Papers by Junlin Wang
AllenNLP Interpret: A Framework for Explaining Predictions of NLP Models (D19-3)
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| Challenge: | Existing interpretation codebases make it difficult to apply these methods to new models and tasks. |
| Approach: | They propose a framework for interpreting NLP models that provides explanations for specific models. |
| Outcome: | The proposed framework provides interpretation primitives for any AllenNLP model and task, a suite of built-in interpretation methods, and a library of front-end visualization components. |
GAP-Gen: Guided Automatic Python Code Generation (2023.eacl-srw)
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| Challenge: | Several previous approaches convert a sentence into a formal statement by mapping verbs to functions in the formal language. |
| Approach: | They propose a Guided Automatic Python Code Generation method based on Python syntactic constraints and semantic constraints. |
| Outcome: | The proposed method achieves better results on automatic Python code generation task than previous methods. |
Multimodal Reasoning with Multimodal Knowledge Graph (2024.acl-long)
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| Challenge: | Multimodal reasoning with large language models (LLMs) often suffers from hallucinations and the presence of deficient or outdated knowledge within LLMs. |
| Approach: | They propose a multimodal reasoning method that leverages multimodal knowledge graphs to learn rich and semantic knowledge across modalities. |
| Outcome: | The proposed method outperforms state-of-the-art models on multimodal question answering and multimodal analogy reasoning tasks while training on only a small fraction of parameters. |
Gradient-based Analysis of NLP Models is Manipulable (2020.findings-emnlp)
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| Challenge: | Recent work has shown that explanation techniques can be unstable and can be manipulated to hide the actual reasoning behind the predictions of NLP models. |
| Approach: | They propose to merge a BERT-based sentiment classifier with a Facade Model that overwhelms the gradients without affecting the predictions. |
| Outcome: | The proposed model overwhelms the gradients without affecting the predictions on a variety of NLP tasks, such as sentiment analysis, NLI, and QA. |
AMO-Bench: Large Language Models Still Struggle in High School Math Competitions (2026.findings-acl)
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Junlin Liu, Shengnan An, Shuang Zhou, Dan Ma, Yehao Lin, Xinxuan Lv, Xuanlin Wang, Xiaoyu Li, Ziwen Wang, Xuezhi Cao, Xunliang Cai
| Challenge: | Existing benchmarks for mathematical reasoning are becoming less effective due to performance saturation. |
| Approach: | They propose to use a mathematical reasoning benchmark with Olympiad difficulty to evaluate top-tier LLMs. |
| Outcome: | The proposed benchmarks are cross-validated by experts to meet IMO difficulty standards and entirely original problems to prevent performance leakages from data memorization. |
NeuroComparatives: Neuro-Symbolic Distillation of Comparative Knowledge (2024.findings-naacl)
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| Challenge: | Comparative knowledge is an essential component of our world knowledge, yet understudied in prior literature. |
| Approach: | They propose a framework for comparative knowledge distillation overgenerated from language models . they use a corpus of 8.8M comparisons over 1.74M entity pairs to acquire comparative information . |
| Outcome: | The proposed framework acquires comparative knowledge between everyday objects . human evaluations show that it outperforms existing resources in terms of validity . |
ReCaLL: Membership Inference via Relative Conditional Log-Likelihoods (2024.emnlp-main)
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| Challenge: | ReCaLL (Relative Conditional Log-Likelihood) is a membership inference attack that can detect LLMs’ pretraining data by leveraging their conditional language modeling capabilities. |
| Approach: | They propose a membership inference attack to detect LLMs’ pretraining data by leveraging their conditional language modeling capabilities. |
| Outcome: | The proposed model achieves state-of-the-art performance on the WikiMIA dataset, even with random and synthetic prefixes, and can be further improved using an ensemble approach. |
Multi-Modality Expansion and Retention for LLMs through Parameter Merging and Decoupling (2025.acl-long)
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Junlin Li, Guodong Du, Jing Li, Sim Kuan Goh, Wenya Wang, Yequan Wang, Fangming Liu, Ho-Kin Tang, Saleh Alharbi, Daojing He, Min Zhang
| Challenge: | Large Language Models (LLMs) are a cornerstone in artificial intelligence due to their exceptional performance. |
| Approach: | They propose a training-free approach that integrates existing MLLMs for effective multimodal expansion while retaining their original performance. |
| Outcome: | The proposed approach can expand LLMs' multimodal capabilities while retaining original performance. |
From Personal to Collective: On the Role of Local and Global Knowledge in LLM Personalization (2026.findings-acl)
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| Challenge: | Large language models (LLMs) are the backbone of modern AI systems supporting a wide range of applications. |
| Approach: | They propose a Local–Global knowledge framework that augments user-specific signals with a global knowledge encoding collective behavioral trends. |
| Outcome: | The proposed framework outperforms existing methods on five personalization benchmarks. |
RLHFPoison: Reward Poisoning Attack for Reinforcement Learning with Human Feedback in Large Language Models (2024.acl-long)
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| Challenge: | Recent advances in Large Language Models (LLMs) have significantly enhanced the capabilities in natural language processing. |
| Approach: | They propose a method to poison large language models by using annotators to rank a set of collected responses to generate longer tokens. |
| Outcome: | The proposed method can generate longer tokens without harming the original safety alignment performance. |
Raccoon: Prompt Extraction Benchmark of LLM-Integrated Applications (2024.findings-acl)
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| Challenge: | Recent advances in Large Language Models (LLMs) have markedly shifted the landscape of AI, enabling these models to tackle complex, real-world tasks through natural language instructions. |
| Approach: | They propose a benchmark which evaluates a model's susceptibility to prompt extraction attacks by employing a dual approach to evaluate the effectiveness of existing defenses and the resilience of the models. |
| Outcome: | The proposed benchmark assesses models under both defenseless and defended scenarios, employing a dual approach to evaluate the effectiveness of existing defenses and the resilience of the models. |
Reasoning in Token Economies: Budget-Aware Evaluation of LLM Reasoning Strategies (2024.emnlp-main)
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| Challenge: | Existing evaluations that focus on performance metrics miss a key factor: increased effectiveness due to additional compute. |
| Approach: | They propose to incorporate the compute budget into evaluations to provide a more informative comparison that takes into account both performance metrics and computational cost. |
| Outcome: | The proposed framework outperforms reasoning strategies when they use comparable compute resources. |
Vocabulary Hijacking in LVLMs: Unveiling Critical Attention Heads by Excluding Inert Tokens to Mitigate Hallucination (2026.acl-long)
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| Challenge: | Large Vision-Language Models (LVLMs) are capable of processing visual inputs, but are susceptible to hallucinations. |
| Approach: | They propose a method to localize and localize specific visual tokens, which are defined as **Inert Tokens**, across layers, revealing a rigid semantic collapse. |
| Outcome: | The proposed approach reduces the likelihood of LVLMs being hijacked by visual inputs while maintaining general capabilities. |
Adversarial Math Word Problem Generation (2024.findings-emnlp)
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| Challenge: | Large language models (LLMs) have revolutionized the educational landscape due to the great improvements in their natural language generation and problem-solving capabilities. |
| Approach: | They propose a cost-effective approach to attack large language models using abstract syntax trees to generate adversarial examples that preserve the structure and difficulty of the original questions aimed for assessment. |
| Outcome: | The proposed approach significantly degrades students' math problem-solving ability on open- and closed-source LLMs. |