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Minzhi Li

7 accepted papers

2025

Distilling an End-to-End Voice Assistant Without Instruction Training Data

ACL 2025long

Voice assistants, such as Siri and Google Assistant, typically model audio and text separately, resulting in lost speech information and increased complexity. Recent efforts to address this with end-to-end Speech Large Language Models (speech-in, text-out) trained with supervised finetuning (SFT) ha…

Cited by 0SourcePDFScholar
2025

DnA-Eval: Enhancing Large Language Model Evaluation through Decomposition and Aggregation

COLING 2025main

The acceleration of Large Language Models (LLMs) research has opened up new possibilities for evaluating generated text. Though LLMs serve as scalable and economical evaluators, how reliable these evaluators is still under-explored. Prior research efforts in the meta-evaluation of LLMs as judges lim…

Cited by 4SourcePDFScholar
2025

Mind the Gap: Static and Interactive Evaluations of Large Audio Models

ACL 2025long

As AI chatbots become ubiquitous, voice interaction presents a compelling way to enable rapid, high-bandwidth communication for both semantic and social signals. This has driven research into Large Audio Models (LAMs) to power voice-native experiences. However, aligning LAM development with user goa…

2024

Social Intelligence Data Infrastructure: Structuring the Present and Navigating the Future

ACL 2024findings

As Natural Language Processing (NLP) systems become increasingly integrated into human social life, these technologies will need to increasingly rely on social intelligence. Although there are many valuable datasets that benchmark isolated dimensions of social intelligence, there does not yet exist…

Cited by 6SourcePDFScholar
2023

CoAnnotating: Uncertainty-Guided Work Allocation between Human and Large Language Models for Data Annotation

EMNLP 2023long main

Annotated data plays a critical role in Natural Language Processing (NLP) in training models and evaluating their performance. Given recent developments in Large Language Models (LLMs), models such as ChatGPT demonstrate zero-shot capability on many text-annotation tasks, comparable with or even exc…

Cited by 0SourcecodeScholar
2023

Retrieving Multimodal Information for Augmented Generation: A Survey

EMNLP 2023long findings

As Large Language Models (LLMs) become popular, there emerged an important trend of using multimodality to augment the LLMs' generation ability, which enables LLMs to better interact with the world. However, there lacks a unified perception of at which stage and how to incorporate different modaliti…

Cited by 0SourceScholar