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Found 12 papers, 4 papers with code
XL$^2$Bench: A Benchmark for Extremely Long Context Understanding with Long-range Dependencies
However, prior benchmarks create datasets that ostensibly cater to long-text comprehension by expanding the input of traditional tasks, which falls short to exhibit the unique characteristics of long-text understanding, including long dependency tasks and longer text length compatible with modern LLMs' context window size.
Text-Video Retrieval via Variational Multi-Modal Hypergraph Networks
Compared to conventional textual retrieval, the main obstacle for text-video retrieval is the semantic gap between the textual nature of queries and the visual richness of video content.
Towards Verifiable Text Generation with Evolving Memory and Self-Reflection
Despite the remarkable ability of large language models (LLMs) in language comprehension and generation, they often suffer from producing factually incorrect information, also known as hallucination.
Explainability for Large Language Models: A Survey
For each paradigm, we summarize the goals and dominant approaches for generating local explanations of individual predictions and global explanations of overall model knowledge.
Answering Ambiguous Questions via Iterative Prompting
To provide feasible answers to an ambiguous question, one approach is to directly predict all valid answers, but this can struggle with balancing relevance and diversity.
Approximated Doubly Robust Search Relevance Estimation
We further instantiate the proposed unbiased relevance estimation framework in Baidu search, with comprehensive practical solutions designed regarding the data pipeline for click behavior tracking and online relevance estimation with an approximated deep neural network.
Pre-trained Language Model based Ranking in Baidu Search
However, it is nontrivial to directly apply these PLM-based rankers to the large-scale web search system due to the following challenging issues:(1) the prohibitively expensive computations of massive neural PLMs, especially for long texts in the web-document, prohibit their deployments in an online ranking system that demands extremely low latency;(2) the discrepancy between existing ranking-agnostic pre-training objectives and the ad-hoc retrieval scenarios that demand comprehensive relevance modeling is another main barrier for improving the online ranking system;(3) a real-world search engine typically involves a committee of ranking components, and thus the compatibility of the individually fine-tuned ranking model is critical for a cooperative ranking system.
Group-wise Contrastive Learning for Neural Dialogue Generation
Neural dialogue response generation has gained much popularity in recent years.
Data Manipulation: Towards Effective Instance Learning for Neural Dialogue Generation via Learning to Augment and Reweight
In this paper, we propose a data manipulation framework to proactively reshape the data distribution towards reliable samples by augmenting and highlighting effective learning samples as well as reducing the effect of inefficient samples simultaneously.
Learning from Easy to Complex: Adaptive Multi-curricula Learning for Neural Dialogue Generation
Current state-of-the-art neural dialogue systems are mainly data-driven and are trained on human-generated responses.
Adaptive Parameterization for Neural Dialogue Generation
For each conversation, the model generates parameters of the encoder-decoder by referring to the input context.
KNPTC: Knowledge and Neural Machine Translation Powered Chinese Pinyin Typo Correction
In contrast to previous work, KNPTC is able to integrate explicit knowledge into NMT for pinyin typo correction, and is able to learn to correct a variety of typos without the guidance of manually selected constraints or languagespecific features.
