Understanding LLM Behavior in Multi-Target Cross-Lingual Summarization
Summary
A new study addresses the underexplored area of multi-target cross-lingual text summarization (MTXLS), where a source document is summarized into multiple target languages. Researchers introduced the multi-target cross-lingual element-aware (MEA) benchmark, covering 24 target languages, to evaluate end-to-end and pipeline LLM approaches. Benchmarking revealed that MTXLS performance significantly trails English monolingual summarization. To understand LLM behavior, a layer-wise analysis framework was proposed, indicating that translation and summarization emerge jointly within later layers, rather than as distinct stages, with most processing and errors occurring at similar depths. Based on these findings, an inference-time activation steering method was developed, which utilizes hidden representations from English summarization to guide MTXLS generation. Experiments demonstrated this method consistently improves MTXLS quality across target languages.
Key takeaway
For NLP Engineers developing multi-target cross-lingual summarization systems, recognize that current LLM performance significantly lags English monolingual benchmarks. You should investigate layer-wise analysis to understand joint translation and summarization emergence in your models. Consider implementing inference-time activation steering, using English summarization representations, to consistently improve MTXLS quality across diverse target languages. This approach offers a clear path to enhance cross-lingual capabilities.
Key insights
LLMs perform cross-lingual summarization and translation jointly in later layers, not sequentially.
Principles
- MTXLS performance lags English monolingual summarization.
- Task-relevant processing and errors occur in later layers.
- Hidden representations can guide cross-lingual generation.
Method
An inference-time activation steering method guides MTXLS generation by utilizing hidden representations derived from English summarization.
In practice
- Use the MEA benchmark for MTXLS evaluation.
- Apply activation steering for cross-lingual quality.
- Analyze LLM layers for task-specific behavior.
Topics
- Multi-target Cross-lingual Summarization
- Large Language Models
- LLM Benchmarking
- Activation Steering
- Neural Network Analysis
- MEA Benchmark
Best for: Research Scientist, AI Scientist, NLP Engineer
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Editorial summary, takeaway, and curation by AIssential. Original article published by Artificial Intelligence.