Papers by Eric Wong
Comparing Styles across Languages (2023.emnlp-main)
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| Challenge: | Communication practices vary across cultures. Inherent differences in how people think and behave influence cultural norms. |
| Approach: | They propose a framework to extract stylistic differences from multilingual language models (LMs) they use a multilingual lexica to consolidate feature importances into comparable lexical categories . |
| Outcome: | The proposed framework generates comprehensive style lexica in any language and consolidates feature importances from LMs into comparable lexical categories. |
NSF-SciFy: Mining the NSF Awards Database for Scientific Claims (2026.acl-long)
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| Challenge: | NSF-SciFy contains 2.8 million claims from 400,000 abstracts spanning all science and mathematics disciplines. |
| Approach: | They propose to use a dataset to extract scientific claims from National Science Foundation award abstracts and to use it to refine language models. |
| Outcome: | The proposed method improves non-technical abstract generation, claim extraction, and investigation proposal extraction tasks while maintaining high precision and lower recall. |
Towards Style Alignment in Cross-Cultural Translation (2025.acl-long)
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| Challenge: | Successful communication relies on the speaker’s intended style aligning with the listener’s interpreted style. |
| Approach: | They propose a method that leverages learned stylistic concepts to encourage LLM translation to appropriately convey cultural communication norms and align style. |
| Outcome: | The proposed method aims to encourage translations to convey cultural communication norms and align style. |
Flaw or Artifact? Rethinking Prompt Sensitivity in Evaluating LLMs (2025.emnlp-main)
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| Challenge: | a high prompt sensitivity has been widely accepted as a core limitation of large language models . a recent study suggests that prompt senescence may be an artifact of evaluation processes . |
| Approach: | They examine whether prompt sensitivity is an inherent weakness or an artifact of evaluation . they find that heuristic evaluation methods overlook semantically correct responses . large language models have achieved remarkable success across a wide range of tasks . |
| Outcome: | The proposed model is more robust to prompt templates than previously thought . the authors show that prompt sensitivity may be an artifact of evaluation rather than a flaw . |
Avoiding Copyright Infringement via Large Language Model Unlearning (2025.findings-naacl)
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| Challenge: | Pre-trained Large Language Models (LLMs) have demonstrated remarkable capabilities but also pose significant legal and ethical concerns. |
| Approach: | They propose a framework that unlearns copyrighted content from large language models over multiple time steps by identifying and removing specific weight updates in the model’s parameters that correspond to copyright content. |
| Outcome: | The proposed framework achieves an effective trade-off between unlearning efficacy and general-purpose language abilities, outperforming baselines. |
Probabilistic Soundness Guarantees in LLM Reasoning Chains (2025.emnlp-main)
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| Challenge: | Existing methods for detecting propagated errors in reasoning chains are inadequate . author et al. (2017) show that initial errors propagate and undermine reliability of final conclusion . |
| Approach: | They propose a framework that evaluates each reasoning step based solely on previously-verified premises and provides certified statistical guarantees of its soundness. |
| Outcome: | ARES achieves state-of-the-art performance across four benchmarks and demonstrates superior robustness on very long synthetic reasoning chains. |
Adaptively profiling models with task elicitation (2025.emnlp-main)
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| Challenge: | Language model evaluations fail to characterize consequential failure modes, forcing experts to inspect outputs and build new benchmarks. |
| Approach: | They propose a method that automatically builds new evaluations to profile model behavior. |
| Outcome: | The proposed method finds that language models fail in hundreds of tasks . it also finds that o3-mini is prone to hallucination when fabrications are repeated . |