Papers with iterative algorithm
BottleSum: Unsupervised and Self-supervised Sentence Summarization using the Information Bottleneck Principle (D19-1)
Copied to clipboard
| Challenge: | Existing approaches to extractive and abstractive summarization rely on large-scale parallel corpora of input text and output summaries for direct supervision. |
| Approach: | They propose an unsupervised approach to sentence summarization using the Information Bottleneck principle. |
| Outcome: | The proposed method outperforms unsupervised models on automatic metrics and human evaluation along multiple attributes. |
Toward Automatic Discovery of a Canine Phonetic Alphabet (2025.acl-long)
Copied to clipboard
| Challenge: | a new algorithm for vocalization communication between dogs is being developed . phonetic units alone are not sufficient to constitute a "language" |
| Approach: | They propose an algorithm that produces a complete alphabet of distinct canine phonemes . the algorithm is expected to function on canines and other animal species . |
| Outcome: | The proposed algorithm produces a complete alphabet of distinct canine phoneme-like units . it is expected to work on canines and other animal species . |
Preparation of Bangla Speech Corpus from Publicly Available Audio & Text (2020.lrec-1)
Copied to clipboard
Shafayat Ahmed, Nafis Sadeq, Sudipta Saha Shubha, Md. Nahidul Islam, Muhammad Abdullah Adnan, Mohammad Zuberul Islam
| Challenge: | Automated speech recognition systems require large annotated speech corpus for training. |
| Approach: | They propose to use publicly available Bangla audiobooks and TV news recordings as input to prepare a large speech corpus with reasonable confidence. |
| Outcome: | The proposed algorithm outperforms the existing speech corpus and the existing corpus with speaker diarization and gender detection. |
Defending LLMs against Jailbreak Attacks via Template-Based ICL with a Defensive Suffix (2026.findings-acl)
Copied to clipboard
| Challenge: | State-of-the-art large language models (LLMs) are vulnerable to jailbreak attacks, such as GCG and AutoDAN. |
| Approach: | They propose to take the advances of online In-Context Learning and an offline defensive suffix and optimize them using an iterative algorithm and an online stochastic random search to identify the most effective ICL demonstrations. |
| Outcome: | The proposed method reduces attack success rate to nearly *0% while maintaining the model’s utility on benign tasks and incurring only *negligible* computational overhead. |