Search Results for author: Haonan Li

Found 23 papers, 15 papers with code

CULG: Commercial Universal Language Generation

no code implementations NAACL (ACL) 2022 Haonan Li, Yameng Huang, Yeyun Gong, Jian Jiao, Ruofei Zhang, Timothy Baldwin, Nan Duan

Pre-trained language models (PLMs) have dramatically improved performance for many natural language processing (NLP) tasks in domains such as finance and healthcare.

Marketing Text Generation

KFCNet: Knowledge Filtering and Contrastive Learning for Generative Commonsense Reasoning

no code implementations Findings (EMNLP) 2021 Haonan Li, Yeyun Gong, Jian Jiao, Ruofei Zhang, Timothy Baldwin, Nan Duan

Pre-trained language models have led to substantial gains over a broad range of natural language processing (NLP) tasks, but have been shown to have limitations for natural language generation tasks with high-quality requirements on the output, such as commonsense generation and ad keyword generation.

Contrastive Learning Text Generation

Fact-Checking the Output of Large Language Models via Token-Level Uncertainty Quantification

no code implementations7 Mar 2024 Ekaterina Fadeeva, Aleksandr Rubashevskii, Artem Shelmanov, Sergey Petrakov, Haonan Li, Hamdy Mubarak, Evgenii Tsymbalov, Gleb Kuzmin, Alexander Panchenko, Timothy Baldwin, Preslav Nakov, Maxim Panov

Uncertainty scores leverage information encapsulated in the output of a neural network or its layers to detect unreliable predictions, and we show that they can be used to fact-check the atomic claims in the LLM output.

Fact Checking Hallucination +1

A Chinese Dataset for Evaluating the Safeguards in Large Language Models

no code implementations19 Feb 2024 Yuxia Wang, Zenan Zhai, Haonan Li, Xudong Han, Lizhi Lin, Zhenxuan Zhang, Jingru Zhao, Preslav Nakov, Timothy Baldwin

Previous studies have proposed comprehensive taxonomies of the risks posed by LLMs, as well as corresponding prompts that can be used to examine the safety mechanisms of LLMs.

Learning From Failure: Integrating Negative Examples when Fine-tuning Large Language Models as Agents

1 code implementation18 Feb 2024 Renxi Wang, Haonan Li, Xudong Han, Yixuan Zhang, Timothy Baldwin

Large language models (LLMs) have achieved success in acting as agents, which interact with environments through tools like search engines.

Mathematical Reasoning Multi-hop Question Answering +1

Location Aware Modular Biencoder for Tourism Question Answering

1 code implementation4 Jan 2024 Haonan Li, Martin Tomko, Timothy Baldwin

To overcome this, we propose treating the QA task as a dense vector retrieval problem, where we encode questions and POIs separately and retrieve the most relevant POIs for a question by utilizing embedding space similarity.

Question Answering Retrieval

Can Large Language Model Comprehend Ancient Chinese? A Preliminary Test on ACLUE

1 code implementation14 Oct 2023 Yixuan Zhang, Haonan Li

To bridge this gap, we present ACLUE, an evaluation benchmark designed to assess the capability of language models in comprehending ancient Chinese.

Language Modelling Large Language Model

Large Language Models Only Pass Primary School Exams in Indonesia: A Comprehensive Test on IndoMMLU

1 code implementation7 Oct 2023 Fajri Koto, Nurul Aisyah, Haonan Li, Timothy Baldwin

In this work, we introduce IndoMMLU, the first multi-task language understanding benchmark for Indonesian culture and languages, which consists of questions from primary school to university entrance exams in Indonesia.

Multi-task Language Understanding World Knowledge

Do-Not-Answer: A Dataset for Evaluating Safeguards in LLMs

1 code implementation25 Aug 2023 Yuxia Wang, Haonan Li, Xudong Han, Preslav Nakov, Timothy Baldwin

With the rapid evolution of large language models (LLMs), new and hard-to-predict harmful capabilities are emerging.

The Hitchhiker's Guide to Program Analysis: A Journey with Large Language Models

no code implementations1 Aug 2023 Haonan Li, Yu Hao, Yizhuo Zhai, Zhiyun Qian

By carefully designing the framework and the prompts, we are able to overcome a number of challenges, including bug-specific modeling, the large problem scope, the non-deterministic nature of LLMs, etc.

CMMLU: Measuring massive multitask language understanding in Chinese

1 code implementation15 Jun 2023 Haonan Li, Yixuan Zhang, Fajri Koto, Yifei Yang, Hai Zhao, Yeyun Gong, Nan Duan, Timothy Baldwin

As the capabilities of large language models (LLMs) continue to advance, evaluating their performance becomes increasingly crucial and challenging.

Large Language Model

Bactrian-X: Multilingual Replicable Instruction-Following Models with Low-Rank Adaptation

1 code implementation24 May 2023 Haonan Li, Fajri Koto, Minghao Wu, Alham Fikri Aji, Timothy Baldwin

However, research on multilingual instruction tuning has been limited due to the scarcity of high-quality instruction-response datasets across different languages.

Instruction Following

Sentiment-Aware Word and Sentence Level Pre-training for Sentiment Analysis

1 code implementation18 Oct 2022 Shuai Fan, Chen Lin, Haonan Li, Zhenghao Lin, Jinsong Su, Hang Zhang, Yeyun Gong, Jian Guo, Nan Duan

Most existing pre-trained language representation models (PLMs) are sub-optimal in sentiment analysis tasks, as they capture the sentiment information from word-level while under-considering sentence-level information.

Contrastive Learning Language Modelling +3

Persia: An Open, Hybrid System Scaling Deep Learning-based Recommenders up to 100 Trillion Parameters

1 code implementation10 Nov 2021 Xiangru Lian, Binhang Yuan, XueFeng Zhu, Yulong Wang, Yongjun He, Honghuan Wu, Lei Sun, Haodong Lyu, Chengjun Liu, Xing Dong, Yiqiao Liao, Mingnan Luo, Congfei Zhang, Jingru Xie, Haonan Li, Lei Chen, Renjie Huang, Jianying Lin, Chengchun Shu, Xuezhong Qiu, Zhishan Liu, Dongying Kong, Lei Yuan, Hai Yu, Sen yang, Ce Zhang, Ji Liu

Specifically, in order to ensure both the training efficiency and the training accuracy, we design a novel hybrid training algorithm, where the embedding layer and the dense neural network are handled by different synchronization mechanisms; then we build a system called Persia (short for parallel recommendation training system with hybrid acceleration) to support this hybrid training algorithm.

Recommendation Systems

KFCNet: Knowledge Filtering and Contrastive Learning Network for Generative Commonsense Reasoning

no code implementations14 Sep 2021 Haonan Li, Yeyun Gong, Jian Jiao, Ruofei Zhang, Timothy Baldwin, Nan Duan

Pre-trained language models have led to substantial gains over a broad range of natural language processing (NLP) tasks, but have been shown to have limitations for natural language generation tasks with high-quality requirements on the output, such as commonsense generation and ad keyword generation.

Contrastive Learning Text Generation

Target Word Masking for Location Metonymy Resolution

1 code implementation COLING 2020 Haonan Li, Maria Vasardani, Martin Tomko, Timothy Baldwin

Existing metonymy resolution approaches rely on features extracted from external resources like dictionaries and hand-crafted lexical resources.

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