Papers with IA
Segmenting Numerical Substitution Ciphers (2022.emnlp-main)
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| Challenge: | Existing methods for deciphering historical substitution ciphers are difficult to crack . cipheries that are not segmented are still difficult to deciphere . |
| Approach: | They propose automatic methods to segment historical substitution ciphers using BPE and unigram language models. |
| Outcome: | The proposed methods achieve an average segmentation error of 2% on 100 monoalphabetic ciphers and 27% on 3 real historical homophonic cipheries. |
Keep the Primary, Rewrite the Secondary: A Two-Stage Approach for Paraphrase Generation (2021.findings-acl)
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| Challenge: | Existing approaches to generate paraphrases are decomposable, but some use a sequence-to-sequence model to generate each word in a uniform way. |
| Approach: | They propose a framework for identification then aggregation of input tokens and a custom decoder to generate paraphrases. |
| Outcome: | The proposed framework outperforms previous studies on two benchmark datasets and generates paraphrases in interpretable and controllable way. |
Exploring the Vulnerability of the Content Moderation Guardrail in Large Language Models via Intent Manipulation (2025.findings-emnlp)
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| Challenge: | Prior work has shown that intent detection enhances LLMs’ moderation guardrails, but the robustness of these guardrail mechanisms under malicious manipulations remains under-explored. |
| Approach: | They propose a two-stage intent-based prompt-refinement framework that first transforms harmful inquiries into structured outlines and further reframes them into declarative-style narratives. |
| Outcome: | The proposed framework outperforms several cutting-edge jailbreak methods and evades even advanced Intent Analysis (IA) and Chain-of-Thought (CoT)-based defenses. |
From Insights to Actions: The Impact of Interpretability and Analysis Research on NLP (2024.emnlp-main)
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| Challenge: | Interpretability and analysis (IA) research is a growing subfield within NLP . a criticism of this work is that it lacks actionable insights and therefore has little impact on NLP. |
| Approach: | They propose to quantify the impact of interpretation and analysis research on NLP . they use citation graphs and a survey to find out what is missing in IA research . |
| Outcome: | The proposed study shows that IA research is well-cited outside of IA and central in the NLP citation graph. |
Intention Analysis Makes LLMs A Good Jailbreak Defender (2025.coling-main)
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| Challenge: | Existing methods to align large language models with human values overlook the intrinsic nature of jailbreaks, which limits their effectiveness in complex scenarios. |
| Approach: | They propose a simple yet highly effective defense strategy, i.e., Intention Analysis (IA). They show that IA suppresses LLM’s tendency to follow jailbreak prompts, thereby enhancing safety. |
| Outcome: | The proposed strategy reduces harmfulness of LLMs and outperforms GPT-3.5 in attack success rate. |
Adaptive Immune-based Sound-Shape Code Substitution for Adversarial Chinese Text Attacks (2024.emnlp-main)
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| Challenge: | Existing text attack methods are designed for English text, but robust implementation of Chinese text is understudied. |
| Approach: | They propose an adaptive immune-based sound-shape code algorithm for Chinese text attacks . they leverage the Sound-Shape Code to generate natural substitutions . |
| Outcome: | The proposed algorithm produces high-quality Chinese adversarial examples . it can reduce duplication of population and improve search ability . |
Identification of Multiple Logical Interpretations in Counter-Arguments (2025.emnlp-main)
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Wenzhi Wang, Paul Reisert, Shoichi Naito, Naoya Inoue, Machi Shimmei, Surawat Pothong, Jungmin Choi, Kentaro Inui
| Challenge: | Counter-arguments (CAs) are a good way to improve learners' critical thinking skills . however, it is difficult to provide every learner tailored feedback due to limited human resources and heavy workloads. |
| Approach: | They propose to annotate a dataset of 134 CAs annotated with 13 logical predicate questions and train a model with Reinforcement Learning with Verifiable Rewards to identify multiple logical interpretations. |
| Outcome: | The proposed model performs on par with larger proprietary models. |
Revealing the Parametric Knowledge of Language Models: A Unified Framework for Attribution Methods (2024.acl-long)
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| Challenge: | Language Models acquire parametric knowledge from their training process, embedding it within their weights. |
| Approach: | They propose a new evaluation framework to quantify and compare the knowledge revealed by Instance Attribution and Neuron Attributions. |
| Outcome: | The proposed evaluation framework compares the knowledge revealed by IA and NA with that of neuron attribution methods. |
Role of Language Relatedness in Multilingual Fine-tuning of Language Models: A Case Study in Indo-Aryan Languages (2021.emnlp-main)
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| Challenge: | A study of multilingual fine-tuning yields better performance on downstream NLP applications . low resource languages such as Oriya and Punjabi are found to be the largest beneficiaries of multi-lingual fine tuning. |
| Approach: | They propose to leverage the relatedness of languages that belong to the same family in NLP models by multilingual fine-tuning. |
| Outcome: | The proposed approach improves performance on downstream NLP tasks by 15% compared to monolingual fine-tuning. |
IAEval: A Comprehensive Evaluation of Instance Attribution on Natural Language Understanding (2023.findings-emnlp)
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| Challenge: | Instance attribution (IA) aims to identify the training instances leading to the prediction of a test example. |
| Approach: | They propose a systematic and comprehensive evaluation scheme covering four significant requirements: sufficiency, completeness, stability and plausibility. |
| Outcome: | The proposed evaluation scheme covers four significant requirements: sufficiency, completeness, stability and plausibility. |
Can Input Attributions Explain Inductive Reasoning in In-Context Learning? (2025.findings-acl)
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| Challenge: | interpreting the internal process of neural models has long been a challenge . despite rapid progress, there are still questions bridging the IA and MI eras . |
| Approach: | They propose to use input attribution methods to interpret in-context learning . they find that a certain simple IA method works best in large models . |
| Outcome: | The proposed method is the best for interpreting LLM-based ICL, but the larger the model, the harder it is to interpret it. |
Conflict-Aware Memory for Embodied Agents: Enhancing Vector Data Quality via Detection Rules (2026.acl-long)
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| Challenge: | Embodied agents have successfully leveraged large language models (LLMs) to better transform human instructions and images into executable task plans. |
| Approach: | They propose Conflict Detection Rules to identify and manage data quality issues in vector knowledge bases and correct the index structure. |
| Outcome: | Experimental results show that planners with Conflict Detection Rules exceed the basic LLM planner by 15.25% and 14.25% in grammatical accuracy (GA) and interpretation accuracy (IA) on average. |