Exploring Fine-Tuning for In-Context Retrieval and Efficient KV-Caching in Long-Context Language Models (2026.eacl-short)
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| Challenge: | Long-Context Language Models (LCLMs) can encode entire document collections, offering a strong alternative to retrieval-augmented generation (RAG). |
| Approach: | They propose to use LCLMs to encode documents with context windows of millions of tokens to improve their performance. |
| Outcome: | The proposed training strategies improve long-context performance and their robustness under compression techniques. |
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