alleviating the phenomenon of “Lost in the Middle
Is this still an issue with current models? Haven't looked into it recently but haystack evals would make me think now.
6 Matching Annotations
- Oct 2025
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arxiv.org arxiv.org
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GPTCache [39] addresses theissue of high latency when using the LLM APIs by buildinga semantic cache for storing LLM responses
Database of frequent responses? Hey you asked a thing almost identical to this other person, "joop" here's your answer.
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In logit-based RAG, generative mod-els integrate retrieval information through logits during the
This is one of those things that at the surface I think I get, but really don't make sense in practice. Missing something. What is even the benefit of doing this? Directly affecting the logit outputs? This would require a specially trained output model, or retrieval model to make any sense.
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In the image domain, several studies [103]–[106] employcross-attention mechanisms to fuse retrieval results by inte-grating their latent representations. Conversely, Li et al. [107]implement a text-image Affine Combination Module (ACM)that directly concatenates hidden features.
Model integrated image combine? IPAdapter related at all? https://github.com/tencent-ailab/IP-Adapter/
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allowing for input chunking
Within the attention heads? Chunked context for RAG is common, which is fine and would still work injecting into latent space, but this seems to apply something else INSIDE the model rather than chunking the context that gets injected.
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2) Latent Representation-based RAG: In latentrepresentation-based RAG framework, retrieved objects areincorporated into generative models as latent representations.This enhances the model’s comprehension abilities andimproves the quality of the generated content.
Open weight models only I would think, unless the knowledge store was passed to the provider.
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