# CoTAR: Chain-of-Thought Attribution Reasoning with Multi-level Granularity

arXiv: https://arxiv.org/abs/2404.10513 · HTML: https://mlsanity.com/paper/2404.10513/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Placed in Prompting and chain-of-thought reasoning, one of 17 directions in LLM reasoning.
- Builds on 13 of its 20 references that are in the map.
- First posted on arXiv 16 Apr 2024.

## Placed in

- LLM reasoning / [Prompting and chain-of-thought reasoning](https://mlsanity.com/lr/prompting-and-chain-of-thought-reasoning/)

## Builds on (most cited first, 13 of 13)

- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Mistral 7B](https://mlsanity.com/paper/2310.06825/) (2023)
- [Scaling Instruction-Finetuned Language Models](https://mlsanity.com/paper/2210.11416/) (2022)
- [Emergent Abilities of Large Language Models](https://mlsanity.com/paper/2206.07682/) (2022)
- [LaMDA: Language Models for Dialog Applications](https://mlsanity.com/paper/2201.08239/) (2022)
- [Enabling Large Language Models to Generate Text with Citations](https://mlsanity.com/paper/2305.14627/) (2023)
- [Evaluating Verifiability in Generative Search Engines](https://mlsanity.com/paper/2304.09848/) (2023)
- [Teaching language models to support answers with verified quotes](https://mlsanity.com/paper/2203.11147/) (2022)
- [Attributed Question Answering: Evaluation and Modeling for Attributed Large Language Models](https://mlsanity.com/paper/2212.08037/) (2022)
- [The Dawn After the Dark: An Empirical Study on Factuality Hallucination in Large Language Models](https://mlsanity.com/paper/2401.03205/) (2024)
- [Attribute First, then Generate: Locally-attributable Grounded Text Generation](https://mlsanity.com/paper/2403.17104/) (2024)
- [SAIL: Search-Augmented Instruction Learning](https://mlsanity.com/paper/2305.15225/) (2023)
- [Exploring the Relationship between LLM Hallucinations and Prompt Linguistic Nuances: Readability, Formality, and Concreteness](https://mlsanity.com/paper/2309.11064/) (2023)

## Built on by (most cited first, 12 of 12)

- [LongFaith: Enhancing Long-Context Reasoning in LLMs with Faithful Synthetic Data](https://mlsanity.com/paper/2502.12583/) (2025)
- [Attribute or Abstain: Large Language Models as Long Document Assistants](https://mlsanity.com/paper/2407.07799/) (2024)
- [RAG Foundry: A Framework for Enhancing LLMs for Retrieval Augmented Generation](https://mlsanity.com/paper/2408.02545/) (2024)
- [Large Language Model Sourcing: A Survey](https://mlsanity.com/paper/2510.10161/) (2025)
- [RAGentA: Multi-Agent Retrieval-Augmented Generation for Attributed Question Answering](https://mlsanity.com/paper/2506.16988/) (2025)
- [MAVIS: A Benchmark for Multimodal Source Attribution in Long-form Visual Question Answering](https://mlsanity.com/paper/2511.12142/) (2025)
- [On Synthesizing Data for Context Attribution in Question Answering](https://mlsanity.com/paper/2504.05317/) (2025)
- [Are Finer Citations Always Better? Rethinking Granularity for Attributed Generation](https://mlsanity.com/paper/2604.01432/) (2026)
- [SAFE: Improving LLM Systems using Sentence-Level In-generation Attribution](https://mlsanity.com/paper/2505.12621/) (2025)
- [SQuAI: Scientific Question-Answering with Multi-Agent Retrieval-Augmented Generation](https://mlsanity.com/paper/2510.15682/) (2025)
- [Attribution Techniques for Mitigating Hallucinated Information in RAG Systems: A Survey](https://mlsanity.com/paper/2601.19927/) (2026)
- [MultAttnAttrib: Training-Free Multimodal Attribution in Long Document Question Answering](https://mlsanity.com/paper/2607.01420/) (2026)

## Abstract (arXiv)

State-of-the-art performance in QA tasks is currently achieved by systems employing Large Language Models (LLMs), however these models tend to hallucinate information in their responses. One approach focuses on enhancing the generation process by incorporating attribution from the given input to the output. However, the challenge of identifying appropriate attributions and verifying their accuracy against a source is a complex task that requires significant improvements in assessing such systems. We introduce an attribution-oriented Chain-of-Thought reasoning method to enhance the accuracy of attributions. This approach focuses the reasoning process on generating an attribution-centric output. Evaluations on two context-enhanced question-answering datasets using GPT-4 demonstrate improved accuracy and correctness of attributions. In addition, the combination of our method with finetuning enhances the response and attribution accuracy of two smaller LLMs, showing their potential to outperform GPT-4 in some cases.