Papers by Xuanming Zhang

8 papers
DevEval: A Manually-Annotated Code Generation Benchmark Aligned with Real-World Code Repositories (2024.findings-acl)

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Challenge: Existing benchmarks are poorly aligned with real-world code repositories and are insufficient to evaluate the coding abilities of Large Language Models (LLMs).
Approach: They propose a repository-level benchmark named DevEval to evaluate LLMs' coding abilities in real-world code repositories.
Outcome: The proposed benchmarks show that the LLMs perform better in real-world code repositories than existing benchmarks.
Aspect-Based Sentiment Analysis as Fine-Grained Opinion Mining (2020.lrec-1)

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Challenge: a large body of research has been done on aspect-based sentiment analysis (ABSA) for almost two decades . aspect-Based sentiment analysis is a task that extracts sentiment/opinions from text in terms of targets .
Approach: They propose a meaning-preserving annotation scheme for aspect-based sentiment analysis . they then apply it to two popular ABSA datasets to examine their results .
Outcome: The proposed approach improves the state of aspect-based sentiment analysis (ABSA) by preserving the meaning of the sentiment.
SocialEval: Evaluating Social Intelligence of Large Language Models (2025.acl-long)

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Challenge: Existing work on LLMs does not address their social intelligence (SI) and their discrepancy with humans.
Approach: They propose a script-based bilingual SI benchmark that integrates outcome-oriented goal achievement evaluation and process-oriented interpersonal ability evaluation by manually crafting narrative scripts.
Outcome: The proposed model is based on a script-based bilingual evaluation paradigm that integrates outcome- and process-oriented evaluation by manually crafting narrative scripts.
DECOR: Improving Coherence in L2 English Writing with a Novel Benchmark for Incoherence Detection, Reasoning, and Rewriting (2024.emnlp-main)

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Challenge: Existing automated writing evaluation systems only detect incoherence in writing . a recent study has found that incorporating specific reasons for incohence improves the quality of rewrites .
Approach: They propose a benchmark that includes expert annotations for detecting incoherence in L2 English writing, identifying the underlying reasons, and rewriting the incoerent sentences.
Outcome: The proposed benchmark improves coherence in L2 English writing by fine-tuning models . the authors find that incorporating specific reasons improves quality of rewrites .
Budget-Aware Anytime Reasoning with LLM-Synthesized Preference Data (2026.findings-acl)

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Challenge: Recent work has explored reasoning efficiency via test-time scaling and early exit strategies.
Approach: They propose an anytime reasoning framework and the Anytime Index to improve model quality . they also propose an inference-time self-improvement method to produce better intermediate solutions .
Outcome: The proposed method improves on NaturalPlan, AIME, and GPQA datasets and improves reasoning quality and efficiency under budget constraints.
Bringing Pedagogy into Focus: Evaluating Virtual Teaching Assistants’ Question-Answering in Asynchronous Learning Environments (2025.findings-emnlp)

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Challenge: Existing assessments rely on surface-level metrics and lack sufficient grounding in educational theory . a new framework is proposed to evaluate VTAs in asynchronous learning environments .
Approach: They propose a pedagogically-oriented evaluation framework tailored to asynchronous forum discussions . they construct classifiers using expert annotations of VTA responses on a diverse set of forum posts .
Outcome: The proposed evaluation framework is rooted in learning sciences and tailored to asynchronous forum discussions.
ProLex: A Benchmark for Language Proficiency-oriented Lexical Substitution (2024.findings-acl)

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Challenge: Lexical Substitution fails to consider substitutes of equal or higher proficiency than the target word.
Approach: They propose a task to find appropriate substitutes for a given word in a context sentence but not those that are of equal or higher proficiency than the target.
Outcome: The proposed model outperforms ChatGPT by an average of 3.2% in F-score and achieves comparable results with GPT-4 on ProLex.
VarBench: Robust Language Model Benchmarking Through Dynamic Variable Perturbation (2024.findings-emnlp)

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Challenge: Recent benchmarks release only training and validation sets, keeping the test set labels closed-source.
Approach: They propose to extract variables from each test case and define a value range for each variable.
Outcome: The proposed method improves the accuracy of the evaluations on four datasets covering mathematical generation and multiple-choice tasks.

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