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Baotian Hu

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12 papers

2 author rows

AAAI Conference 2026 Conference Paper

Improving Value-based Process Verifier via Low-Cost Variance Reduction

Zetian Sun
Dongfang Li
Baotian Hu
Min Zhang

Large language models (LLMs) have achieved remarkable success in a wide range of tasks. However, their reasoning capabilities, particularly in complex domains like mathematics, remain a significant challenge. Value-based process verifiers, which estimate the probability of a partial reasoning chain leading to a correct solution, are a promising approach for improving reasoning. Nevertheless, their effectiveness is often hindered by estimation error in their training annotations, a consequence of the limited number of Monte Carlo (MC) samples feasible due to the high cost of LLM inference. In this paper, we identify that the estimation error primarily arises from high variance rather than bias, and the MC estimator is a Minimum Variance Unbiased Estimator (MVUE). To address the problem, we propose the Compound Monte Carlo Sampling (ComMCS) method, which constructs an unbiased estimator by linearly combining the MC estimators from the current and subsequent steps. Theoretically, we show that our method leads to a predictable reduction in variance, while maintaining an unbiased estimation without additional LLM inference cost. We also perform empirical experiments on the MATH-500 and GSM8K benchmarks to demonstrate the effectiveness of our method. Notably, ComMCS outperforms regression-based optimization method by 2.8 points, the non-variance-reduced baseline by 2.2 points on MATH-500 on Best-of-32 sampling experiment.

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AAAI Conference 2025 Conference Paper

CMT: A Memory Compression Method for Continual Knowledge Learning of Large Language Models

Dongfang Li
Zetian Sun
Xinshuo Hu
Baotian Hu
Min Zhang

Large Language Models (LLMs) need to adapt to the continuous changes in data, tasks, and user preferences. Due to their massive size and the high costs associated with training, LLMs are not suitable for frequent retraining. However, updates are necessary to keep them in sync with rapidly evolving human knowledge. To address these challenges, this paper proposes the Compression Memory Training (CMT) method, an efficient and effective online adaptation framework for LLMs that features robust knowledge retention capabilities. Inspired by human memory mechanisms, CMT compresses and extracts information from new documents to be stored in a memory bank. When answering to queries related to these new documents, the model aggregates these document memories from the memory bank to better answer user questions. The parameters of the LLM itself do not change during training and inference, reducing the risk of catastrophic forgetting. To enhance the encoding, retrieval, and aggregation of memory, we further propose three new general and flexible techniques, including memory-aware objective, self-matching and top-k aggregation. Extensive experiments conducted on three continual learning datasets (i.e., StreamingQA, SQuAD and ArchivalQA) demonstrate that the proposed method improves model adaptability and robustness across multiple base LLMs (e.g., +4.07 EM & +4.19 F1 in StreamingQA with Llama-2-7b).

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NeurIPS Conference 2024 Conference Paper

In-Context Learning State Vector with Inner and Momentum Optimization

Dongfang Li
Zhenyu Liu
Xinshuo Hu
Zetian Sun
Baotian Hu
Min Zhang

Large Language Models (LLMs) have exhibited an impressive ability to perform In-Context Learning (ICL) from only a few examples. Recent works have indicated that the functions learned by ICL can be represented through compressed vectors derived from the transformer. However, the working mechanisms and optimization of these vectors are yet to be thoroughly explored. In this paper, we address this gap by presenting a comprehensive analysis of these compressed vectors, drawing parallels to the parameters trained with gradient descent, and introducing the concept of state vector. Inspired by the works on model soup and momentum-based gradient descent, we propose inner and momentum optimization methods that are applied to refine the state vector progressively as test-time adaptation. Moreover, we simulate state vector aggregation in the multiple example setting, where demonstrations comprising numerous examples are usually too lengthy for regular ICL, and further propose a divide-and-conquer aggregation method to address this challenge. We conduct extensive experiments using Llama-2 and GPT-J in both zero-shot setting and few-shot setting. The experimental results show that our optimization method effectively enhances the state vector and achieves the state-of-the-art performance on diverse tasks.

