# "I Never Said That": A dataset, taxonomy and baselines on response clarity classification

arXiv: https://arxiv.org/abs/2409.13879 · HTML: https://mlsanity.com/paper/2409.13879/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 43 references that are in the map.
- First posted on arXiv 20 Sep 2024.

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

- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [Large Language Models are Zero-Shot Reasoners](https://mlsanity.com/paper/2205.11916/) (2022)
- [XLNet: Generalized Autoregressive Pretraining for Language Understanding](https://mlsanity.com/paper/1906.08237/) (2019)
- [DeBERTa: Decoding-enhanced BERT with Disentangled Attention](https://mlsanity.com/paper/2006.03654/) (2020)
- [Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context](https://mlsanity.com/paper/1901.02860/) (2019)
- [Know What You Don't Know: Unanswerable Questions for SQuAD](https://mlsanity.com/paper/1806.03822/) (2018)
- [Larger language models do in-context learning differently](https://mlsanity.com/paper/2303.03846/) (2023)
- [AmbigQA: Answering Ambiguous Open-domain Questions](https://mlsanity.com/paper/2004.10645/) (2020)
- [Stop Measuring Calibration When Humans Disagree](https://mlsanity.com/paper/2210.16133/) (2022)
- [Uncertainty in Natural Language Generation: From Theory to Applications](https://mlsanity.com/paper/2307.15703/) (2023)
- [What Comes Next? Evaluating Uncertainty in Neural Text Generators Against Human Production Variability](https://mlsanity.com/paper/2305.11707/) (2023)
- [Challenges in Information-Seeking QA: Unanswerable Questions and Paragraph Retrieval](https://mlsanity.com/paper/2010.11915/) (2020)
- [ArchivalQA: A Large-scale Benchmark Dataset for Open Domain Question Answering over Historical News Collections](https://mlsanity.com/paper/2109.03438/) (2021)
- [ConditionalQA: A Complex Reading Comprehension Dataset with Conditional Answers](https://mlsanity.com/paper/2110.06884/) (2021)
- [Did they answer? Subjective acts and intents in conversational discourse](https://mlsanity.com/paper/2104.04470/) (2021)
- [ReCO: A Large Scale Chinese Reading Comprehension Dataset on Opinion](https://mlsanity.com/paper/2006.12146/) (2020)
- [Understanding Politics via Contextualized Discourse Processing](https://mlsanity.com/paper/2012.15784/) (2020)
- [When in Doubt, Ask: Generating Answerable and Unanswerable Questions, Unsupervised](https://mlsanity.com/paper/2010.01611/) (2020)

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

- [SemEval-2026 Task 6: CLARITY -- Unmasking Political Question Evasions](https://mlsanity.com/paper/2603.14027/) (2026)
- [C-QUERI: Congressional Questions, Exchanges, and Responses in Institutions Dataset](https://mlsanity.com/paper/2509.21548/) (2025)
- [CSE-UOI at SemEval-2026 Task 6: A Two-Stage Heterogeneous Ensemble with Deliberative Complexity Gating for Political Evasion Detection](https://mlsanity.com/paper/2603.12453/) (2026)
- [SG-UniBuc-NLP at SemEval-2026 Task 6: Multi-Head RoBERTa with Chunking for Long-Context Evasion Detection](https://mlsanity.com/paper/2604.26375/) (2026)
- [CLaC at SemEval-2026 Task 6: Response Clarity Detection in Political Discourse](https://mlsanity.com/paper/2605.02170/) (2026)
- [AsymVerify at SemEval-2026 Task 6: Asymmetric Confidence-Gated Verification for Political Evasion Detection](https://mlsanity.com/paper/2607.20439/) (2026)
- [A Shaky Voice Is Not Always a Dodge: Benchmarking Textual and Vocal Evasion Detection in Earnings Calls](https://mlsanity.com/paper/2608.28040/) (2026)
- [PolERo: Studying Political Evasion in Romanian](https://mlsanity.com/paper/2609.02391/) (2026)
- [Caught in the Story: Narrative Captivity in Multi-turn LLMs Conversation](https://mlsanity.com/paper/2609.03407/) (2026)

## Abstract (arXiv)

Equivocation and ambiguity in public speech are well-studied discourse phenomena, especially in political science and analysis of political interviews. Inspired by the well-grounded theory on equivocation, we aim to resolve the closely related problem of response clarity in questions extracted from political interviews, leveraging the capabilities of Large Language Models (LLMs) and human expertise. To this end, we introduce a novel taxonomy that frames the task of detecting and classifying response clarity and a corresponding clarity classification dataset which consists of question-answer (QA) pairs drawn from political interviews and annotated accordingly. Our proposed two-level taxonomy addresses the clarity of a response in terms of the information provided for a given question (high-level) and also provides a fine-grained taxonomy of evasion techniques that relate to unclear, ambiguous responses (lower-level). We combine ChatGPT and human annotators to collect, validate and annotate discrete QA pairs from political interviews, to be used for our newly introduced response clarity task. We provide a detailed analysis and conduct several experiments with different model architectures, sizes and adaptation methods to gain insights and establish new baselines over the proposed dataset and task.