# Chain-of-Factors Paper-Reviewer Matching

arXiv: https://arxiv.org/abs/2310.14483 · HTML: https://mlsanity.com/paper/2310.14483/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 33 of its 66 references that are in the map.
- First posted on arXiv 23 Oct 2023.

## Builds on (most cited first, 30 of 33)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [Representation Learning with Contrastive Predictive Coding](https://mlsanity.com/paper/1807.03748/) (2018)
- [Layer Normalization](https://mlsanity.com/paper/1607.06450/) (2016)
- [Prefix-Tuning: Optimizing Continuous Prompts for Generation](https://mlsanity.com/paper/2101.00190/) (2021)
- [Dense Passage Retrieval for Open-Domain Question Answering](https://mlsanity.com/paper/2004.04906/) (2020)
- [The Power of Scale for Parameter-Efficient Prompt Tuning](https://mlsanity.com/paper/2104.08691/) (2021)
- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [Scaling Instruction-Finetuned Language Models](https://mlsanity.com/paper/2210.11416/) (2022)
- [MS MARCO: A Human Generated MAchine Reading COmprehension Dataset](https://mlsanity.com/paper/1611.09268/) (2016)
- [SciBERT: A Pretrained Language Model for Scientific Text](https://mlsanity.com/paper/1903.10676/) (2019)
- [PubMedQA: A Dataset for Biomedical Research Question Answering](https://mlsanity.com/paper/1909.06146/) (2019)
- [Super-NaturalInstructions: Generalization via Declarative Instructions on 1600+ NLP Tasks](https://mlsanity.com/paper/2204.07705/) (2022)
- [AdapterFusion: Non-Destructive Task Composition for Transfer Learning](https://mlsanity.com/paper/2005.00247/) (2020)
- [SPECTER: Document-level Representation Learning using Citation-informed Transformers](https://mlsanity.com/paper/2004.07180/) (2020)
- [GraphFormers: GNN-nested Transformers for Representation Learning on Textual Graph](https://mlsanity.com/paper/2105.02605/) (2021)
- [SciRepEval: A Multi-Format Benchmark for Scientific Document Representations](https://mlsanity.com/paper/2211.13308/) (2022)
- [Neighborhood Contrastive Learning for Scientific Document Representations with Citation Embeddings](https://mlsanity.com/paper/2202.06671/) (2022)
- [A Web-scale system for scientific knowledge exploration](https://mlsanity.com/paper/1805.12216/) (2018)
- [Mitigating Manipulation in Peer Review via Randomized Reviewer Assignments](https://mlsanity.com/paper/2006.16437/) (2020)
- [PeerReview4All: Fair and Accurate Reviewer Assignment in Peer Review](https://mlsanity.com/paper/1806.06237/) (2018)
- [Paper Matching with Local Fairness Constraints](https://mlsanity.com/paper/1905.11924/) (2019)
- [COCO-DR: Combating Distribution Shifts in Zero-Shot Dense Retrieval with Contrastive and Distributionally Robust Learning](https://mlsanity.com/paper/2210.15212/) (2022)
- [The Effect of Metadata on Scientific Literature Tagging: A Cross-Field Cross-Model Study](https://mlsanity.com/paper/2302.03341/) (2023)
- [Editable User Profiles for Controllable Text Recommendation](https://mlsanity.com/paper/2304.04250/) (2023)
- [Pre-training Multi-task Contrastive Learning Models for Scientific Literature Understanding](https://mlsanity.com/paper/2305.14232/) (2023)
- [PaRe: A Paper-Reviewer Matching Approach Using a Common Topic Space](https://mlsanity.com/paper/1909.11258/) (2019)
- [Making Paper Reviewing Robust to Bid Manipulation Attacks](https://mlsanity.com/paper/2102.06020/) (2021)
- [A multi-label classification method using a hierarchical and transparent representation for paper-reviewer recommendation](https://mlsanity.com/paper/1912.08976/) (2019)

## Built on by (most cited first, 11 of 11)

- [A Comprehensive Survey of Scientific Large Language Models and Their Applications in Scientific Discovery](https://mlsanity.com/paper/2406.10833/) (2024)
- [PaperArena: An Evaluation Benchmark for Tool-Augmented Agentic Reasoning on Scientific Literature](https://mlsanity.com/paper/2510.10909/) (2025)
- [PhysBERT: A Text Embedding Model for Physics Scientific Literature](https://mlsanity.com/paper/2408.09574/) (2024)
- [LogiCoL: Logically-Informed Contrastive Learning for Set-based Dense Retrieval](https://mlsanity.com/paper/2505.19588/) (2025)
- [RATE: Reviewer Profiling and Annotation-free Training for Expertise Ranking in Peer Review Systems](https://mlsanity.com/paper/2601.19637/) (2026)
- [OmniReview: A Large-scale Benchmark and LLM-enhanced Framework for Realistic Reviewer Recommendation](https://mlsanity.com/paper/2602.08896/) (2026)
- [Using Artificial Intuition in Distinct, Minimalist Classification of Scientific Abstracts for Management of Technology Portfolios](https://mlsanity.com/paper/2508.13182/) (2025)
- [Improving Scientific Document Retrieval with Academic Concept Index](https://mlsanity.com/paper/2601.00567/) (2026)
- [Detecting Miscitation on the Scholarly Web through LLM-Augmented Text-Rich Graph Learning](https://mlsanity.com/paper/2603.12290/) (2026)
- [Beyond Paper-to-Paper: Structured Profiling and Rubric Scoring for Paper-Reviewer Matching](https://mlsanity.com/paper/2604.05866/) (2026)
- [MERIT: Matching Expertise via Rubric-Informed Training for Reviewer Assignment](https://mlsanity.com/paper/2605.27865/) (2026)

## Abstract (arXiv)

With the rapid increase in paper submissions to academic conferences, the need for automated and accurate paper-reviewer matching is more critical than ever. Previous efforts in this area have considered various factors to assess the relevance of a reviewer's expertise to a paper, such as the semantic similarity, shared topics, and citation connections between the paper and the reviewer's previous works. However, most of these studies focus on only one factor, resulting in an incomplete evaluation of the paper-reviewer relevance. To address this issue, we propose a unified model for paper-reviewer matching that jointly considers semantic, topic, and citation factors. To be specific, during training, we instruction-tune a contextualized language model shared across all factors to capture their commonalities and characteristics; during inference, we chain the three factors to enable step-by-step, coarse-to-fine search for qualified reviewers given a submission. Experiments on four datasets (one of which is newly contributed by us) spanning various fields such as machine learning, computer vision, information retrieval, and data mining consistently demonstrate the effectiveness of our proposed Chain-of-Factors model in comparison with state-of-the-art paper-reviewer matching methods and scientific pre-trained language models.