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NeurIPS Conference 2024 Conference Paper

SelectIT: Selective Instruction Tuning for LLMs via Uncertainty-Aware Self-Reflection

Liangxin Liu
Xuebo Liu
Derek F. Wong
Dongfang Li
Ziyi Wang
Baotian Hu
Min Zhang

Instruction tuning (IT) is crucial to tailoring large language models (LLMs) towards human-centric interactions. Recent advancements have shown that the careful selection of a small, high-quality subset of IT data can significantly enhance the performance of LLMs. Despite this, common approaches often rely on additional models or data, which increases costs and limits widespread adoption. In this work, we propose a novel approach, termed $\textit{SelectIT}$, that capitalizes on the foundational capabilities of the LLM itself. Specifically, we exploit the intrinsic uncertainty present in LLMs to more effectively select high-quality IT data, without the need for extra resources. Furthermore, we introduce a curated IT dataset, the $\textit{Selective Alpaca}$, created by applying SelectIT to the Alpaca-GPT4 dataset. Empirical results demonstrate that IT using Selective Alpaca leads to substantial model ability enhancement. The robustness of SelectIT has also been corroborated in various foundation models and domain-specific tasks. Our findings suggest that longer and more computationally intensive IT data may serve as superior sources of IT, offering valuable insights for future research in this area. Data, code, and scripts are freely available at https: //github. com/Blue-Raincoat/SelectIT.

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AAAI Conference 2024 Conference Paper

Separate the Wheat from the Chaff: Model Deficiency Unlearning via Parameter-Efficient Module Operation

Xinshuo Hu
Dongfang Li
Baotian Hu
Zihao Zheng
Zhenyu Liu
Min Zhang

Large language models (LLMs) have been widely used in various applications but are known to suffer from issues related to untruthfulness and toxicity. While parameter-efficient modules (PEMs) have demonstrated their effectiveness in equipping models with new skills, leveraging PEMs for deficiency unlearning remains underexplored. In this work, we propose a PEMs operation approach, namely Extraction-before-Subtraction (Ext-Sub), to enhance the truthfulness and detoxification of LLMs through the integration of ``expert'' PEM and ``anti-expert'' PEM. Remarkably, even anti-expert PEM possess valuable capabilities due to their proficiency in generating fabricated content, which necessitates language modeling and logical narrative competence. Rather than merely negating the parameters, our approach involves extracting and eliminating solely the deficiency capability within anti-expert PEM while preserving the general capabilities. To evaluate the effectiveness of our approach in terms of truthfulness and detoxification, we conduct extensive experiments on LLMs, encompassing additional abilities such as language modelling and mathematical reasoning. Our empirical results demonstrate that our approach effectively improves truthfulness and detoxification, while largely preserving the fundamental abilities of LLMs.

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ICML Conference 2024 Conference Paper

VisionGraph: Leveraging Large Multimodal Models for Graph Theory Problems in Visual Context

Yunxin Li
Baotian Hu
Haoyuan Shi
Wei Wang 0164
Longyue Wang
Min Zhang 0005

Large Multimodal Models (LMMs) have achieved impressive success in visual reasoning, particularly in visual mathematics. However, problem-solving capabilities in graph theory remain less explored for LMMs, despite being a crucial aspect of mathematical reasoning that requires an accurate understanding of graphical structures and multi-step reasoning on visual graphs. To step forward in this direction, we are the first to design a benchmark named VisionGraph, used to explore the capabilities of advanced LMMs in solving multimodal graph theory problems. It encompasses eight complex graph problem tasks, from connectivity to shortest path problems. Subsequently, we present a Description-Program-Reasoning (DPR) chain to enhance the logical accuracy of reasoning processes through graphical structure description generation and algorithm-aware multi-step reasoning. Our extensive study shows that 1) GPT-4V outperforms Gemini Pro in multi-step graph reasoning; 2) All LMMs exhibit inferior perception accuracy for graphical structures, whether in zero/few-shot settings or with supervised fine-tuning (SFT), which further affects problem-solving performance; 3) DPR significantly improves the multi-step graph reasoning capabilities of LMMs and the GPT-4V (DPR) agent achieves SOTA performance.

