mlsanityData to 7 Oct 2026

CarExpert: Leveraging Large Language Models for In-Car Conversational Question Answering

arXiv 2310.09536 ↗First posted 14 Oct 2023cs.CL cs.IR cs.LGcited by 10

On the map: Retrieval-augmented generation → RAG robustness and adaptive retrieval

Abstract (arXiv)

Large language models (LLMs) have demonstrated remarkable performance by following natural language instructions without fine-tuning them on domain-specific tasks and data. However, leveraging LLMs for domain-specific question answering suffers from severe limitations. The generated answer tends to hallucinate due to the training data collection time (when using off-the-shelf), complex user utterance and wrong retrieval (in retrieval-augmented generation). Furthermore, due to the lack of awareness about the domain and expected output, such LLMs may generate unexpected and unsafe answers that are not tailored to the target domain. In this paper, we propose CarExpert, an in-car retrieval-augmented conversational question-answering system leveraging LLMs for different tasks. Specifically, CarExpert employs LLMs to control the input, provide domain-specific documents to the extractive and generative answering components, and controls the output to ensure safe and domain-specific answers. A comprehensive empirical evaluation exhibits that CarExpert outperforms state-of-the-art LLMs in generating natural, safe and car-specific answers.

Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). Placed in RAG robustness and adaptive retrieval, one of 15 directions in Retrieval-augmented generation. Builds on 33 of its 47 references that are in the map. First posted on arXiv 14 Oct 2023.

Builds on

33 of 47 references.

References · 33

  1. 2017Attention Is All You Needcited by 59,932
  2. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  3. 2020Language Models are Few-Shot Learnerscited by 32,199
  4. 2022Training language models to follow instructions with human feedbackcited by 15,712
  5. 2023Llama 2: Open Foundation and Fine-Tuned Chat Modelscited by 12,865
  6. 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,049
  7. 2022PaLM: Scaling Language Modeling with Pathwayscited by 5,304
  8. 2016SQuAD: 100,000+ Questions for Machine Comprehension of Textcited by 4,115
  9. 2020Dense Passage Retrieval for Open-Domain Question Answeringcited by 3,779
  10. 2021Finetuned Language Models Are Zero-Shot Learnerscited by 3,747
  11. 2022Constitutional AI: Harmlessness from AI Feedbackcited by 2,950
  12. 2019ALBERT: A Lite BERT for Self-supervised Learning of Language Representationscited by 2,772
21 more
  1. 2022Survey of Hallucination in Natural Language Generationcited by 2,056
  2. 2022Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language modelscited by 1,970
  3. 2021Improving language models by retrieving from trillions of tokenscited by 1,278
  4. 2022LaMDA: Language Models for Dialog Applicationscited by 1,218
  5. 2022Ignore Previous Prompt: Attack Techniques For Language Modelscited by 760
  6. 2022Demonstrate-Search-Predict: Composing retrieval and language models for knowledge-intensive NLPcited by 316
  7. 2021SPLADE: Sparse Lexical and Expansion Model for First Stage Rankingcited by 283
  8. 2022Leveraging Large Language Models for Multiple Choice Question Answeringcited by 199
  9. 2022GrIPS: Gradient-free, Edit-based Instruction Search for Prompting Large Language Modelscited by 127
  10. 2022Language Model Cascadescited by 104
  11. 2022From Distillation to Hard Negative Sampling: Making Sparse Neural IR Models More Effectivecited by 98
  12. 2020Few-Shot Text Generation with Pattern-Exploiting Trainingcited by 87
  13. 2016An Empirical Evaluation of doc2vec with Practical Insights into Document Embedding Generationcited by 48
  14. 2021Conversational Question Answering: A Surveycited by 31
  15. 2022FiDO: Fusion-in-Decoder optimized for stronger performance and faster inferencecited by 31
  16. 2018Probabilistic FastText for Multi-Sense Word Embeddingscited by 25
  17. 2022DialoKG: Knowledge-Structure Aware Task-Oriented Dialogue Generationcited by 19
  18. 2022Uni-Retriever: Towards Learning The Unified Embedding Based Retriever in Bing Sponsored Searchcited by 17
  19. 2023Complex QA and language models hybrid architectures, Surveycited by 13
  20. 2022A Universal Discriminator for Zero-Shot Generalizationcited by 11
  21. 2021Grounding Dialogue Systems via Knowledge Graph Aware Decoding with Pre-trained Transformerscited by 8

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 1; 2024: 4; 2025: 2; 2026: 3.

10 papers.

  1. 2023Retrieval-Augmented Generation for Large Language Models: A Surveycited by 2,185
  2. 2025Large Language Models and Their Applications in Roadway Safety and Mobility Enhancement: A Comprehensive Reviewcited by 8
  3. 2024Evaluating and Enhancing Trustworthiness of LLMs in Perception Taskscited by 4
  4. 2024LLMs Can Check Their Own Results to Mitigate Hallucinations in Traffic Understanding Taskscited by 3
  5. 2024Performance Characterization of Expert Router for Scalable LLM Inferencecited by 2
  6. 2024Tapping in a Remote Vehicle's onboard LLM to Complement the Ego Vehicle's Field-of-Viewcited by 1
  7. 2025Automated Factual Benchmarking for In-Car Conversational Systems using Large Language Modelscited by 1
  8. 2026LoCar: Localization-Aware Evaluation of In-Vehicle Assistants through Fine-Grained Sociolinguistic Controlcited by 1
  9. 2026Towards Systematic Qualification of Vision-Language Models for Automotive Perception Systemscited by 0
  10. 2026Automated Evaluation of Multi-Turn Dialogues in In-Car Conversational Assistantscited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2024Evaluating the Retrieval Component in LLM-Based Question Answering Systems
  2. 2025A Survey of Large Language Model Agents for Question Answering
  3. 2024Retrieval Augmented Generation for Domain-specific Question Answering
  4. 2023InCA: Rethinking In-Car Conversational System Assessment Leveraging Large Language Models
  5. 2023Modeling Uncertainty and Using Post-fusion as Fallback Improves Retrieval Augmented Generation with LLMs
  6. 2025Building Open-Retrieval Conversational Question Answering Systems by Generating Synthetic Data and Decontextualizing User Questions
  7. 2024Conv-CoA: Improving Open-domain Question Answering in Large Language Models via Conversational Chain-of-Action
  8. 2025Benchmarking Contextual Understanding for In-Car Conversational Systems