Search Results for author:
Found 25 papers, 18 papers with code
LLMs are Biased Evaluators But Not Biased for Retrieval Augmented Generation
Recent studies have demonstrated that large language models (LLMs) exhibit significant biases in evaluation tasks, particularly in preferentially rating and favoring self-generated content.
PairDistill: Pairwise Relevance Distillation for Dense Retrieval
Effective information retrieval (IR) from vast datasets relies on advanced techniques to extract relevant information in response to queries.
FactAlign: Long-form Factuality Alignment of Large Language Models
In this paper, we address this gap by proposing FactAlign, a novel alignment framework designed to enhance the factuality of LLMs' long-form responses while maintaining their helpfulness.
Investigating Decoder-only Large Language Models for Speech-to-text Translation
Large language models (LLMs), known for their exceptional reasoning capabilities, generalizability, and fluency across diverse domains, present a promising avenue for enhancing speech-related tasks.
Two Tales of Persona in LLMs: A Survey of Role-Playing and Personalization
The concept of persona, originally adopted in dialogue literature, has re-surged as a promising framework for tailoring large language models (LLMs) to specific context (e. g., personalized search, LLM-as-a-judge).
Editing the Mind of Giants: An In-Depth Exploration of Pitfalls of Knowledge Editing in Large Language Models
Knowledge editing is a rising technique for efficiently updating factual knowledge in large language models (LLMs) with minimal alteration of parameters.
A Survey of Generative Information Retrieval
By examining state-of-the-art GR techniques and their applications, this survey aims to provide a foundational understanding of GR and inspire further innovations in this transformative approach to information retrieval.
InstUPR : Instruction-based Unsupervised Passage Reranking with Large Language Models
This paper introduces InstUPR, an unsupervised passage reranking method based on large language models (LLMs).
Unsupervised Multilingual Dense Retrieval via Generative Pseudo Labeling
Dense retrieval methods have demonstrated promising performance in multilingual information retrieval, where queries and documents can be in different languages.
CONVERSER: Few-Shot Conversational Dense Retrieval with Synthetic Data Generation
Conversational search provides a natural interface for information retrieval (IR).
Open-Domain Conversational Question Answering with Historical Answers
Open-domain conversational question answering can be viewed as two tasks: passage retrieval and conversational question answering, where the former relies on selecting candidate passages from a large corpus and the latter requires better understanding of a question with contexts to predict the answers.
Controllable User Dialogue Act Augmentation for Dialogue State Tracking
Prior work has demonstrated that data augmentation is useful for improving dialogue state tracking.
PLM-ICD: Automatic ICD Coding with Pretrained Language Models
Prior work has shown that pretrained language models underperformed on this task with the regular finetuning scheme.
Miutsu: NTU's TaskBot for the Alexa Prize
This paper introduces Miutsu, National Taiwan University's Alexa Prize TaskBot, which is designed to assist users in completing tasks requiring multiple steps and decisions in two different domains -- home improvement and cooking.
Islander: A Real-Time News Monitoring and Analysis System
With thousands of news articles from hundreds of sources distributed and shared every day, news consumption and information acquisition have been increasingly difficult for readers.
Modeling Diagnostic Label Correlation for Automatic ICD Coding
To address this problem, we propose a two-stage framework to improve automatic ICD coding by capturing the label correlation.
Beyond Domain APIs: Task-oriented Conversational Modeling with Unstructured Knowledge Access Track in DSTC9
This challenge track aims to expand the coverage of task-oriented dialogue systems by incorporating external unstructured knowledge sources.
Overview of the Ninth Dialog System Technology Challenge: DSTC9
Interactive evaluation of dialog, and 4.
Adapting Pretrained Transformer to Lattices for Spoken Language Understanding
It is shown that encoding lattices as opposed to 1-best results generated by automatic speech recognizer (ASR) boosts the performance of spoken language understanding (SLU).
Learning Spoken Language Representations with Neural Lattice Language Modeling
Pre-trained language models have achieved huge improvement on many NLP tasks.
Towards Unsupervised Language Understanding and Generation by Joint Dual Learning
The prior work is the first attempt that utilized the duality between NLU and NLG to improve the performance via a dual supervised learning framework.
Learning ASR-Robust Contextualized Embeddings for Spoken Language Understanding
Employing pre-trained language models (LM) to extract contextualized word representations has achieved state-of-the-art performance on various NLP tasks.
Dual Supervised Learning for Natural Language Understanding and Generation
Natural language understanding (NLU) and natural language generation (NLG) are both critical research topics in the NLP field.
Learning Multi-Level Information for Dialogue Response Selection by Highway Recurrent Transformer
With the increasing research interest in dialogue response generation, there is an emerging branch formulating this task as selecting next sentences, where given the partial dialogue contexts, the goal is to determine the most probable next sentence.
RAP-Net: Recurrent Attention Pooling Networks for Dialogue Response Selection
The response selection has been an emerging research topic due to the growing interest in dialogue modeling, where the goal of the task is to select an appropriate response for continuing dialogues.
