Papers Sentence Completion
“Sentence Completion” 태그가 달린 논문 100편 · 필터 해제
Evaluating Developmental Cognition Capabilities of LLMs
Conversational AI is increasingly personalized around users' preferences, histories, goals, and knowledge, but much less around how users interpret and take up model outputs to construct and understand their reality. We …
Sentence CompletionStroke Lesions as a Rosetta Stone for Language Model Interpretability
Large language models (LLMs) have achieved remarkable capabilities, yet methods to verify which model components are truly necessary for language function remain limited. Current interpretability approaches rely on inter…
Sentence CompletionQueerGen: How LLMs Reflect Societal Norms on Gender and Sexuality in Sentence Completion Tasks
This paper examines how Large Language Models (LLMs) reproduce societal norms, particularly heterocisnormativity, and how these norms translate into measurable biases in their text generations. We investigate whether exp…
Sentence CompletionDecoding Workload and Agreement From EEG During Spoken Dialogue With Conversational AI
Passive brain-computer interfaces offer a potential source of implicit feedback for alignment of large language models, but most mental state decoding has been done in controlled tasks. This paper investigates whether es…
Sentence CompletionFIBER: A Multilingual Evaluation Resource for Factual Inference Bias
Large language models are widely used across domains, yet there are concerns about their factual reliability and biases. Factual knowledge probing offers a systematic means to evaluate these aspects. Most existing benchm…
Sentence CompletionBuilding Domain-Specific Small Language Models via Guided Data Generation
Large Language Models (LLMs) have shown remarkable success in supporting a wide range of knowledge-intensive tasks. In specialized domains, there is growing interest in leveraging LLMs to assist subject matter experts wi…
Synthetic Data GenerationSentence CompletionQuestion AnsweringDomain AdaptationAnalogical Structure, Minimal Contextual Cues and Contrastive Distractors: Input Design for Sample-Efficient Linguistic Rule Induction
Large language models achieve strong performance on many tasks, but their training makes it hard to see which properties of the input support efficient linguistic rule learning. We ask how three cognitively-inspired prin…
Contrastive LearningSentence CompletionPerCoR: Evaluating Commonsense Reasoning in Persian via Multiple-Choice Sentence Completion
We introduced PerCoR (Persian Commonsense Reasoning), the first large-scale Persian benchmark for commonsense reasoning. PerCoR contains 106K multiple-choice sentence-completion problems drawn from more than forty news, …
Sentence CompletionInvestigating Gender Bias in LLM-Generated Stories via Psychological Stereotypes
As Large Language Models (LLMs) are increasingly used across different applications, concerns about their potential to amplify gender biases in various tasks are rising. Prior research has often probed gender bias using …
Sentence CompletionQuestion AnsweringEvaluating Gender Bias in Large Language Models
Gender bias in artificial intelligence has become an important issue, particularly in the context of language models used in communication-oriented applications. This study examines the extent to which Large Language Mod…
Model SelectionSentenceSentence CompletionKatzBot: Revolutionizing Academic Chatbot for Enhanced Communication
Effective communication within universities is crucial for addressing the diverse information needs of students, alumni, and external stakeholders. However, existing chatbot systems often fail to deliver accurate, contex…
ChatbotLanguage ModelingLanguage ModellingLarge Language Model+2BiasAlert: A Plug-and-play Tool for Social Bias Detection in LLMs
Evaluating the bias in Large Language Models (LLMs) becomes increasingly crucial with their rapid development. However, existing evaluation methods rely on fixed-form outputs and cannot adapt to the flexible open-text ge…
Bias DetectionQuestion AnsweringSentenceSentence Completion+1Ranking LLMs by compression
We conceptualize the process of understanding as information compression, and propose a method for ranking large language models (LLMs) based on lossless data compression. We demonstrate the equivalence of compression le…
coreference-resolutionCoreference ResolutionData CompressionLanguage Modeling+5Mixture-of-Subspaces in Low-Rank Adaptation
In this paper, we introduce a subspace-inspired Low-Rank Adaptation (LoRA) method, which is computationally efficient, easy to implement, and readily applicable to large language, multimodal, and diffusion models. Initia…
Common Sense ReasoningImage GenerationQuestion AnsweringSentence Completion+3Enhancing Bangla Language Next Word Prediction and Sentence Completion through Extended RNN with Bi-LSTM Model On N-gram Language
Texting stands out as the most prominent form of communication worldwide. Individual spend significant amount of time writing whole texts to send emails or write something on social media, which is time consuming in this…
PredictionSentenceSentence CompletionMixLoRA: Enhancing Large Language Models Fine-Tuning with LoRA-based Mixture of Experts
Fine-tuning Large Language Models (LLMs) is a common practice to adapt pre-trained models for specific applications. While methods like LoRA have effectively addressed GPU memory constraints during fine-tuning, their per…
Common Sense ReasoningGPUMixture-of-ExpertsMulti-Task Learning+4Language Model Sentence Completion with a Parser-Driven Rhetorical Control Method
Controlled text generation (CTG) seeks to guide large language model (LLM) output to produce text that conforms to desired criteria. The current study presents a novel CTG algorithm that enforces adherence toward specifi…
Language ModelingLanguage ModellingLarge Language ModelSentence+2Parameter-Efficient Sparsity Crafting from Dense to Mixture-of-Experts for Instruction Tuning on General Tasks
Large language models (LLMs) have demonstrated considerable proficiency in general natural language processing (NLP) tasks. Instruction tuning, a successful paradigm, enhances the ability of LLMs to follow natural langua…
Arithmetic ReasoningCode GenerationCommon Sense ReasoningGPU+5Illuminating the Black Box: A Psychometric Investigation into the Multifaceted Nature of Large Language Models
This study explores the idea of AI Personality or AInality suggesting that Large Language Models (LLMs) exhibit patterns similar to human personalities. Assuming that LLMs share these patterns with humans, we investigate…
SentenceSentence CompletionLLM in a flash: Efficient Large Language Model Inference with Limited Memory
Large language models (LLMs) are central to modern natural language processing, delivering exceptional performance in various tasks. However, their substantial computational and memory requirements present challenges, es…
CPUGPULanguage ModelingLanguage Modelling+2