Summary of Enhancing Long Context Performance in Llms Through Inner Loop Query Mechanism, by Yimin Tang et al.
Enhancing Long Context Performance in LLMs Through Inner Loop Query Mechanism
by Yimin Tang, Yurong Xu, Ning Yan, Masood Mortazavi
First submitted to arxiv on: 11 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Information Retrieval (cs.IR)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel approach to transformer-based language models is proposed to improve the handling of longer contexts. The current quadratic scaling of computational complexity with input size limits the effectiveness of large language models in training and inference. Retrieval-augmented generation (RAG) based models can better handle long contexts by filtering out unnecessary information, but most methods only perform retrieval based on the initial query. This paper introduces Inner Loop Memory Augmented Tree Retrieval (ILM-TR), which involves inner-loop queries based not only on the query question itself but also on intermediate findings. The approach retrieves information from a RAG system and integrates data from lengthy documents at various levels of abstraction. The generated texts are stored in Short-Term Memory (STM) and used to formulate the next query, with the process repeated until convergence is achieved. Experimental results demonstrate improvements over traditional retrieval-augmented LLMs, particularly in long context tests such as M-NIAH and BABILong. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to make language models better at handling longer pieces of text has been developed. The problem is that current models get slower and less accurate when they have to process really big inputs. Some other approaches use a “retrieval” system to help the model figure out what’s important, but they only do this once. This new approach does it multiple times, based on what the model has learned so far. It also stores what it finds in something called Short-Term Memory and uses that information to make its next move. The results show that this approach works better than others for tasks like understanding long passages of text. |
Keywords
» Artificial intelligence » Inference » Rag » Retrieval augmented generation » Transformer