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AAAI Conference 2023 Conference Paper

Improving Biomedical Entity Linking with Cross-Entity Interaction

Zhenran Xu
Yulin Chen
Baotian Hu

Biomedical entity linking (EL) is the task of linking mentions in a biomedical document to corresponding entities in a knowledge base (KB). The challenge in biomedical EL lies in leveraging mention context to select the most appropriate entity among possible candidates. Although some EL models achieve competitive results by retrieving candidate entities and then exploiting context to re-rank them, these re-ranking models concatenate mention context with one candidate at a time. They lack fine-grained interaction among candidates, and potentially cannot handle ambiguous mentions when facing candidates both with high lexical similarity. We cope with this issue using a re-ranking model based on prompt tuning, which represents mention context and all candidates at once, letting candidates in comparison attend to each other. We also propose a KB-enhanced self-supervised pretraining strategy. Instead of large-scale pretraining on biomedical EL data in previous work, we use masked language modeling with synonyms from KB. Our method achieves state-of-the-art results on 3 biomedical EL datasets: NCBI disease, BC5CDR and COMETA, showing the effectiveness of cross-entity interaction and KB-enhanced pretraining strategy. Code is available at https://github.com/HITsz-TMG/Prompt-BioEL.

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IJCAI Conference 2023 Conference Paper

PPAT: Progressive Graph Pairwise Attention Network for Event Causality Identification

Zhenyu Liu
Baotian Hu
Zhenran Xu
Min Zhang

Event Causality Identification (ECI) aims to identify the causality between a pair of event mentions in a document, which is composed of sentence-level ECI (SECI) and document-level ECI (DECI). Previous work applies various reasoning models to identify the implicit event causality. However, they indiscriminately reason all event causality in the same way, ignoring that most inter-sentence event causality depends on intra-sentence event causality to infer. In this paper, we propose a progressive graph pairwise attention network (PPAT) to consider the above dependence. PPAT applies a progressive reasoning strategy, as it first predicts the intra-sentence event causality, and then infers the more implicit inter-sentence event causality based on the SECI result. We construct a sentence boundary event relational graph, and PPAT leverages a simple pairwise attention mechanism, which attends to different reasoning chains on the graph. In addition, we propose a causality-guided training strategy for assisting PPAT in learning causality-related representations on every layer. Extensive experiments show that our model achieves state-of-the-art performance on three benchmark datasets (5. 5%, 2. 2% and 4. 5% F1 gains on EventStoryLine, MAVEN-ERE and Causal-TimeBank). Code is available at https: //github. com/HITsz-TMG/PPAT.

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IJCAI Conference 2022 Conference Paper

Enhancing Entity Representations with Prompt Learning for Biomedical Entity Linking

Tiantian Zhu
Yang Qin
Qingcai Chen
Baotian Hu
Yang Xiang

Biomedical entity linking aims to map mentions in biomedical text to standardized concepts or entities in a curated knowledge base (KB) such as Unified Medical Language System (UMLS). The latest research tends to solve this problem in a unified framework solely based on surface form matching between mentions and entities. Specifically, these methods focus on addressing the variety challenge of the heterogeneous naming of biomedical concepts. Yet, the ambiguity challenge that the same word under different contexts may refer to distinct entities is usually ignored. To address this challenge, we propose a two-stage linking algorithm to enhance the entity representations based on prompt learning. The first stage includes a coarser-grained retrieval from a representation space defined by a bi-encoder that independently embeds the mention and entity’s surface forms. Unlike previous one-model-fits-all systems, each candidate is then re-ranked with a finer-grained encoder based on prompt-tuning that utilizes the contextual information. Extensive experiments show that our model achieves promising performance improvements compared with several state-of-the-art techniques on the largest biomedical public dataset MedMentions and the NCBI disease corpus. We also observe by cases that the proposed prompt-tuning strategy is effective in solving both the variety and ambiguity challenges in the linking task.

