Papers with SDC
Product Description and QA Assisted Self-Supervised Opinion Summarization (2024.findings-naacl)
Copied to clipboard
Tejpalsingh Siledar, Rupasai Rangaraju, Sankara Muddu, Suman Banerjee, Amey Patil, Sudhanshu Singh, Muthusamy Chelliah, Nikesh Garera, Swaprava Nath, Pushpak Bhattacharyya
| Challenge: | Existing methods to generate opinion summarization without supervised training data are limited due to the lack of additional sources. |
| Approach: | They propose a synthetic dataset creation strategy that leverages reviews and additional sources to generate a pseudo-summary. |
| Outcome: | The proposed approach achieves 14.5% improvement in ROUGE-1 F1 over existing models. |
Shami: A Corpus of Levantine Arabic Dialects (L18-1)
Copied to clipboard
| Challenge: | Modern Standard Arabic is the official written language used in education and media . however, the spoken language varies widely across the Arab world . |
| Approach: | They construct a levantine dialect corpus covering data from four dialects spoken in four countries . they describe rules for pre-processing without affecting the meaning so that it is processable by NLP tools. |
| Outcome: | The proposed corpus is larger than existing corpora in terms of size, words and vocabularies. |
Synthesize, if you do not have: Effective Synthetic Dataset Creation Strategies for Self-Supervised Opinion Summarization in E-commerce (2023.findings-emnlp)
Copied to clipboard
Tejpalsingh Siledar, Suman Banerjee, Amey Patil, Sudhanshu Singh, Muthusamy Chelliah, Nikesh Garera, Pushpak Bhattacharyya
| Challenge: | Existing approaches to generate general and aspect-specific opinion summarization are limited due to their reliance on human-specified aspects and seed words. |
| Approach: | They propose synthetic dataset creation approaches for general and aspect-specific opinion summarization . general opinion summaries struggle to generate faithful to the input reviews, they say . aspect- specific opinion summarisation models are limited due to reliance on human-specified aspects . |
| Outcome: | The proposed approach outperforms existing models on three e-commerce test sets on general and aspect-specific opinion summarization. |
Understanding Silent Data Corruption in LLM Training (2025.acl-long)
Copied to clipboard
| Challenge: | Large language models (LLMs) are a challenging task because of their large size and complexity. |
| Approach: | They propose to isolate and analyze the impact of SDCs on LLM training by using a cloud computing platform to access unhealthy nodes swept out of production by automated fleet management. |
| Outcome: | The proposed model training compares healthy production nodes with unhealthy nodes exhibiting SDCs at three levels: at each submodule computation, at a single optimizer step, and at . training period. |
Beyond Generation: Leveraging LLM Creativity to Overcome Label Bias in Classification (2025.findings-acl)
Copied to clipboard
| Challenge: | Existing methods to mitigate label bias by leveraging in-domain data are often unavailable in real-world scenarios. |
| Approach: | They propose a calibration method that generates synthetic in-domain data from a few in-context demonstrations and utilizes it for calibration. |
| Outcome: | The proposed method reduces label bias by leveraging in-domain data from demonstrations. |