Papers by Da-shan Shiu
RAD-Bench: Evaluating Large Language Models’ Capabilities in Retrieval Augmented Dialogues (2025.naacl-industry)
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| Challenge: | Existing benchmarks assess LLMs' chat abilities in multi-turn dialogues or their use of retrieval for augmented responses in limited tasks such as knowledge QA or numeric reasoning. |
| Approach: | They propose a benchmark to evaluate LLMs' capabilities in multi-turn dialogues following retrievals. |
| Outcome: | The proposed benchmark evaluates LLMs' ability to perform in multi-turn dialogues following retrievals over 6 representative scenarios. |
Enhancing Function-Calling Capabilities in LLMs: Strategies for Prompt Formats, Data Integration, and Multilingual Translation (2025.naacl-industry)
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| Challenge: | Large language models (LLMs) have significantly advanced autonomous agents, particularly in zero-shot tool usage, also known as function calling. |
| Approach: | They propose to integrate function descriptions into prompt formats and introduce a new Decision Token for conditional prompts. |
| Outcome: | The proposed decision token improves function-calling accuracy and relevance detection and a translation pipeline overcomes multilingual limitations. |
Let’s Fuse Step by Step: A Generative Fusion Decoding Algorithm with LLMs for Robust and Instruction-Aware ASR and OCR (2025.findings-acl)
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| Challenge: | Various fusion strategies have been explored for integration of large language models into multi-modal systems. |
| Approach: | They propose a framework for deep fusion decoding that integrates large language models into cross-modal text recognition systems. |
| Outcome: | The proposed framework surpasses cascaded methods in English and Mandarin, and significantly reduces WERs by 17.7%. |