6 papers
TRUEBench: Can LLM Response Meet Real-world Constraints as Productivity Assistant?
Jiho Park, Jongyoon Song, Minjin Choi +3
Large language models (LLMs) are increasingly integral as productivity assistants, but existing benchmarks fall short in rigorously evaluating their real-world instruction-followin…
GRAM: Generative Recommendation via Semantic-aware Multi-granular Late Fusion
Sunkyung Lee, Minjin Choi, Eunseong Choi +2
Generative recommendation is an emerging paradigm that leverages the extensive knowledge of large language models by formulating recommendations into a text-to-text generation task…
DIFF: Dual Side-Information Filtering and Fusion for Sequential Recommendation
Hye-young Kim, Minjin Choi, Sunkyung Lee +2
Side-information Integrated Sequential Recommendation (SISR) benefits from auxiliary item information to infer hidden user preferences, which is particularly effective for sparse i…
Linear Item-Item Model with Neural Knowledge for Session-based Recommendation
Minjin Choi, Sunkyung Lee, Seongmin Park +1
Session-based recommendation (SBR) aims to predict users' subsequent actions by modeling short-term interactions within sessions. Existing neural models primarily focus on capturin…
From Reading to Compressing: Exploring the Multi-document Reader for Prompt Compression
Eunseong Choi, Sunkyung Lee, Minjin Choi +2
Large language models (LLMs) have achieved significant performance gains using advanced prompting techniques over various tasks. However, the increasing length of prompts leads to…
Temporal Linear Item-Item Model for Sequential Recommendation
Seongmin Park, Mincheol Yoon, Minjin Choi +1
In sequential recommendation (SR), neural models have been actively explored due to their remarkable performance, but they suffer from inefficiency inherent to their complexity. On…