---
title: "Zhengxuan Wu — all works — Elamur Research"
description: "All 31 works of Zhengxuan Wu in the Elamur Research library, the most cited first: our summaries where we have them, else the original."
canonical: "https://research.elamur.ai/free/en/authors/zhengxuan-wu/works/"
---
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# Zhengxuan Wu — all works

31 works · [← Zhengxuan Wu](https://md.research.elamur.ai/free/en/authors/zhengxuan-wu/)

1. **[Dynabench: Rethinking Benchmarking in NLP ↗](https://aclanthology.org/2021.naacl-main.324/) Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies · no summary yet**Douwe Kiela et al.*2021 · 482 citations*
2. **[MQuAKE: Assessing Knowledge Editing in Language Models via Multi-Hop Questions ↗](https://aclanthology.org/2023.emnlp-main.971/) Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing · no summary yet**Zexuan Zhong et al.*2023 · 199 citations*
3. **[DynaSent: A Dynamic Benchmark for Sentiment Analysis ↗](https://aclanthology.org/2021.acl-long.186/) Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers) · no summary yet**Christopher Potts et al.*2021 · 103 citations*
4. [**Mapping the Increasing Use of LLMs in Scientific Papers arXiv:2404.01268**Weixin Liang, Yaohui Zhang, Zhengxuan Wu, et al.*2024 · 89 citations*](https://md.research.elamur.ai/free/en/papers/liang-2024-mapping-increasing-use-llms/)
5. **[Quantifying large language model usage in scientific papers ↗](https://doi.org/10.1038/s41562-025-02273-8) Nature Human Behaviour · no summary yet**Weixin Liang et al.*2025 · 81 citations*
6. [**Finding Alignments Between Interpretable Causal Variables and Distributed Neural Representations arXiv:2303.02536**Atticus Geiger, Zhengxuan Wu, Christopher Potts, et al.*2023 · 81 citations*](https://md.research.elamur.ai/free/en/papers/geiger-2023-finding-alignments-between-interpretable-causal/)
7. **[Interpretability at Scale: Identifying Causal Mechanisms in Alpaca](https://md.research.elamur.ai/free/en/papers/wu-2023-interpretability-scale-identifying-causal-mechanisms/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Zhengxuan Wu, Atticus Geiger, Thomas Icard, et al.*2023 · 81 citations*
8. **[ReFT: Representation Finetuning for Language Models](https://md.research.elamur.ai/free/en/papers/wu-2024-reft-representation-finetuning-language-models/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Zhengxuan Wu, Aryaman Arora, Zheng Wang, et al.*2024 · 74 citations*
9. **[Causal Abstraction: A Theoretical Foundation for Mechanistic Interpretability ↗](https://arxiv.org/abs/2301.04709) arXiv:2301.04709 · no summary yet**Atticus Geiger et al.*2023 · 65 citations*
10. **[AxBench: Steering LLMs? Even Simple Baselines Outperform Sparse Autoencoders ↗](https://arxiv.org/abs/2501.17148) arXiv:2501.17148 · no summary yet**Zhengxuan Wu et al.*2025 · 59 citations*
11. **[Inducing Causal Structure for Interpretable Neural Networks ↗](https://arxiv.org/abs/2112.00826) arXiv (Cornell University) · no summary yet**Atticus Geiger et al.*2021 · 53 citations*
12. **[RAVEL: Evaluating Interpretability Methods on Disentangling Language Model Representations ↗](https://aclanthology.org/2024.acl-long.470/) Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · no summary yet**Jing Huang et al.*2024 · 35 citations*
13. **[Rigorously Assessing Natural Language Explanations of Neurons](https://md.research.elamur.ai/free/en/papers/huang-2023-rigorously-assessing-natural-language-explanations/) [BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP](https://md.research.elamur.ai/free/en/venues/blackboxnlp/)**Jing Huang, Atticus Geiger, Karel D'Oosterlinck, et al.*2023 · 30 citations*
