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IEEE/ICACT20240321 Slide.05        [Big Slide]	Oral Presentation
	key points and questions on using large language models (LLMs) for fine-grained, interpretable quality estimation: Can chain-of-thought (CoT) prompting make LLMs competitive with state-of-the-art models for segment-level machine translation evaluation? What is the optimal CoT prompt design tracing model reasoning step-by-step? Formally, LLM quality estimation (QE) is f(perplexity, token, sentence) scores. We compare: 3 single-step models using each feature individually 2 CoT models combining perplexity + token and perplexity + token + sentence How to design effective single-step vs CoT prompts? We hypothesize CoT explanations can increase model interpretability. However, common approaches like XLM-R and TeacherSim overly optimize attention to the last token, reducing meaningfulness. Key questions are: Can CoT prompting improve on single-step performance? How to design prompts guiding reasoning and explanation? Can CoT interpretability outperform attention-based methods? We aim to thoroughly evaluate knowledge prompting strategies for segment-level quality estimation using LLMs.

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