Papers by Ayana Niwa
Disentangling the Effects of Unlearning in Measuring Parametric Faithfulness of Chain-of-Thought (2026.acl-srw)
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Ryo Mitsuhashi, Gaku Morio, Ayana Niwa, Masahiro Kaneko, Kentaro Inui, Terufumi Morishita, Yuta Koreeda, Yasuhiro Sogawa
| Challenge: | Chain-of-Thought (CoT) has been debated as a model's faithfulness to internal reasoning process. |
| Approach: | They propose to use unlearning to measure parametric faithfulness of models by adjusting for unintended artifacts of unlearning. |
| Outcome: | The proposed metric accounts for the unintended artifacts of unlearning and shows that it is non-negligible. |
AmbigNLG: Addressing Task Ambiguity in Instruction for NLG (2024.emnlp-main)
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| Challenge: | AmbigNLG is a novel task designed to tackle task ambiguity in instructions for NLG . ambiguous instructions often impede the performance of Large Language Models (LLMs) . |
| Approach: | They propose an ambiguity taxonomy that categorizes different types of instruction ambiguities and refines initial instructions with clearer specifications. |
| Outcome: | The proposed task improves alignment of generated text with user expectations, achieving 15.02-point increase in ROUGE scores. |
ExaGPT: Example-Based Machine-Generated Text Detection for Human Interpretability (2026.findings-acl)
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| Challenge: | Existing interpretable detectors are not aligned with the human decision-making process and fail to offer evidence that users easily understand. |
| Approach: | They propose an interpretable detection approach that checks whether a text is human-written or LLM-generated by checking whether it shares more similar spans with human-generated texts. |
| Outcome: | ExaGPT outperforms interpretable detectors by +37.0 points at a false positive rate of 1%. |
Interpretability for Language Learners Using Example-Based Grammatical Error Correction (2022.acl-long)
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| Challenge: | Existing neural-based GEC models mainly aim at improving accuracy, but their interpretability has not been explored. |
| Approach: | They propose an example-based method that generates corrections using retrieved examples. |
| Outcome: | The proposed method improves interpretability and supports language learners. |
Rectifying Belief Space via Unlearning to Harness LLMs’ Reasoning (2025.findings-acl)
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| Challenge: | Large Language Models (LLMs) exhibit sophisticated reasoning yet still generate incorrect answers. |
| Approach: | They propose a belief space rectification framework that suppresses spurious beliefs and enhances true ones to reduce erroneous reasoning and generalization. |
| Outcome: | The proposed framework reduces erroneous reasoning and improves generalization on three QA datasets and three LLMs. |