Lost in the Middle: How Language Models Use Long Contexts Paper Reading

In this paper reading session, Sally-Ann DeLucia and Amber Roberts discuss the paper "Improving Language Model Retrieval with Query-Aware Contextualization" by OpenAI's team. The paper focuses on improving retrieval performance in large language models (LLMs) by manipulating the context given to them. Key takeaways from this discussion include: 1. Encoder-decoder models have a bidirectional encoder that allows for better understanding of context based on preceding and future tokens, which can be leveraged to improve retrieval performance in LLMs. 2. Placing the query or question before and after the document can significantly improve retrieval performance in LLMs. 3. The architecture of transformers may change as more research is conducted into understanding how these models use context. 4. Pushing relevant information to the top and returning fewer documents are promising strategies for improving retrieval performance in LLMs. 5. Observability tools can be helpful in understanding how these models use context and can aid in experimentation with different architectures.

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