title: Context Tuning for Retrieval Augmented Generation
summary: Large language models (LLMs) have the remarkable ability to solve new tasks
  with just a few examples, but they need access to the right tools. Retrieval
  Augmented Generation (RAG) addresses this problem by retrieving a list of
  relevant tools for a given task. However, RAG's tool retrieval step requires
  all the required information to be explicitly present in the query. This is a
  limitation, as semantic search, the widely adopted tool retrieval method, can
  fail when the query is incomplete or lacks context. To address this limitation,
  we propose Context Tuning for RAG, which employs a smart context retrieval
  system to fetch relevant information that improves both tool retrieval and plan
  generation. Our lightweight context retrieval model uses numerical,
  categorical, and habitual usage signals to retrieve and rank context items. Our
  empirical results demonstrate that context tuning significantly enhances
  semantic search, achieving a 3.5-fold and 1.5-fold improvement in Recall@K for
  context retrieval and tool retrieval tasks respectively, and resulting in an
  11.6% increase in LLM-based planner accuracy. Additionally, we show that our
  proposed lightweight model using Reciprocal Rank Fusion (RRF) with LambdaMART
  outperforms GPT-4 based retrieval. Moreover, we observe context augmentation at
  plan generation, even after tool retrieval, reduces hallucination.
paper_authors._id: 6577cd43c37954680aa21292
paper_authors.name: Raviteja Anantha
paper_authors._id: 6577cd43c37954680aa21293
paper_authors.name: Tharun Bethi
paper_authors._id: 6577cd43c37954680aa21294
paper_authors.name: Danil Vodianik
paper_authors._id: 6577cd43c37954680aa21295
paper_authors.name: Srinivas Chappidi
paper_title: Context Tuning for Retrieval Augmented Generation
paper_summary: Large language models (LLMs) have the remarkable ability to solve new tasks
  with just a few examples, but they need access to the right tools. Retrieval
  Augmented Generation (RAG) addresses this problem by retrieving a list of
  relevant tools for a given task. However, RAG's tool retrieval step requires
  all the required information to be explicitly present in the query. This is a
  limitation, as semantic search, the widely adopted tool retrieval method, can
  fail when the query is incomplete or lacks context. To address this limitation,
  we propose Context Tuning for RAG, which employs a smart context retrieval
  system to fetch relevant information that improves both tool retrieval and plan
  generation. Our lightweight context retrieval model uses numerical,
  categorical, and habitual usage signals to retrieve and rank context items. Our
  empirical results demonstrate that context tuning significantly enhances
  semantic search, achieving a 3.5-fold and 1.5-fold improvement in Recall@K for
  context retrieval and tool retrieval tasks respectively, and resulting in an
  11.6% increase in LLM-based planner accuracy. Additionally, we show that our
  proposed lightweight model using Reciprocal Rank Fusion (RRF) with LambdaMART
  outperforms GPT-4 based retrieval. Moreover, we observe context augmentation at
  plan generation, even after tool retrieval, reduces hallucination.
paper_ai_summary: Context Tuning enhances Retrieval Augmented Generation by improving context retrieval, tool retrieval, and LLM-based planner accuracy using smart context signals and fusion techniques.
paper_ai_keywords: Retrieval Augmented Generation (RAG) semantic search context retrieval tool retrieval numerical signals categorical signals habitual usage signals lightweight context retrieval model Recall@K LambdaMART Reciprocal Rank Fusion (RRF) plan generation hallucination