# SylloBio-NLI: Evaluating Large Language Models on Biomedical Syllogistic Reasoning

arXiv: https://arxiv.org/abs/2410.14399 · HTML: https://mlsanity.com/paper/2410.14399/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Placed in Clinical and biomedical applications, one of 17 directions in LLM reasoning.
- Builds on 7 of its 35 references that are in the map.
- First posted on arXiv 18 Oct 2024.

## Placed in

- LLM reasoning / [Clinical and biomedical applications](https://mlsanity.com/lr/clinical-and-biomedical-applications/)

## Builds on (most cited first, 7 of 7)

- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [Mistral 7B](https://mlsanity.com/paper/2310.06825/) (2023)
- [Language models show human-like content effects on reasoning tasks](https://mlsanity.com/paper/2207.07051/) (2022)
- [Natural Language Reasoning, A Survey](https://mlsanity.com/paper/2303.14725/) (2023)
- [Reasoning Circuits in Language Models: A Mechanistic Interpretation of Syllogistic Inference](https://mlsanity.com/paper/2408.08590/) (2024)
- [GLoRE: Evaluating Logical Reasoning of Large Language Models](https://mlsanity.com/paper/2310.09107/) (2023)
- [A Systematic Analysis of Large Language Models as Soft Reasoners: The Case of Syllogistic Inferences](https://mlsanity.com/paper/2406.11341/) (2024)

## Built on by (most cited first, 9 of 9)

- [Mitigating Content Effects on Reasoning in Language Models through Fine-Grained Activation Steering](https://mlsanity.com/paper/2505.12189/) (2025)
- [Integrating Expert Knowledge into Logical Programs via LLMs](https://mlsanity.com/paper/2502.12275/) (2025)
- [An AI Monkey Gets Grapes for Sure -- Sphere Neural Networks for Reliable Decision-Making](https://mlsanity.com/paper/2601.00142/) (2026)
- [FregeLogic at SemEval 2026 Task 11: A Hybrid Neuro-Symbolic Architecture for Content-Robust Syllogistic Validity Prediction](https://mlsanity.com/paper/2604.18328/) (2026)
- [SEF-CLGC at SemEval-2026 Task 11: Logical Notation Impact on Language Model Performance](https://mlsanity.com/paper/2606.09157/) (2026)
- [Data-driven Machine Learning Cannot Reach Symbolic-level Logical Reasoning -- The Limit of the Scaling Law](https://mlsanity.com/paper/2606.26454/) (2026)
- [SciR: A Controllable Benchmark for Scientific Reasoning in LLMs](https://mlsanity.com/paper/2606.13020/) (2026)
- [Logical Judgments Under Pressure: Diagnosing Syllogistic Stability with Learned Soft Prefixes](https://mlsanity.com/paper/2607.18228/) (2026)
- [Are you Talking Logic to Me? Assessing Language Models Syllogistic Reasoning Capabilities](https://mlsanity.com/paper/2608.12374/) (2026)

## Abstract (arXiv)

Syllogistic reasoning is crucial for Natural Language Inference (NLI). This capability is particularly significant in specialized domains such as biomedicine, where it can support automatic evidence interpretation and scientific discovery. This paper presents SylloBio-NLI, a novel framework that leverages external ontologies to systematically instantiate diverse syllogistic arguments for biomedical NLI. We employ SylloBio-NLI to evaluate Large Language Models (LLMs) on identifying valid conclusions and extracting supporting evidence across 28 syllogistic schemes instantiated with human genome pathways. Extensive experiments reveal that biomedical syllogistic reasoning is particularly challenging for zero-shot LLMs, which achieve an average accuracy between 70% on generalized modus ponens and 23% on disjunctive syllogism. At the same time, we found that few-shot prompting can boost the performance of different LLMs, including Gemma (+14%) and LLama-3 (+43%). However, a deeper analysis shows that both techniques exhibit high sensitivity to superficial lexical variations, highlighting a dependency between reliability, models' architecture, and pre-training regime. Overall, our results indicate that, while in-context examples have the potential to elicit syllogistic reasoning in LLMs, existing models are still far from achieving the robustness and consistency required for safe biomedical NLI applications.