# Science Checker Reloaded: A Bidirectional Paradigm for Transparency and Logical Reasoning

arXiv: https://arxiv.org/abs/2402.13897 · HTML: https://mlsanity.com/paper/2402.13897/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Knowledge- and tool-augmented reasoning, one of 17 directions in LLM reasoning.
- Builds on 20 of its 38 references that are in the map.
- First posted on arXiv 21 Feb 2024.

## Placed in

- LLM reasoning / [Knowledge- and tool-augmented reasoning](https://mlsanity.com/lr/knowledge-and-tool-augmented-reasoning/)

## Builds on (most cited first, 20 of 20)

- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Lost in the Middle: How Language Models Use Long Contexts](https://mlsanity.com/paper/2307.03172/) (2023)
- [M3-Embedding: Multi-Linguality, Multi-Functionality, Multi-Granularity Text Embeddings Through Self-Knowledge Distillation](https://mlsanity.com/paper/2402.03216/) (2024)
- [MTEB: Massive Text Embedding Benchmark](https://mlsanity.com/paper/2210.07316/) (2022)
- [Making Monolingual Sentence Embeddings Multilingual using Knowledge Distillation](https://mlsanity.com/paper/2004.09813/) (2020)
- [Generate rather than Retrieve: Large Language Models are Strong Context Generators](https://mlsanity.com/paper/2209.10063/) (2022)
- [Answering Complex Open-Domain Questions with Multi-Hop Dense Retrieval](https://mlsanity.com/paper/2009.12756/) (2020)
- [Small LLMs Are Weak Tool Learners: A Multi-LLM Agent](https://mlsanity.com/paper/2401.07324/) (2024)
- [Search-in-the-Chain: Interactively Enhancing Large Language Models with Search for Knowledge-intensive Tasks](https://mlsanity.com/paper/2304.14732/) (2023)
- [A Comprehensive Survey on Vector Database: Storage and Retrieval Technique, Challenge](https://mlsanity.com/paper/2310.11703/) (2023)
- [Text2KGBench: A Benchmark for Ontology-Driven Knowledge Graph Generation from Text](https://mlsanity.com/paper/2308.02357/) (2023)
- [Understanding Retrieval Augmentation for Long-Form Question Answering](https://mlsanity.com/paper/2310.12150/) (2023)
- [RETA-LLM: A Retrieval-Augmented Large Language Model Toolkit](https://mlsanity.com/paper/2306.05212/) (2023)
- [A Cost Analysis of Generative Language Models and Influence Operations](https://mlsanity.com/paper/2308.03740/) (2023)
- [Small Language Models Fine-tuned to Coordinate Larger Language Models improve Complex Reasoning](https://mlsanity.com/paper/2310.18338/) (2023)
- [Predicting Efficiency/Effectiveness Trade-offs for Dense vs. Sparse Retrieval Strategy Selection](https://mlsanity.com/paper/2109.10739/) (2021)
- [Reducing LLM Hallucinations using Epistemic Neural Networks](https://mlsanity.com/paper/2312.15576/) (2023)
- [An Ontology-Based Dialogue Management System for Banking and Finance Dialogue Systems](https://mlsanity.com/paper/1804.04838/) (2018)
- [Automatic Claim Review for Climate Science via Explanation Generation](https://mlsanity.com/paper/2107.14740/) (2021)
- [KGI: An Integrated Framework for Knowledge Intensive Language Tasks](https://mlsanity.com/paper/2204.03985/) (2022)

## Abstract (arXiv)

Information retrieval is a rapidly evolving field. However it still faces significant limitations in the scientific and industrial vast amounts of information, such as semantic divergence and vocabulary gaps in sparse retrieval, low precision and lack of interpretability in semantic search, or hallucination and outdated information in generative models. In this paper, we introduce a two-block approach to tackle these hurdles for long documents. The first block enhances language understanding in sparse retrieval by query expansion to retrieve relevant documents. The second block deepens the result by providing comprehensive and informative answers to the complex question using only the information spread in the long document, enabling bidirectional engagement. At various stages of the pipeline, intermediate results are presented to users to facilitate understanding of the system's reasoning. We believe this bidirectional approach brings significant advancements in terms of transparency, logical thinking, and comprehensive understanding in the field of scientific information retrieval.