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Tianhuang Su

2 accepted papers

2025

Review-Instruct: A Review-Driven Multi-Turn Conversations Generation Method for Large Language Models

ACL 2025finding

The effectiveness of large language models (LLMs) in conversational AI is hindered by their reliance on single-turn supervised fine-tuning (SFT) data, which limits contextual coherence in multi-turn dialogues. Existing methods for generating multi-turn dialogue data struggle to ensure both diversity…

2024

Teaching Small Language Models to Reason for Knowledge-Intensive Multi-Hop Question Answering

ACL 2024findings

Large Language Models (LLMs) can teach small language models (SLMs) to solve complex reasoning tasks (e.g., mathematical question answering) by Chain-of-thought Distillation (CoTD). Specifically, CoTD fine-tunes SLMs by utilizing rationales generated from LLMs such as ChatGPT. However, CoTD has cert…