimental results on one of the largest KBQA datasets (i. e. , CSQA) verify the effectiveness of our approach and deliver a new state-of-the-art performance.

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TIST Journal 2015 Journal Article

Learning to Rank from Noisy Data

Wenkui Ding
Xiubo Geng
Xu-Dong Zhang

Learning to rank, which learns the ranking function from training data, has become an emerging research area in information retrieval and machine learning. Most existing work on learning to rank assumes that the training data is clean, which is not always true, however. The ambiguity of query intent, the lack of domain knowledge, and the vague definition of relevance levels all make it difficult for common annotators to give reliable relevance labels to some documents. As a result, the relevance labels in the training data of learning to rank usually contain noise. If we ignore this fact, the performance of learning-to-rank algorithms will be damaged. In this article, we propose considering the labeling noise in the process of learning to rank and using a two-step approach to extend existing algorithms to handle noisy training data. In the first step, we estimate the degree of labeling noise for a training document. To this end, we assume that the majority of the relevance labels in the training data are reliable and we use a graphical model to describe the generative process of a training query, the feature vectors of its associated documents, and the relevance labels of these documents. The parameters in the graphical model are learned by means of maximum likelihood estimation. Then the conditional probability of the relevance label given the feature vector of a document is computed. If the probability is large, we regard the degree of labeling noise for this document as small; otherwise, we regard the degree as large. In the second step, we extend existing learning-to-rank algorithms by incorporating the estimated degree of labeling noise into their loss functions. Specifically, we give larger weights to those training documents with smaller degrees of labeling noise and smaller weights to those with larger degrees of labeling noise. As examples, we demonstrate the extensions for McRank, RankSVM, RankBoost, and RankNet. Empirical results on benchmark datasets show that the proposed approach can effectively distinguish noisy documents from clean ones, and the extended learning-to-rank algorithms can achieve better performances than baselines.

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NeurIPS Conference 2010 Conference Paper

A New Probabilistic Model for Rank Aggregation

Tao Qin
Xiubo Geng
Tie-Yan Liu

This paper is concerned with rank aggregation, which aims to combine multiple input rankings to get a better ranking. A popular approach to rank aggregation is based on probabilistic models on permutations, e. g. , the Luce model and the Mallows model. However, these models have their limitations in either poor expressiveness or high computational complexity. To avoid these limitations, in this paper, we propose a new model, which is defined with a coset-permutation distance, and models the generation of a permutation as a stagewise process. We refer to the new model as coset-permutation distance based stagewise (CPS) model. The CPS model has rich expressiveness and can therefore be used in versatile applications, because many different permutation distances can be used to induce the coset-permutation distance. The complexity of the CPS model is low because of the stagewise decomposition of the permutation probability and the efficient computation of most coset-permutation distances. We apply the CPS model to supervised rank aggregation, derive the learning and inference algorithms, and empirically study their effectiveness and efficiency. Experiments on public datasets show that the derived algorithms based on the CPS model can achieve state-of-the-art ranking accuracy, and are much more efficient than previous algorithms.

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