8 citations · 32 across the 36 of their papers we have counts for
11 papers · 1 filter
Structured List-Grounded Question Answering
Mujeen Sung, Song Feng, James Gung +3
Document-grounded dialogue systems aim to answer user queries by leveraging external information. Previous studies have mainly focused on handling free-form documents, often overlo…
DFlow: Diverse Dialogue Flow Simulation with Large Language Models
Wanyu Du, Song Feng, James Gung +4
Developing language model-based dialogue agents requires effective data to train models that can follow specific task logic. However, most existing data simulation methods focus on…
FineSurE: Fine-grained Summarization Evaluation using LLMs
Hwanjun Song, Hang Su, Igor Shalyminov +2
Automated evaluation is crucial for streamlining text summarization benchmarking and model development, given the costly and time-consuming nature of human evaluation. Traditional…
CERET: Cost-Effective Extrinsic Refinement for Text Generation
Jason Cai, Hang Su, Monica Sunkara +2
Large Language Models (LLMs) are powerful models for generation tasks, but they may not generate good quality outputs in their first attempt. Apart from model fine-tuning, existing…
TofuEval: Evaluating Hallucinations of LLMs on Topic-Focused Dialogue Summarization
Liyan Tang, Igor Shalyminov, Amy Wing-mei Wong +11
Single document news summarization has seen substantial progress on faithfulness in recent years, driven by research on the evaluation of factual consistency, or hallucinations. We…
Can Your Model Tell a Negation from an Implicature? Unravelling Challenges With Intent Encoders
Yuwei Zhang, Siffi Singh, Sailik Sengupta +4
Conversational systems often rely on embedding models for intent classification and intent clustering tasks. The advent of Large Language Models (LLMs), which enable instructional…
Semi-Supervised Dialogue Abstractive Summarization via High-Quality Pseudolabel Selection
Jianfeng He, Hang Su, Jason Cai +3
Semi-supervised dialogue summarization (SSDS) leverages model-generated summaries to reduce reliance on human-labeled data and improve the performance of summarization models. Whil…
FLAP: Flow-Adhering Planning with Constrained Decoding in LLMs
Shamik Roy, Sailik Sengupta, Daniele Bonadiman +2
Planning is a crucial task for agents in task oriented dialogs (TODs). Human agents typically resolve user issues by following predefined workflows, decomposing workflow steps into…
MAGID: An Automated Pipeline for Generating Synthetic Multi-modal Datasets
Hossein Aboutalebi, Hwanjun Song, Yusheng Xie +7
Development of multimodal interactive systems is hindered by the lack of rich, multimodal (text, images) conversational data, which is needed in large quantities for LLMs. Previous…
Eliciting Better Multilingual Structured Reasoning from LLMs through Code
Bryan Li, Tamer Alkhouli, Daniele Bonadiman +2
The development of large language models (LLM) has shown progress on reasoning, though studies have largely considered either English or simple reasoning tasks. To address this, we…
DeAL: Decoding-time Alignment for Large Language Models
James Y. Huang, Sailik Sengupta, Daniele Bonadiman +6
Large Language Models (LLMs) are nowadays expected to generate content aligned with human preferences. Current work focuses on alignment at model training time, through techniques…