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AAAI Conference 2022 Conference Paper

Unifying Model Explainability and Robustness for Joint Text Classification and Rationale Extraction

Dongfang Li
Baotian Hu
Qingcai Chen
Tujie Xu
Jingcong Tao
Yunan Zhang

Recent works have shown explainability and robustness are two crucial ingredients of trustworthy and reliable text classi- ﬁcation. However, previous works usually address one of two aspects: i) how to extract accurate rationales for explainability while being beneﬁcial to prediction; ii) how to make the predictive model robust to different types of adversarial attacks. Intuitively, a model that produces helpful explanations should be more robust against adversarial attacks, because we cannot trust the model that outputs explanations but changes its prediction under small perturbations. To this end, we propose a joint classiﬁcation and rationale extraction model named AT-BMC. It includes two key mechanisms: mixed Adversarial Training (AT) is designed to use various perturbations in discrete and embedding space to improve the model’s robustness, and Boundary Match Constraint (BMC) helps to locate rationales more precisely with the guidance of boundary information. Performances on benchmark datasets demonstrate that the proposed AT-BMC outperforms baselines on both classiﬁcation and rationale extraction by a large margin. Robustness analysis shows that the proposed AT-BMC decreases the attack success rate effectively by up to 69%. The empirical results indicate that there are connections between robust models and better explanations.

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AAAI Conference 2018 Conference Paper

Learning to Extract Coherent Summary via Deep Reinforcement Learning

Yuxiang Wu
Baotian Hu

Coherence plays a critical role in producing a high-quality summary from a document. In recent years, neural extractive summarization is becoming increasingly attractive. However, most of them ignore the coherence of summaries when extracting sentences. As an effort towards extracting coherent summaries, we propose a neural coherence model to capture the cross-sentence semantic and syntactic coherence patterns. The proposed neural coherence model obviates the need for feature engineering and can be trained in an end-to-end fashion using unlabeled data. Empirical results show that the proposed neural coherence model can efﬁciently capture the cross-sentence coherence patterns. Using the combined output of the neural coherence model and ROUGE package as the reward, we design a reinforcement learning method to train a proposed neural extractive summarizer which is named Reinforced Neural Extractive Summarization (RNES) model. The RNES model learns to optimize coherence and informative importance of the summary simultaneously. The experimental results show that the proposed RNES outperforms existing baselines and achieves state-of-the-art performance in term of ROUGE on CNN/Daily Mail dataset. The qualitative evaluation indicates that summaries produced by RNES are more coherent and readable.

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NeurIPS Conference 2014 Conference Paper

Convolutional Neural Network Architectures for Matching Natural Language Sentences

Baotian Hu
Zhengdong Lu
Hang Li
Qingcai Chen

Semantic matching is of central importance to many natural language tasks \cite{bordes2014semantic, RetrievalQA}. A successful matching algorithm needs to adequately model the internal structures of language objects and the interaction between them. As a step toward this goal, we propose convolutional neural network models for matching two sentences, by adapting the convolutional strategy in vision and speech. The proposed models not only nicely represent the hierarchical structures of sentences with their layer-by-layer composition and pooling, but also capture the rich matching patterns at different levels. Our models are rather generic, requiring no prior knowledge on language, and can hence be applied to matching tasks of different nature and in different languages. The empirical study on a variety of matching tasks demonstrates the efficacy of the proposed model on a variety of matching tasks and its superiority to competitor models.

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