Papers by Shayan Ali Akbar
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. |
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. |