14. **[CEBaB: Estimating the Causal Effects of Real-World Concepts on NLP Model Behavior ↗](https://arxiv.org/abs/2205.14140) arXiv (Cornell University) · no summary yet**Eldar David Abraham et al.*2022 · 29 citations*
15. **[Causal Proxy Models for Concept-Based Model Explanations ↗](https://arxiv.org/abs/2209.14279) arXiv (Cornell University) · no summary yet**Zhengxuan Wu et al.*2022 · 27 citations*
16. **[pyvene: A Library for Understanding and Improving PyTorch Models via Interventions ↗](https://arxiv.org/abs/2403.07809) arXiv (Cornell University) · no summary yet**Zhengxuan Wu et al.*2024 · 26 citations*
17. **[Causal Distillation for Language Models ↗](https://aclanthology.org/2022.naacl-main.318/) Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies · no summary yet**Zhengxuan Wu et al.*2022 · 25 citations*
18. **[Inducing Character-level Structure in Subword-based Language Models with Type-level Interchange Intervention Training ↗](https://aclanthology.org/2023.findings-acl.770/) Findings of the Association for Computational Linguistics: ACL 2023 · no summary yet**Jing Huang et al.*2023 · 21 citations*
19. **[ReCOGS: How Incidental Details of a Logical Form Overshadow an Evaluation of Semantic Interpretation ↗](https://aclanthology.org/2023.tacl-1.96/) Transactions of the Association for Computational Linguistics · no summary yet**Zhengxuan Wu, Christopher D. Manning, Christopher Potts*2023 · 19 citations*
20. **[ReaSCAN: Compositional Reasoning in Language Grounding ↗](https://arxiv.org/abs/2109.08994) arXiv (Cornell University) · no summary yet**Zhengxuan Wu et al.*2021 · 19 citations*
21. **[ZeroC: A Neuro-Symbolic Model for Zero-Shot Concept Recognition and Acquisition at Inference Time ↗](https://doi.org/10.52202/068431-0714) neural information processing systems · no summary yet**Tailin Wu et al.*2022 · 11 citations*
22. **[Improved Representation Steering for Language Models ↗](https://doi.org/10.52202/085713-5364) neural information processing systems · no summary yet**Zhengxuan Wu et al.*2025 · 9 citations*
23. **[Identifying the Limits of Cross-Domain Knowledge Transfer for Pretrained Models ↗](https://aclanthology.org/2022.repl4nlp-1.11/) Proceedings of the 7th Workshop on Representation Learning for NLP · no summary yet**Zhengxuan Wu, Nelson F. Liu, Christopher Potts*2022 · 8 citations*
24. **[\textttpyvene: A Library for Understanding and Improving PyTorch Models via Interventions ↗](https://aclanthology.org/2024.naacl-demo.16/) Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 3: System Demonstrations) · no summary yet**Zhengxuan Wu et al.*2024 · 7 citations*
25. **[A Reply to Makelov et al. (2023)'s "Interpretability Illusion" Arguments ↗](https://arxiv.org/abs/2401.12631) arXiv (Cornell University) · no summary yet**Zhengxuan Wu et al.*2024 · 5 citations*
26. **[Language Model Circuits Are Sparse in the Neuron Basis ↗](https://arxiv.org/abs/2601.22594) arXiv (Cornell University) · no summary yet**Aryaman Arora et al.*2026 · 4 citations*
27. **[HyperSteer: Activation Steering at Scale with Hypernetworks ↗](https://arxiv.org/abs/2506.03292) arXiv (Cornell University) · no summary yet**Sun, Jiuding et al.*2025 · 4 citations*
28. **[ADAG: Automatically Describing Attribution Graphs ↗](https://arxiv.org/abs/2604.07615) arXiv (Cornell University) · no summary yet**Aryaman Arora et al.*2026 · 1 citation*
29. **[PreFT: Prefill-only finetuning for efficient inference ↗](https://arxiv.org/abs/2605.14217) arXiv (Cornell University) · no summary yet**Andrew Lanpouthakoun et al.*2026*
30. **[Generalization Dynamics of LM Pre-training ↗](https://arxiv.org/abs/2609.33150) arXiv (Cornell University) · no summary yet**Jiaxin Wen et al.*2026*
31. **[Mechanistic interpretability and control for language models ↗](https://doi.org/10.25740/fg704nm2917) Stanford Digital Repository · no summary yet**Zhengxuan Wu et al.*2026*