Papers by Benfeng Xu

Automated Creativity Evaluation for Large Language Models: A Reference-Based Approach (2025.findings-emnlp)

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Ruizhe Li, Chiwei Zhu, Benfeng Xu, Xiaorui Wang, Zhendong Mao

Challenge:	Existing methods for evaluating creativity of machine-generated texts rely on costly manual annotations or fail to align closely with human assessments.
Approach:	They propose an automated method based on the Torrance Test of Creative Writing (TTCW) .
Outcome:	The proposed method improves the alignment between LLM evaluations and human assessments.

Curriculum Learning for Natural Language Understanding (2020.acl-main)

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Benfeng Xu, Licheng Zhang, Zhendong Mao, Quan Wang, Hongtao Xie, Yongdong Zhang

Semantic Text Processing

Challenge:	Pre-trained language models can be fine tuned to perform NLU tasks in a straightforward manner.
Approach:	They propose a pretrain-finetune paradigm for natural language understanding (NLU) they propose 'a cross-trainset' approach that allows users to distinguish easy from difficult examples .
Outcome:	The proposed approach achieves significant performance improvements on a wide range of NLU tasks.

S2ynRE: Two-stage Self-training with Synthetic data for Low-resource Relation Extraction (2023.acl-long)

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Benfeng Xu, Quan Wang, Yajuan Lyu, Dai Dai, Yongdong Zhang, Zhendong Mao

Information Extraction & Text Mining

Challenge:	Existing methods for relation extraction suffer from the inadequacy of large-scale annotated data.
Approach:	They propose a framework for two-stage self-training with synthetic data for relation extraction .
Outcome:	The proposed framework is based on two-stage self-training with synthetic data . it is able to synthesize large quantities of training data and iteratively and alternately learn from synthetic and golden data together.

UniRel: Unified Representation and Interaction for Joint Relational Triple Extraction (2022.emnlp-main)

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Wei Tang, Benfeng Xu, Yuyue Zhao, Zhendong Mao, Yifeng Liu, Yong Liao, Haiyong Xie

Information Extraction & Text Mining

Challenge:	Existing approaches to extract rich correlations between entities and relations are not fully exploited by existing methods.
Approach:	They propose to unify entities and relations by jointly encoding them within a concatenated natural language sequence and unify the modeling of interactions with a proposed Interaction Map.
Outcome:	The proposed method is more efficient and efficient than existing methods and can be scaled up to 2021.

WildGraphBench: Benchmarking GraphRAG with Wild-Source Corpora (2026.findings-acl)

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Pengyu Wang, Benfeng Xu, Licheng Zhang, Shaohan Wang, Mingxuan Du, Chiwei Zhu, Zhendong Mao

Information Retrieval

Challenge:	Existing benchmarks for Graph-based Retrieval-Augmented Generation (GraphRAG) rely on short, curated passages as external knowledge, failing to adequately evaluate systems in realistic settings involving long contexts and large-scale heterogeneous documents.
Approach:	They propose a benchmark to assess GraphRAG performance in the wild using Wikipedia's unique structure where cohesive narratives are grounded in long and heterogeneous external reference documents.
Outcome:	Experiments with articles across 12 top-level topics show that GraphRAG performs better in the wild than existing methods.

From Real to Synthetic: Synthesizing Millions of Diversified and Complicated User Instructions with Attributed Grounding (2025.acl-long)

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Chiwei Zhu, Benfeng Xu, Xiaorui Wang, Zhendong Mao

Challenge:	a pursuit of diverse, complex, and large-scale instruction data is crucial for automatically aligning large language models . authors: methods that generate synthetic instructions at scale suffer from limited grounding sources . attributed grounding is a technique that can be used to align language models with human .
Approach:	They synthesize 1 million instructions using attributed grounding and a bottom-up synthesis process that leverages web documents to generate a situation, then a meaningful instruction.
Outcome:	The proposed framework achieves leading performance on benchmarks and scales with more web corpora.

FS-Researcher: Test-Time Scaling for Long-Horizon Research Tasks with File-System-Based Agents (2026.acl-long)

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Chiwei Zhu, Benfeng Xu, Mingxuan Du, Shaohan Wang, Xiaorui Wang, Zhendong Mao, Yongdong Zhang

Semantic Text Processing

Challenge:	Long trajectories in deep research often exceed model context limits, compressing token budgets for both evidence collection and report writing.
Approach:	They propose a file-system-based framework that scales deep research beyond context window . a Context Builder agent acts as a librarian and a Report Writer agent composes the final report .
Outcome:	Experiments on two open-ended benchmarks show that FS-Researcher achieves state-of-the-art report quality across different backbone models.

EmRel: Joint Representation of Entities and Embedded Relations for Multi-triple Extraction (2022.naacl-main)

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Benfeng Xu, Quan Wang, Yajuan Lyu, Yabing Shi, Yong Zhu, Jie Gao, Zhendong Mao

Information Extraction & Text Mining

Challenge:	Existing studies only explore entity representations, but propose a novel triple perspective for relation extraction.
Approach:	They propose to explicitly introduce relation representation and jointly represent it with entities to identify valid triples.
Outcome:	The proposed method is based on ablations and document-level relation extraction and joint entity and relation extraction.

KNN-Instruct: Automatic Instruction Construction with K Nearest Neighbor Deduction (2024.emnlp-main)

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Jianshang Kou, Benfeng Xu, Chiwei Zhu, Zhendong Mao

Reasoning

Challenge:	Existing methods for generating synthetic instructions for large language models suffer from stale distribution and scalability.
Approach:	They propose a method which incorporates KNN deduction to produce meaningful new instructions by summarizing and learning from existing ones.
Outcome:	The proposed method outperforms all 7B models on the LMSYS leaderboard.

Rationales Are Not Silver Bullets: Measuring the Impact of Rationales on Model Performance and Reliability (2025.findings-acl)

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Chiwei Zhu, Benfeng Xu, An Yang, Junyang Lin, Quan Wang, Chang Zhou, Zhendong Mao

Language Models

Challenge:	Existing studies have shown that training language models with rationales augmentation is beneficial, but this view does not hold consistently.
Approach:	They conduct comprehensive investigations to thoroughly inspect the impact of rationales on model performance and a novel perspective of model reliability.
Outcome:	The proposed method outperforms untrained models in several areas and provides informative regulations on the broad utilization of rationales.

On the Calibration of Large Language Models and Alignment (2023.findings-emnlp)

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Chiwei Zhu, Benfeng Xu, Quan Wang, Yongdong Zhang, Zhendong Mao

Language Models

Challenge:	Large language models are becoming more popular and are proving to be reliable . however, their reliability is often understudied due to their uncertainty and complex structure .
Approach:	They conduct a systematic examination of the calibration of aligned language models throughout the entire construction process including pretraining and alignment training.
Outcome:	The results shed light on whether popular large language models are well-calibrated and how the training process influences model calibration.