| Challenge: | Existing methods for linguistic steganography are vulnerable to automated detection. |
| Approach: | They propose an encoding algorithm with improved near-imperceptible guarantees based on implicit assumptions on statistical behaviors of fluent text. |
| Outcome: | The proposed algorithm improves on existing steganographic systems with near-imperceptible guarantees. |
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Frustratingly Easy Edit-based Linguistic Steganography with a Masked Language Model (2021.naacl-main)
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| Challenge: | linguistic steganography is the practice of concealing a secret message in some cover data such that an eavesdropper is not even aware of the existence of the secret message. |
| Approach: | They propose to use edit-based linguistic steganography to generate genuine-looking texts by using a masked language model that eliminates painstaking rule construction and has a high payload capacity. |
| Outcome: | The proposed method eliminates painstaking rule construction and has a high payload capacity for an edit-based model. |
Near-imperceptible Neural Linguistic Steganography via Self-Adjusting Arithmetic Coding (2020.emnlp-main)
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| Challenge: | Linguistic steganography studies how to hide secret messages in natural language cover texts. |
| Approach: | They propose a method which encodes secret messages using self-adjusting arithmetic coding based on a neural language model. |
| Outcome: | The proposed method outperforms the state-of-the-art methods on four datasets by 15.3% and 38.9% in terms of bits/word and KL metrics. |
Addressing Segmentation Ambiguity in Neural Linguistic Steganography (2022.aacl-short)
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| Challenge: | Recent studies on neural linguistic steganography ignore the fact that the sender must detokenize cover texts to avoid arousing the eavesdropper’s suspicion. |
| Approach: | They propose to decode a secret message in a way that does not arouse suspicion of the eavesdropper. |
| Outcome: | The proposed techniques are applicable to languages without explicit word boundaries. |
Addressing Tokenization Inconsistency in Steganography and Watermarking Based on Large Language Models (2025.emnlp-main)
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| Challenge: | Large language models have improved the capacities and efficiency of text generation. |
| Approach: | They propose a method for tokenization inconsistency and a watermarking technique to address this problem. |
| Outcome: | The proposed methods improve fluency, imperceptibility, and anti-steganalysis capacity. |
Efficient Provably Secure Linguistic Steganography via Range Coding (2026.acl-long)
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| Challenge: | Linguistic steganography is a promising field in safeguarding information . previous methods have achieved perfect imperceptibility but at the expense of embedding capacity. |
| Approach: | They propose to use a classical entropy coding method to achieve secure steganography . they propose to employ a rotation mechanism to achieve embedding efficiency . |
| Outcome: | The proposed method outperforms existing methods in embedding capacity and embeddability. |
Look Who’s Talking Now: Covert Channels From Biased LLMs (2024.findings-emnlp)
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| Challenge: | steganography encodes hidden messages into model-generated tokens . tradeoff between how much hidden information can be introduced and how much the model can be perturbed is important . |
| Approach: | They propose to use large language model-based steganography to encode hidden messages into model-generated tokens. |
| Outcome: | The proposed techniques are nearly optimal under a practical but difficult set of constraints . the proposed techniques ensure that only someone with the appropriate decoding key can access the hidden information . |
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations) (2023.acl-demo)
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| Challenge: | 58 papers were selected for inclusion in the program, while a small number received only two reviews. |
| Approach: | the 61st Annual Meeting of the Association for Computational Linguistics (ACL 2023) will be held in london from July 9-14, 2023 . 58 submissions were selected for inclusion in the program, with an acceptance rate of 37%) |
| Outcome: | the system demonstration track received a record number of submissions . 58 papers were selected for inclusion in the program . |
Proceedings of the First Workshop on Commonsense Inference in Natural Language Processing (D19-60)
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| Challenge: | Workshop on Commonsense Inference in Natural Language Processing focuses on commonsense knowledge representation and application in NLP tasks. |
| Approach: | COIN is a workshop on commonsense inference in natural language processing . workshop included two shared tasks on reading comprehension using commonsensense knowledge . |
| Outcome: | the workshop focused on modeling commonsense knowledge and commonsensing in natural language processing tasks. |
Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations) (2026.acl-demo)
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| Challenge: | ACL 2026 System Demonstration Track accepted 85 papers . one paper received Best Demo award . |
| Approach: | the 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026) took place from July 2-7, 2026 in San Diego, California. |
| Outcome: | the ACL 2026 System Demonstration Track accepted 85 papers based on the submitted reviews . one paper received the best demo award: The olmOCR Project: Building Fully Open OCR using VLMs . |
Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations) (2025.acl-demo)
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| Challenge: | ACL 2025 System Demonstration Track accepted 64 papers based on reviews . short-listed 7 papers for Best System Demo award . |
| Approach: | the ACL 2025 System Demonstration Track is a conference for papers describing system demonstrations . the track received a record 187 submissions, of which 178 papers were valid with required materials . |
| Outcome: | the ACL 2025 System Demonstration Track received 187 submissions . 178 papers were valid with required materials . |