Search Results for author:
Found 19 papers, 7 papers with code
#HowYouTagTweets: Learning User Hashtagging Preferences via Personalized Topic Attention
It is hypothesized that one’s interests in a hashtag are related with what they said before (user history) and the existing posts present the hashtag (hashtag contexts).
Personalized Search-based Query Rewrite System for Conversational AI
Query rewrite (QR) is an emerging component in conversational AI systems, reducing user defect.
StructEval: Benchmarking LLMs' Capabilities to Generate Structural Outputs
As Large Language Models (LLMs) become integral to software development workflows, their ability to generate structured outputs has become critically important.
ZJUKLAB at SemEval-2025 Task 4: Unlearning via Model Merging
This paper presents the ZJUKLAB team's submission for SemEval-2025 Task 4: Unlearning Sensitive Content from Large Language Models.
MEGA-Bench: Scaling Multimodal Evaluation to over 500 Real-World Tasks
We evaluate a wide variety of frontier vision-language models on MEGA-Bench to understand their capabilities across these dimensions.
VLM2Vec: Training Vision-Language Models for Massive Multimodal Embedding Tasks
Our results show that VLM2Vec achieves an absolute average improvement of 10% to 20% over existing multimodal embedding models on both in-distribution and out-of-distribution datasets in MMEB.
Semi-Supervised Reward Modeling via Iterative Self-Training
Reward models (RM) capture the values and preferences of humans and play a central role in Reinforcement Learning with Human Feedback (RLHF) to align pretrained large language models (LLMs).
CKnowEdit: A New Chinese Knowledge Editing Dataset for Linguistics, Facts, and Logic Error Correction in LLMs
To address this gap, we introduce CKnowEdit, the first-ever Chinese knowledge editing dataset designed to correct linguistic, factual, and logical errors in LLMs.
VideoScore: Building Automatic Metrics to Simulate Fine-grained Human Feedback for Video Generation
The main barrier is the lack of large-scale human-annotated dataset.
MMLU-Pro: A More Robust and Challenging Multi-Task Language Understanding Benchmark
In the age of large-scale language models, benchmarks like the Massive Multitask Language Understanding (MMLU) have been pivotal in pushing the boundaries of what AI can achieve in language comprehension and reasoning across diverse domains.
Position-Aware Parameter Efficient Fine-Tuning Approach for Reducing Positional Bias in LLMs
Recent advances in large language models (LLMs) have enhanced their ability to process long input contexts.
RecMind: Large Language Model Powered Agent For Recommendation
While the recommendation system (RS) has advanced significantly through deep learning, current RS approaches usually train and fine-tune models on task-specific datasets, limiting their generalizability to new recommendation tasks and their ability to leverage external knowledge due to model scale and data size constraints.
Knowledge Enhanced Multi-Domain Recommendations in an AI Assistant Application
We propose to unify these approaches: using information from interactions in other domains as well as external knowledge graphs to make predictions in a new domain that would not be possible with either information source alone.
Graph Meets LLM: A Novel Approach to Collaborative Filtering for Robust Conversational Understanding
This paper presents our "Collaborative Query Rewriting" approach, which specifically addresses the task of rewriting new user interactions that have not been previously observed in the user's history.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+9
PALR: Personalization Aware LLMs for Recommendation
Then we adopt a LLM-based ranking model to generate recommended items.
KG-ECO: Knowledge Graph Enhanced Entity Correction for Query Rewriting
Query Rewriting (QR) plays a critical role in large-scale dialogue systems for reducing frictions.
PENTATRON: PErsonalized coNText-Aware Transformer for Retrieval-based cOnversational uNderstanding
Conversational understanding is an integral part of modern intelligent devices.
A Comparative Study on Transformer vs RNN in Speech Applications
Sequence-to-sequence models have been widely used in end-to-end speech processing, for example, automatic speech recognition (ASR), speech translation (ST), and text-to-speech (TTS).
Ranked #17 on
Speech Recognition
on AISHELL-1
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+5
