# Revealing the Unwritten: Visual Investigation of Beam Search Trees to Address Language Model Prompting Challenges

arXiv: https://arxiv.org/abs/2310.11252 · HTML: https://mlsanity.com/paper/2310.11252/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 39 references that are in the map.
- First posted on arXiv 17 Oct 2023.

## Builds on (most cited first, 24 of 24)

- [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)
- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [Survey of Hallucination in Natural Language Generation](https://mlsanity.com/paper/2202.03629/) (2022)
- [BLOOM: A 176B-Parameter Open-Access Multilingual Language Model](https://mlsanity.com/paper/2211.05100/) (2022)
- [A Survey on Bias and Fairness in Machine Learning](https://mlsanity.com/paper/1908.09635/) (2019)
- [Language (Technology) is Power: A Critical Survey of "Bias" in NLP](https://mlsanity.com/paper/2005.14050/) (2020)
- [Do Prompt-Based Models Really Understand the Meaning of their Prompts?](https://mlsanity.com/paper/2109.01247/) (2021)
- [Towards Understanding and Mitigating Social Biases in Language Models](https://mlsanity.com/paper/2106.13219/) (2021)
- [Surface Form Competition: Why the Highest Probability Answer Isn't Always Right](https://mlsanity.com/paper/2104.08315/) (2021)
- [Negated and Misprimed Probes for Pretrained Language Models: Birds Can Talk, But Cannot Fly](https://mlsanity.com/paper/1911.03343/) (2019)
- [Explainable Prediction of Medical Codes from Clinical Text](https://mlsanity.com/paper/1802.05695/) (2018)
- [A Survey of the State of Explainable AI for Natural Language Processing](https://mlsanity.com/paper/2010.00711/) (2020)
- [On Human Predictions with Explanations and Predictions of Machine Learning Models: A Case Study on Deception Detection](https://mlsanity.com/paper/1811.07901/) (2018)
- [Language models are not naysayers: An analysis of language models on negation benchmarks](https://mlsanity.com/paper/2306.08189/) (2023)
- [Seq2Seq-Vis: A Visual Debugging Tool for Sequence-to-Sequence Models](https://mlsanity.com/paper/1804.09299/) (2018)
- [Investigating BERT's Knowledge of Language: Five Analysis Methods with NPIs](https://mlsanity.com/paper/1909.02597/) (2019)
- [A Survey of Visual Analytics Techniques for Machine Learning](https://mlsanity.com/paper/2008.09632/) (2020)
- [Using Natural Sentences for Understanding Biases in Language Models](https://mlsanity.com/paper/2205.06303/) (2022)
- [GenNI: Human-AI Collaboration for Data-Backed Text Generation](https://mlsanity.com/paper/2110.10185/) (2021)
- [Beware the Rationalization Trap! When Language Model Explainability Diverges from our Mental Models of Language](https://mlsanity.com/paper/2207.06897/) (2022)
- [Negation, Coordination, and Quantifiers in Contextualized Language Models](https://mlsanity.com/paper/2209.07836/) (2022)
- [Mind Your Bias: A Critical Review of Bias Detection Methods for Contextual Language Models](https://mlsanity.com/paper/2211.08461/) (2022)
- [Probing Quantifier Comprehension in Large Language Models: Another Example of Inverse Scaling](https://mlsanity.com/paper/2306.07384/) (2023)

## Built on by (most cited first, 6 of 6)

- [generAItor: Tree-in-the-Loop Text Generation for Language Model Explainability and Adaptation](https://mlsanity.com/paper/2403.07627/) (2024)
- [A Large Language Model-based Computational Approach to Improve Identity-Related Write-Ups](https://mlsanity.com/paper/2312.16659/) (2023)
- [InFerActive: Interactive Tree-Based Exploration of LLM Sampling for Safety Evaluation](https://mlsanity.com/paper/2512.10234/) (2025)
- [Agile Deliberation: Concept Deliberation for Subjective Visual Classification](https://mlsanity.com/paper/2512.10821/) (2025)
- [GFlowState: Visualizing the Training of Generative Flow Networks Beyond the Reward](https://mlsanity.com/paper/2604.21830/) (2026)
- [Exposing the Unsaid: Visualizing Hidden LLM Bias through Stochastic Path Aggregation](https://mlsanity.com/paper/2606.19344/) (2026)

## Abstract (arXiv)

The growing popularity of generative language models has amplified interest in interactive methods to guide model outputs. Prompt refinement is considered one of the most effective means to influence output among these methods. We identify several challenges associated with prompting large language models, categorized into data- and model-specific, linguistic, and socio-linguistic challenges. A comprehensive examination of model outputs, including runner-up candidates and their corresponding probabilities, is needed to address these issues. The beam search tree, the prevalent algorithm to sample model outputs, can inherently supply this information. Consequently, we introduce an interactive visual method for investigating the beam search tree, facilitating analysis of the decisions made by the model during generation. We quantitatively show the value of exposing the beam search tree and present five detailed analysis scenarios addressing the identified challenges. Our methodology validates existing results and offers additional insights.