Papers with extraction time
Large Language Models Are Effective Human Annotation Assistants, But Not Good Independent Annotators (2026.findings-acl)
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| Challenge: | State-of-the-art NLP models are expensive and inefficient for event annotation. |
| Approach: | They propose to integrate LLMs into a holistic workflow that summarizes news with event coreference resolution and argument extraction in three modes: AI-only, AI assistance, and human only. |
| Outcome: | The proposed workflow integrates LLMs to alleviate human labor in a holistic pipeline. |
Insights into LLM Long-Context Failures: When Transformers Know but Don’t Tell (2024.findings-emnlp)
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| Challenge: | Large Language Models (LLMs) exhibit positional bias, struggling to utilize information from the middle or end of long contexts. |
| Approach: | They propose to examine LLMs' long-context generalizations by probing their hidden representations. |
| Outcome: | The proposed models excel at processing extended contexts while preserving their positional bias. |