36 citations · 50 across the 8 of their papers we have counts for
10 papers · 1 filter
Advancing Large Language Models to Capture Varied Speaking Styles and Respond Properly in Spoken Conversations
Guan-Ting Lin, Cheng-Han Chiang, Hung-yi Lee
In spoken dialogue, even if two current turns are the same sentence, their responses might still differ when they are spoken in different styles. The spoken styles, containing para…
Merging Facts, Crafting Fallacies: Evaluating the Contradictory Nature of Aggregated Factual Claims in Long-Form Generations
Cheng-Han Chiang, Hung-yi Lee
Long-form generations from large language models (LLMs) contain a mix of factual and non-factual claims, making evaluating factuality difficult. Prior works evaluate the factuality…
Over-Reasoning and Redundant Calculation of Large Language Models
Cheng-Han Chiang, Hung-yi Lee
Large language models (LLMs) can solve problems step-by-step. While this chain-of-thought (CoT) reasoning boosts LLMs' performance, it is unclear if LLMs \textit{know} when to use…
A Closer Look into Automatic Evaluation Using Large Language Models
Cheng-Han Chiang, Hung-yi Lee
Using large language models (LLMs) to evaluate text quality has recently gained popularity. Some prior works explore the idea of using LLMs for evaluation, while they differ in som…
Revealing the Blind Spot of Sentence Encoder Evaluation by HEROS
Cheng-Han Chiang, Yung-Sung Chuang, James Glass +1
Existing sentence textual similarity benchmark datasets only use a single number to summarize how similar the sentence encoder's decision is to humans'. However, it is unclear what…
Can Large Language Models Be an Alternative to Human Evaluations?
Cheng-Han Chiang, Hung-yi Lee
Human evaluation is indispensable and inevitable for assessing the quality of texts generated by machine learning models or written by humans. However, human evaluation is very dif…