Papers with Qwen2
LongGenBench: Long-context Generation Benchmark (2024.findings-emnlp)
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| Challenge: | Current long-context benchmarks focus on retrieval-based tests, requiring Large Language Models to locate specific information within extensive input contexts. |
| Approach: | They propose a long-context generation benchmark that allows for flexible configurations of customized generation context lengths. |
| Outcome: | The proposed benchmark improves performance on NIAH and other retrieval-based tests. |
SAGED: A Holistic Bias-Benchmarking Pipeline for Language Models with Customisable Fairness Calibration (2025.coling-main)
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Xin Guan, Nate Demchak, Saloni Gupta, Ze Wang, Ediz Ertekin Jr., Adriano Koshiyama, Emre Kazim, Zekun Wu
| Challenge: | Existing benchmarks for large language models fail to detect bias due to limited scope, contamination, and lack of a fairness baseline. |
| Approach: | They propose a benchmarking pipeline to detect biases in large language models . they use metrics for max disparity, impact ratio, and bias concentration to analyze disparity . |
| Outcome: | SAGED(bias) is the first holistic benchmarking pipeline to address biases in large language models. |
Empirical Study of Zero-shot Keyphrase Extraction with Large Language Models (2025.coling-main)
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| Challenge: | a prompting-based approach can effectively supersede traditional KE methods, a study shows . our code is available at https://github.com/kangnlp/zero-shot-keyphrase-extraction-with-LLMs. |
| Approach: | They propose four prompting strategies for zero-shot keyphrase extraction using Large Language Models. |
| Outcome: | The proposed prompting strategies outperform state-of-the-art prompting methods on KE benchmark datasets. |
Can LLMs Clarify? Investigation and Enhancement of Large Language Models on Argument Claim Optimization (2025.coling-main)
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| Challenge: | While Large Language Models (LLMs) have demonstrated proficiency in text rewriting tasks such as style transfer and query rewrite, their application to claim optimization remains unexplored. |
| Approach: | They propose to use a sliding window mechanism to evaluate the performance of large language models in claim clarification tasks under different settings. |
| Outcome: | The proposed model improves the performance of three LLMs on the claim clarification task under zero-shot, few-shot and supervised fine-tuning settings. |
Unmasking the Imposters: How Censorship and Domain Adaptation Affect the Detection of Machine-Generated Tweets (2025.coling-main)
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| Challenge: | generative AI has been used to generate fluent and convincing text on social media platforms . a new study examines the generative capabilities of four popular large language models . |
| Approach: | They propose a methodology to examine the generative capabilities of four prominent LLMs on Twitter using a dataset from Llama 3, Mistral, Qwen2 and GPT4o. |
| Outcome: | The proposed method examines the generative capabilities of four prominent LLMs on Twitter. |
MDCure: A Scalable Pipeline for Multi-Document Instruction-Following (2025.acl-long)
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| Challenge: | Multi-document (MD) processing is crucial for LLMs to handle real-world tasks such as summarization and question-answering across large sets of documents. |
| Approach: | They propose a framework that generates high-quality synthetic MD instruction data over sets of articles via targeted prompts. |
| Outcome: | MDCure generates high-quality synthetic MD instruction data over sets of articles . evaluations show it improves over pre-trained models by up to 75.1% . |