Calibrated Decision-Making through LLM-Assisted Retrieval

• URL: http://arxiv.org/abs/2411.08891v1
• Date: Mon, 28 Oct 2024 06:41:05 GMT
• Title: Calibrated Decision-Making through LLM-Assisted Retrieval
• Authors: Chaeyun Jang, Hyungi Lee, Seanie Lee, Juho Lee,
• Abstract summary: When large language models confidently provide incorrect information, it can lead humans to make suboptimal decisions. We propose Calibrated Retrieval-Augmented Generation (CalibRAG) to ensure that decisions informed by retrieved documents are well-calibrated.
• Score: 21.14260990790119
• License:
• Abstract: Recently, large language models (LLMs) have been increasingly used to support various decision-making tasks, assisting humans in making informed decisions. However, when LLMs confidently provide incorrect information, it can lead humans to make suboptimal decisions. To prevent LLMs from generating incorrect information on topics they are unsure of and to improve the accuracy of generated content, prior works have proposed Retrieval Augmented Generation (RAG), where external documents are referenced to generate responses. However, traditional RAG methods focus only on retrieving documents most relevant to the input query, without specifically aiming to ensure that the human user's decisions are well-calibrated. To address this limitation, we propose a novel retrieval method called Calibrated Retrieval-Augmented Generation (CalibRAG), which ensures that decisions informed by the retrieved documents are well-calibrated. Then we empirically validate that CalibRAG improves calibration performance as well as accuracy, compared to other baselines across various datasets.

Related papers

• Retriever-and-Memory: Towards Adaptive Note-Enhanced Retrieval-Augmented Generation [72.70046559930555]
  We propose a generic RAG approach called Adaptive Note-Enhanced RAG (Adaptive-Note) for complex QA tasks. Specifically, Adaptive-Note introduces an overarching view of knowledge growth, iteratively gathering new information in the form of notes. In addition, we employ an adaptive, note-based stop-exploration strategy to decide "what to retrieve and when to stop" to encourage sufficient knowledge exploration.
  arXiv  Detail & Related papers  (2024-10-11T14:03:29Z)
• Retrieving, Rethinking and Revising: The Chain-of-Verification Can Improve Retrieval Augmented Generation [38.80878966092216]
  Recent Retrieval Augmented Generation (RAG) aims to enhance Large Language Models (LLMs) We propose the chain-of-verification (CoV-RAG) to enhance the external retrieval correctness and internal generation consistency.
  arXiv  Detail & Related papers  (2024-10-08T08:34:54Z)
• Vietnamese Legal Information Retrieval in Question-Answering System [0.0]
  Retrieval Augmented Generation (RAG) has gained significant recognition for enhancing the capabilities of large language models (LLMs) However, RAG often fall short when applied to the Vietnamese language due to several challenges. This report introduces our three main modifications taken to address these challenges.
  arXiv  Detail & Related papers  (2024-09-05T02:34:05Z)
• Optimizing Query Generation for Enhanced Document Retrieval in RAG [53.10369742545479]
  Large Language Models (LLMs) excel in various language tasks but they often generate incorrect information. Retrieval-Augmented Generation (RAG) aims to mitigate this by using document retrieval for accurate responses.
  arXiv  Detail & Related papers  (2024-07-17T05:50:32Z)
• Ground Every Sentence: Improving Retrieval-Augmented LLMs with Interleaved Reference-Claim Generation [51.8188846284153]
  RAG has been widely adopted to enhance Large Language Models (LLMs) Attributed Text Generation (ATG) has attracted growing attention, which provides citations to support the model's responses in RAG. This paper proposes a fine-grained ATG method called ReClaim(Refer & Claim), which alternates the generation of references and answers step by step.
  arXiv  Detail & Related papers  (2024-07-01T20:47:47Z)
• Improving Retrieval for RAG based Question Answering Models on Financial Documents [0.046603287532620746]
  This paper explores the existing constraints of RAG pipelines and introduces methodologies for enhancing text retrieval. It delves into strategies such as sophisticated chunking techniques, query expansion, the incorporation of metadata annotations, the application of re-ranking algorithms, and the fine-tuning of embedding algorithms.
  arXiv  Detail & Related papers  (2024-03-23T00:49:40Z)
• Unsupervised Information Refinement Training of Large Language Models for Retrieval-Augmented Generation [128.01050030936028]
  We propose an information refinement training method named InFO-RAG. InFO-RAG is low-cost and general across various tasks. It improves the performance of LLaMA2 by an average of 9.39% relative points.
  arXiv  Detail & Related papers  (2024-02-28T08:24:38Z)
• RetrievalQA: Assessing Adaptive Retrieval-Augmented Generation for Short-form Open-Domain Question Answering [42.66223628527439]
  Adaptive retrieval-augmented generation (ARAG) aims to dynamically determine the necessity of retrieval for queries instead of retrieving indiscriminately. This work presents a benchmark, RetrievalQA, comprising 1,271 short-form questions covering new world and long-tail knowledge.
  arXiv  Detail & Related papers  (2024-02-26T09:59:04Z)
• V-STaR: Training Verifiers for Self-Taught Reasoners [71.53113558733227]
  V-STaR trains a verifier using DPO that judges correctness of model-generated solutions. Running V-STaR for multiple iterations results in progressively better reasoners and verifiers.
  arXiv  Detail & Related papers  (2024-02-09T15:02:56Z)
• Corrective Retrieval Augmented Generation [36.04062963574603]
  Retrieval-augmented generation (RAG) relies heavily on relevance of retrieved documents, raising concerns about how the model behaves if retrieval goes wrong. We propose the Corrective Retrieval Augmented Generation (CRAG) to improve the robustness of generation. CRAG is plug-and-play and can be seamlessly coupled with various RAG-based approaches.
  arXiv  Detail & Related papers  (2024-01-29T04:36:39Z)
• Self-Evaluation Improves Selective Generation in Large Language Models [54.003992911447696]
  We reformulate open-ended generation tasks into token-level prediction tasks. We instruct an LLM to self-evaluate its answers. We benchmark a range of scoring methods based on self-evaluation.
  arXiv  Detail & Related papers  (2023-12-14T19:09:22Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.