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Found 18 papers, 6 papers with code
LiPO: Listwise Preference Optimization through Learning-to-Rank
In this work, we formulate the LM alignment as a listwise ranking problem and describe the Listwise Preference Optimization (LiPO) framework, where the policy can potentially learn more effectively from a ranked list of plausible responses given the prompt.
TopCoW: Benchmarking Topology-Aware Anatomical Segmentation of the Circle of Willis (CoW) for CTA and MRA
The TopCoW dataset was the first public dataset with voxel-level annotations for thirteen possible CoW vessel components, enabled by virtual-reality (VR) technology.
Video Summarization: Towards Entity-Aware Captions
We also release a large-scale dataset, VIEWS (VIdeo NEWS), to support research on this task.
On What Basis? Predicting Text Preference Via Structured Comparative Reasoning
Comparative reasoning plays a crucial role in text preference prediction; however, large language models (LLMs) often demonstrate inconsistencies in their reasoning.
Explanation-aware Soft Ensemble Empowers Large Language Model In-context Learning
To fully unleash the power of explanations, we propose EASE, an Explanation-Aware Soft Ensemble framework to empower in-context learning with LLMs.
Video Timeline Modeling For News Story Understanding
In this paper, we present a novel problem, namely video timeline modeling.
Statistical Rejection Sampling Improves Preference Optimization
DPO's lack of a reward model constrains its ability to sample preference pairs from the optimal policy, and SLiC is restricted to sampling preference pairs only from the SFT policy.
Large Language Models are Effective Text Rankers with Pairwise Ranking Prompting
Ranking documents using Large Language Models (LLMs) by directly feeding the query and candidate documents into the prompt is an interesting and practical problem.
Do Not Blindly Imitate the Teacher: Using Perturbed Loss for Knowledge Distillation
In this work, we argue that such a learning objective is sub-optimal because there exists a discrepancy between the teacher's output distribution and the ground truth label distribution.
"Why is this misleading?": Detecting News Headline Hallucinations with Explanations
With the growing need for news headline generation, we argue that the hallucination issue, namely the generated headlines being not supported by the original news stories, is a critical challenge for the deployment of this feature in web-scale systems Meanwhile, due to the infrequency of hallucination cases and the requirement of careful reading for raters to reach the correct consensus, it is difficult to acquire a large dataset for training a model to detect such hallucinations through human curation.
All Birds with One Stone: Multi-task Text Classification for Efficient Inference with One Forward Pass
Multi-Task Learning (MTL) models have shown their robustness, effectiveness, and efficiency for transferring learned knowledge across tasks.
Multi-head or Single-head? An Empirical Comparison for Transformer Training
Multi-head attention plays a crucial role in the recent success of Transformer models, which leads to consistent performance improvements over conventional attention in various applications.
NewsEmbed: Modeling News through Pre-trained Document Representations
Effectively modeling text-rich fresh content such as news articles at document-level is a challenging problem.
Training ELECTRA Augmented with Multi-word Selection
In this study, we present a new text encoder pre-training method that improves ELECTRA based on multi-task learning.
Generating Representative Headlines for News Stories
In this work, we study the problem of generating representative headlines for news stories.
Automated Phrase Mining from Massive Text Corpora
As one of the fundamental tasks in text analysis, phrase mining aims at extracting quality phrases from a text corpus.
Meta-Path Guided Embedding for Similarity Search in Large-Scale Heterogeneous Information Networks
It models vertices as low-dimensional vectors to explore network structure-embedded similarity.
Image Retrieval based on Bag-of-Words model
This article gives a survey for bag-of-words (BoW) or bag-of-features model in image retrieval system.
