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Large Language Model

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Showing 3401–3425 of 6097 papers

TitleStatusHype
AspirinSum: an Aspect-based utility-preserved de-identification Summarization framework—0
How Many Parameters Does it Take to Change a Light Bulb? Evaluating Performance in Self-Play of Conversational Games as a Function of Model Characteristics—0
Exploring Spatial Representations in the Historical Lake District Texts with LLM-based Relation Extraction—0
CityBench: Evaluating the Capabilities of Large Language Models for Urban TasksCode1
LiveMind: Low-latency Large Language Models with Simultaneous InferenceCode1
Ranking LLMs by compression—0
SynDARin: Synthesising Datasets for Automated Reasoning in Low-Resource Languages—0
ReaL: Efficient RLHF Training of Large Language Models with Parameter Reallocation—0
APEER: Automatic Prompt Engineering Enhances Large Language Model Reranking—0
Detecting hallucinations in large language models using semantic entropyCode3
Transferable speech-to-text large language model alignment module—0
Investigating Low-Cost LLM Annotation for~Spoken Dialogue Understanding Datasets—0
Large Language Models are Biased Because They Are Large Language Models—0
BiLD: Bi-directional Logits Difference Loss for Large Language Model DistillationCode1
LIT: Large Language Model Driven Intention Tracking for Proactive Human-Robot Collaboration -- A Robot Sous-Chef Application—0
Enhancing Travel Choice Modeling with Large Language Models: A Prompt-Learning Approach—0
On AI-Inspired UI-DesignCode1
VELO: A Vector Database-Assisted Cloud-Edge Collaborative LLM QoS Optimization Framework—0
SituationalLLM: Proactive language models with scene awareness for dynamic, contextual task guidanceCode0
Block-level Text Spotting with LLMs—0
APPL: A Prompt Programming Language for Harmonious Integration of Programs and Large Language Model PromptsCode3
In-Context Former: Lightning-fast Compressing Context for Large Language Model—0
From Single Agent to Multi-Agent: Improving Traffic Signal Control—0
GPT Czech Poet: Generation of Czech Poetic Strophes with Language Models—0
Detecting Errors through Ensembling Prompts (DEEP): An End-to-End LLM Framework for Detecting Factual ErrorsCode0
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