---
title: "LitLLM: A Toolkit for Scientific Literature Review (2024)"
description: "Drafting a related-work section is slow, and asking an LLM to do it unaided is risky: models invent… 46 references, cited by 35 papers here."
authors: ["Shubham Agarwal", "Gaurav Sahu", "Abhay Puri", "Issam H. Laradji", "Krishnamurthy DJ Dvijotham", "Jason Stanley", "Laurent Charlin", "Christopher Pal"]
year: 2024
venue: "arXiv:2402.01788"
source: "https://arxiv.org/abs/2402.01788"
doi: "10.48550/arxiv.2402.01788"
arxiv: "2402.01788"
licence: "CC BY 4.0 (arXiv)"
licence_url: "http://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/agarwal-2024-litllm-toolkit-scientific-literature-review/"
---
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# LitLLM: A Toolkit for Scientific Literature Review

**Authors:** [Shubham Agarwal](https://md.research.elamur.ai/free/en/authors/shubham-agarwal/), [Gaurav Sahu](https://md.research.elamur.ai/free/en/authors/gaurav-sahu/), [Abhay Puri](https://md.research.elamur.ai/free/en/authors/abhay-puri/), [Issam H. Laradji](https://md.research.elamur.ai/free/en/authors/issam-laradji/), [Krishnamurthy DJ Dvijotham](https://md.research.elamur.ai/free/en/authors/krishnamurthy-dvijotham/), [Jason Stanley](https://md.research.elamur.ai/free/en/authors/jason-stanley/), [Laurent Charlin](https://md.research.elamur.ai/free/en/authors/laurent-charlin/) and [Christopher Pal](https://md.research.elamur.ai/free/en/authors/christopher-pal/) · **Year:** 2024 · **Venue:** arXiv:2402.01788 · **Licence:** [CC BY 4.0 (arXiv)](http://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://arxiv.org/abs/2402.01788) · [PDF](https://arxiv.org/pdf/2402.01788) · [DOI 10.48550/arxiv.2402.01788](https://doi.org/10.48550/arxiv.2402.01788) · [arXiv 2402.01788](https://arxiv.org/abs/2402.01788) · [OpenAlex](https://openalex.org/W4391591686)

**Citations:** 10 citations (OpenAlex, 2026-10-14) · [35](https://md.research.elamur.ai/free/en/papers/agarwal-2024-litllm-toolkit-scientific-literature-review/cited-by/) in our library, of the 149,439 papers whose bibliographies we read

## Summary
Drafting a related-work section is slow, and asking an LLM to do it unaided is risky: models invent references and know nothing published after their training cutoff. LitLLM is an interactive toolkit that addresses this with Retrieval Augmented Generation (RAG). From a user's abstract or rough research idea, an off-the-shelf LLM produces a compact keyword query. Users can add their own keywords or a seed paper. Candidate papers come from the Semantic Scholar and OpenAlex APIs, and a second LLM reorders them by relevance to the abstract. A generator LLM then writes the related-work text from the abstract and the top-ranked papers. It can optionally follow a sentence plan that sets the output length and the line where each reference is cited. The paper reports no benchmark scores. Instead, a small group of researchers tested the demo and preferred plan-guided output for its concision. The contribution is a modular, grounded blueprint for research-assistant tools that can swap in other LLMs or retrieval sources.

## Key points
- Retrieval uses the Semantic Scholar Academic Graph and Recommendations endpoints plus the OpenAlex search endpoint. Together these cover more than 300M metadata records. The keyword prompt asks for at most 5 keywords.
- Re-ranking adopts Sun et al.'s (2023) method of prompting the LLM to generate an ordering of the candidates, with the model listing paper IDs from most to least relevant. A debate-ranking variant with attribution is also explored.
- Generation runs on GPT-3.5-turbo and GPT-4 via the OpenAI API, in zero-shot or plan-based mode. Plan templates specify the sentence count, word count and citation positions. The front end is built in React, and results can be sorted by relevance, citation count or year.
- Evaluation is only a preliminary user study with 5 researchers. In a case study on Li et al. (2023), the system's top-ranked retrieved paper was one the original authors had actually cited.
- The main stated limitation is that only abstracts are processed, not full texts. Future plans include longer-context models, PDF parsing and further search APIs such as Google Scholar.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

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## Cite

Cite as: Shubham Agarwal, Gaurav Sahu, Abhay Puri, Issam H. Laradji, Krishnamurthy DJ Dvijotham, Jason Stanley, Laurent Charlin and Christopher Pal. 2024. LitLLM: A Toolkit for Scientific Literature Review. arXiv:2402.01788.

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@misc{agarwal2024litllm,
  title = {{LitLLM: A Toolkit for Scientific Literature Review}},
  author = {Shubham Agarwal, Gaurav Sahu, Abhay Puri, Issam H. Laradji, Krishnamurthy DJ Dvijotham, Jason Stanley, Laurent Charlin and Christopher Pal},
  year = {2024},
  doi = {10.48550/arxiv.2402.01788},
  eprint = {2402.01788},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.2402.01788}
}
```

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## References

46 references, 24 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

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*Updated 3 November 2026*