Augmenting LLM Reasoning with Dynamic Notes Writing for Complex QA
Iterative RAG for multi-hop question answering faces challenges with lengthy
contexts and the buildup of irrelevant information. This hinders a model's
capacity to process and reason over retrieved content and limits performance.
While recent methods focus on compressing retrieved information, they are
either restricted to single-round RAG, require finetuning or lack scalability
in iterative RAG. To address these challenges, we propose Notes Writing, a
method that generates concise and relevant notes from retrieved documents at
each step, thereby reducing noise and retaining only essential information.
This indirectly increases the effective context length of Large Language Models
(LLMs), enabling them to reason and plan more effectively while processing
larger volumes of input text. Notes Writing is framework agnostic and can be
integrated with different iterative RAG methods. We demonstrate its
effectiveness with three iterative RAG methods, across two models and four
evaluation datasets. Notes writing yields an average improvement of 15.6
percentage points overall, with minimal increase in output tokens.
683400b5231225ee202c20b7
Rishabh Maheshwary
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2025-05-26T08:08:47.558Z
645c26d423ed9b7788d5e24b
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Rishabh Maheshwary
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rmahesh
683400b5231225ee202c20b8
Masoud Hashemi
683400b5231225ee202c20b9
Khyati Mahajan
683400b5231225ee202c20ba
Shiva Krishna Reddy Malay
683400b5231225ee202c20bb
Sai Rajeswar
683400b5231225ee202c20bc
Sathwik Tejaswi Madhusudhan
683400b5231225ee202c20bd
Spandana Gella
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2025-06-11T16:02:33.671Z
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G
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spandanagella
683400b5231225ee202c20be
Vikas Yadav
Augmenting LLM Reasoning with Dynamic Notes Writing for Complex QA
Iterative RAG for multi-hop question answering faces challenges with lengthy
contexts and the buildup of irrelevant information. This hinders a model's
capacity to process and reason over retrieved content and limits performance.
While recent methods focus on compressing retrieved information, they are
either restricted to single-round RAG, require finetuning or lack scalability
in iterative RAG. To address these challenges, we propose Notes Writing, a
method that generates concise and relevant notes from retrieved documents at
each step, thereby reducing noise and retaining only essential information.
This indirectly increases the effective context length of Large Language Models
(LLMs), enabling them to reason and plan more effectively while processing
larger volumes of input text. Notes Writing is framework agnostic and can be
integrated with different iterative RAG methods. We demonstrate its
effectiveness with three iterative RAG methods, across two models and four
evaluation datasets. Notes writing yields an average improvement of 15.6
percentage points overall, with minimal increase in output tokens.
Notes Writing enhances iterative RAG by generating concise notes at each step, improving reasoning and performance while minimizing output increase.
Iterative RAG multi-hop question answering context length irrelevant information dimensionality reduction Notes Writing Large Language Models LLMs framework agnostic evaluation datasets