# Embracing Ambiguity: Shifting the Training Target of NLI Models

arXiv: https://arxiv.org/abs/2106.03020 · HTML: https://mlsanity.com/paper/2106.03020/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 11 references that are in the map.
- First posted on arXiv 6 Jun 2021.

## Builds on (most cited first, 8 of 8)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [A Broad-Coverage Challenge Corpus for Sentence Understanding through Inference](https://mlsanity.com/paper/1704.05426/) (2017)
- [A large annotated corpus for learning natural language inference](https://mlsanity.com/paper/1508.05326/) (2015)
- [Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)](https://mlsanity.com/paper/1511.07289/) (2015)
- [Dataset Cartography: Mapping and Diagnosing Datasets with Training Dynamics](https://mlsanity.com/paper/2009.10795/) (2020)
- [Abductive Commonsense Reasoning](https://mlsanity.com/paper/1908.05739/) (2019)
- [What Can We Learn from Collective Human Opinions on Natural Language Inference Data?](https://mlsanity.com/paper/2010.03532/) (2020)
- [Uncertain Natural Language Inference](https://mlsanity.com/paper/1909.03042/) (2019)

## Built on by (most cited first, 23 of 23)

- [A Survey of Confidence Estimation and Calibration in Large Language Models](https://mlsanity.com/paper/2311.08298/) (2023)
- [We're Afraid Language Models Aren't Modeling Ambiguity](https://mlsanity.com/paper/2304.14399/) (2023)
- [Stop Measuring Calibration When Humans Disagree](https://mlsanity.com/paper/2210.16133/) (2022)
- [You Are What You Annotate: Towards Better Models through Annotator Representations](https://mlsanity.com/paper/2305.14663/) (2023)
- [Can Large Language Models Capture Dissenting Human Voices?](https://mlsanity.com/paper/2305.13788/) (2023)
- [SimANS: Simple Ambiguous Negatives Sampling for Dense Text Retrieval](https://mlsanity.com/paper/2210.11773/) (2022)
- [Symptom Identification for Interpretable Detection of Multiple Mental Disorders](https://mlsanity.com/paper/2205.11308/) (2022)
- [Interpreting Predictive Probabilities: Model Confidence or Human Label Variation?](https://mlsanity.com/paper/2402.16102/) (2024)
- [Always Tell Me The Odds: Fine-grained Conditional Probability Estimation](https://mlsanity.com/paper/2505.01595/) (2025)
- [No Strong Feelings One Way or Another: Re-operationalizing Neutrality in Natural Language Inference](https://mlsanity.com/paper/2306.09918/) (2023)
- [From Disagreement to Understanding: The Case for Ambiguity Detection in NLI](https://mlsanity.com/paper/2507.15114/) (2025)
- [LOGicalThought: Logic-Based Ontological Grounding of LLMs for High-Assurance Reasoning](https://mlsanity.com/paper/2510.01530/) (2025)
- [Possible Stories: Evaluating Situated Commonsense Reasoning under Multiple Possible Scenarios](https://mlsanity.com/paper/2209.07760/) (2022)
- [Unveiling the Multi-Annotation Process: Examining the Influence of Annotation Quantity and Instance Difficulty on Model Performance](https://mlsanity.com/paper/2310.14572/) (2023)
- [Giving AI Personalities Leads to More Human-Like Reasoning](https://mlsanity.com/paper/2502.14155/) (2025)
- [SHALA-LLM: Smartly Handling Ambiguous Labels in Aligning LLMs](https://mlsanity.com/paper/2606.05376/) (2026)
- [On Degrees of Freedom in Defining and Testing Natural Language Understanding](https://mlsanity.com/paper/2305.15130/) (2023)
- [Deep Model Compression Also Helps Models Capture Ambiguity](https://mlsanity.com/paper/2306.07061/) (2023)
- [Self-Knowledge Distillation for Learning Ambiguity](https://mlsanity.com/paper/2406.09719/) (2024)
- [An Understanding-Oriented Robust Machine Reading Comprehension Model](https://mlsanity.com/paper/2207.00187/) (2022)
- [Quantifying Divergence for Human-AI Collaboration and Cognitive Trust](https://mlsanity.com/paper/2312.08722/) (2023)
- [Annotation Entropy Predicts Per-Example Learning Dynamics in LoRA Fine-Tuning](https://mlsanity.com/paper/2604.16332/) (2026)
- [Whose Ground Truth? Embracing Ambiguity in Human-Centered AI](https://mlsanity.com/paper/2610.10805/) (2026)

## Abstract (arXiv)

Natural Language Inference (NLI) datasets contain examples with highly ambiguous labels. While many research works do not pay much attention to this fact, several recent efforts have been made to acknowledge and embrace the existence of ambiguity, such as UNLI and ChaosNLI. In this paper, we explore the option of training directly on the estimated label distribution of the annotators in the NLI task, using a learning loss based on this ambiguity distribution instead of the gold-labels. We prepare AmbiNLI, a trial dataset obtained from readily available sources, and show it is possible to reduce ChaosNLI divergence scores when finetuning on this data, a promising first step towards learning how to capture linguistic ambiguity. Additionally, we show that training on the same amount of data but targeting the ambiguity distribution instead of gold-labels can result in models that achieve higher performance and learn better representations for downstream tasks.