Papers by Md Mosharaf Hossain
Predicting the Focus of Negation: Model and Error Analysis (2020.acl-main)
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| Challenge: | Experimental results show that a scope detector can predict the focus of negation . negation is a complex phenomenon present in all human languages . |
| Approach: | They propose to leverage a scope detector to introduce the scope of negation as an additional input to the neural network. |
| Outcome: | The proposed model obtains the best results to date, and analyzes errors depending on scope and context information. |
HalluMeasure: Fine-grained Hallucination Measurement Using Chain-of-Thought Reasoning (2024.emnlp-main)
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Shayan Ali Akbar, Md Mosharaf Hossain, Tess Wood, Si-Chi Chin, Erica Salinas, Victor Alvarez, Erwin Cornejo
| Challenge: | HalluMeasure is a new LLM-based hallucination detection mechanism that decomposes an LLM response into atomic claims and evaluates each claim against the provided reference context. |
| Approach: | They propose a new LLM-based hallucination detection mechanism that decomposes an LLM response into atomic claims and evaluates each atomic claim against the provided reference context. |
| Outcome: | The proposed model can detect 3 major categories of hallucinations and 10 more specific subtypes which help to identify reasons behind the hallucinian errors. |
Leveraging Affirmative Interpretations from Negation Improves Natural Language Understanding (2022.emnlp-main)
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| Challenge: | Negation poses a challenge in many natural language understanding tasks . leveraging sentences with negation and affirmative interpretations is beneficial for many tasks involving humans . |
| Approach: | They propose to collect negated sentences and their affirmative interpretations and leverage them to build a plug-and-play neural generator that generates an affirmative interpreter. |
| Outcome: | The proposed method does not require manual effort and does not impact other tasks. |
It’s not a Non-Issue: Negation as a Source of Error in Machine Translation (2020.findings-emnlp)
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| Challenge: | In this study, we focus on negation, a universal, core property of human language that affects the semantics of an utterance. |
| Approach: | They focus on negation, a universal, core property of human language that affects semantics of an utterance. |
| Outcome: | The proposed method improves translation quality by 60% in some cases . the authors also provide a linguistically motivated analysis that directly explains the majority of the results. |
An Analysis of Natural Language Inference Benchmarks through the Lens of Negation (2020.emnlp-main)
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| Challenge: | Existing benchmarks for natural language inference ignore negations and can make inferences that are difficult to make. |
| Approach: | They propose a new benchmark for natural language inference in which negation plays a critical role. |
| Outcome: | The proposed benchmarks show that negation plays a critical role in inference judgments. |
An Analysis of Negation in Natural Language Understanding Corpora (2022.acl-short)
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| Challenge: | Using annotator-generated examples, one can evaluate systems with synthetic language that is not representative of language in the wild. |
| Approach: | They analyze negation in eight popular corpora spanning six natural language understanding tasks. |
| Outcome: | The proposed corpora have few negations compared to general-purpose English and are often unimportant . state-of-the-art transformers obtain significantly worse results with instances that contain negation, especially if the negations are important. |
SEEval: Advancing LLM Text Evaluation Efficiency and Accuracy through Self-Explanation Prompting (2025.findings-naacl)
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| Challenge: | Large language models (LLMs) have achieved remarkable success in various natural language generation tasks, but their performance in automatic text evaluation is not ready as human replacements. |
| Approach: | They propose a prompt-based text evaluator that incorporates self-explanation, a metacognitive strategy, to enhance automatic text evaluation. |
| Outcome: | The proposed method achieves competitive and often superior performance compared to the two state-of-the-art baselines – G-Eval and Analyze-Rate – and is 20 times more efficient in terms of run-time. |
A Question-Answer Driven Approach to Reveal Affirmative Interpretations from Verbal Negations (2022.findings-naacl)
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Md Mosharaf Hossain, Luke Holman, Anusha Kakileti, Tiffany Kao, Nathan Brito, Aaron Mathews, Eduardo Blanco
| Challenge: | Negations carry affirmative meanings, which are difficult to process and understand by humans. |
| Approach: | They propose a question-answer driven approach to reveal affirmative interpretations from verbal negations. |
| Outcome: | The proposed approach is based on a natural language inference task . it shows that state-of-the-art transformers are insufficient to reveal affirmative interpretations . |