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Large language models (LLMs), especially when instruction-tuned for chat, have become part of our daily lives, freeing people from the process of searching, extracting, and integrating information from multiple sources by offering a…

计算与语言 · 计算机科学 2024-11-01 Yuxia Wang , Minghan Wang , Muhammad Arslan Manzoor , Fei Liu , Georgi Georgiev , Rocktim Jyoti Das , Preslav Nakov

Generative large language models (LLMs) can be a powerful tool for augmenting text annotation procedures, but their performance varies across annotation tasks due to prompt quality, text data idiosyncrasies, and conceptual difficulty.…

计算与语言 · 计算机科学 2023-06-02 Nicholas Pangakis , Samuel Wolken , Neil Fasching

Large language models (LLMs) are popular for high-quality text generation but can produce harmful content, even when aligned with human values through reinforcement learning. Adversarial prompts can bypass their safety measures. We propose…

计算与语言 · 计算机科学 2024-05-03 Mansi Phute , Alec Helbling , Matthew Hull , ShengYun Peng , Sebastian Szyller , Cory Cornelius , Duen Horng Chau

Large Language Models (LLMs) are revolutionizing the landscape of Generative Artificial Intelligence (GenAI), with innovative LLM-backed solutions emerging rapidly. However, when applied to database technologies, specifically query…

新兴技术 · 计算机科学 2024-11-19 Siraj Munir , Alessandro Aldini

Conversational agents show the promise to allow users to interact with mobile devices using language. However, to perform diverse UI tasks with natural language, developers typically need to create separate datasets and models for each…

人机交互 · 计算机科学 2023-02-21 Bryan Wang , Gang Li , Yang Li

Ads Content Safety at Google requires classifying billions of ads for Google Ads content policies. Consistent and accurate policy enforcement is important for advertiser experience and user safety and it is a challenging problem, so there…

信息检索 · 计算机科学 2024-09-25 Joseph Wallace , Tushar Dogra , Wei Qiao , Yuan Wang

Using Large Language Models (LLMs) to simulate user opinions has received growing attention. Yet LLMs, especially trained with reinforcement learning from human feedback (RLHF), are known to exhibit biases toward dominant viewpoints,…

计算与语言 · 计算机科学 2025-12-09 Ziyun Yu , Yiru Zhou , Chen Zhao , Hongyi Wen

The remarkable success of pretrained language models has motivated the study of what kinds of knowledge these models learn during pretraining. Reformulating tasks as fill-in-the-blanks problems (e.g., cloze tests) is a natural approach for…

计算与语言 · 计算机科学 2020-11-10 Taylor Shin , Yasaman Razeghi , Robert L. Logan , Eric Wallace , Sameer Singh

Conversational multi-doc question answering aims to answer specific questions based on the retrieved documents as well as the contextual conversations. In this paper, we introduce our winning approach for the "Conversational Multi-Doc QA"…

计算与语言 · 计算机科学 2024-02-29 Yiming Li , Zhao Zhang

Current interactive systems with natural language interfaces lack the ability to understand a complex information-seeking request which expresses several implicit constraints at once, and there is no prior information about user preferences…

This survey reviews how large language models (LLMs) are transforming synthetic training data generation in both natural language and code domains. By producing artificial but task-relevant examples, these models can significantly augment…

计算与语言 · 计算机科学 2025-11-21 Mihai Nadas , Laura Diosan , Andreea Tomescu

A commit message is a textual description of the code changes in a commit, which is a key part of the Git version control system (VCS). It captures the essence of software updating. Therefore, it can help developers understand code…

软件工程 · 计算机科学 2024-01-17 Linghao Zhang , Jingshu Zhao , Chong Wang , Peng Liang

This paper investigates the role of dynamic external knowledge integration in improving counter-argument generation using Large Language Models (LLMs). While LLMs have shown promise in argumentative tasks, their tendency to generate…

计算与语言 · 计算机科学 2025-06-23 Anar Yeginbergen , Maite Oronoz , Rodrigo Agerri

Languages are shaped by the inductive biases of their users. Using a classical referential game, we investigate how artificial languages evolve when optimised for inductive biases in humans and large language models (LLMs) via Human-Human,…

计算与语言 · 计算机科学 2025-05-29 Tom Kouwenhoven , Max Peeperkorn , Roy de Kleijn , Tessa Verhoef

Benefiting from diverse instruction datasets, contemporary Large Language Models (LLMs) perform effectively as AI assistants in collaborating with humans. However, LLMs still struggle to generate natural and colloquial responses in…

计算与语言 · 计算机科学 2024-10-16 Renliang Sun , Mengyuan Liu , Shiping Yang , Rui Wang , Junqing He , Jiaxing Zhang

The integration of Large Language Models (LLMs) into chatbot-like search engines poses new challenges for governing, assessing, and scrutinizing the content output by these online entities, especially in light of the Digital Service Act…

The purpose of this study is to assess how large language models (LLMs) can be used for fact-checking and contribute to the broader debate on the use of automated means for veracity identification. To achieve this purpose, we use AI…

Large Language Models (LLMs) have quickly become an invaluable assistant for a variety of tasks. However, their effectiveness is constrained by their ability to tailor responses to human preferences and behaviors via personalization. Prior…

计算与语言 · 计算机科学 2024-11-21 Lucie Charlotte Magister , Katherine Metcalf , Yizhe Zhang , Maartje ter Hoeve

Large language models (LLMs) have shown success in generating high-quality responses. In order to achieve better alignment with LLMs with human preference, various works are proposed based on specific optimization process, which, however,…

计算与语言 · 计算机科学 2024-09-04 Zhuo Li , Yuhao Du , Jinpeng Hu , Xiang Wan , Anningzhe Gao

Sponsored search represents a major source of revenue for web search engines. This popular advertising model brings a unique possibility for advertisers to target users' immediate intent communicated through a search query, usually by…