PLACES: Prompting Language Models for Social Conversation Synthesis (2023.findings-eacl)
Copied to clipboard
Maximillian Chen, Alexandros Papangelis, Chenyang Tao, Seokhwan Kim, Andy Rosenbaum, Yang Liu, Zhou Yu, Dilek Hakkani-Tur
| Challenge: | Currently, collecting high quality conversational data is expensive and infeasible for many applications . a promising direction is to generate synthetic dialogues by prompting large language models . |
| Approach: | They propose to use expert-written conversations as in-context examples to generate synthetic dialogues by prompting large language models. |
| Outcome: | The proposed approach is generalizable to multi-party conversations, compared to human-collected conversations. |
Similar Papers
Evaluating Large Language Models as Generative User Simulators for Conversational Recommendation (2024.naacl-long)
Copied to clipboard
| Challenge: | Large language models show promise in simulating human-like behavior, raising the question of their ability to represent a diverse population of users. |
| Approach: | They propose a protocol to evaluate the degree to which language models can accurately emulate human behavior in conversational recommendation systems. |
| Outcome: | The proposed protocol evaluates five tasks to reveal deviations of language models from human behavior and offers insights on how to reduce deviations with model selection and prompting strategies. |
Synthetic Data in the Era of Large Language Models (2025.acl-tutorials)
Copied to clipboard
| Challenge: | 'synthetic data' is a data generated with the assistance of large language models to make dataset construction faster and cheaper. |
| Approach: | This tutorial seeks to build a shared understanding of recent progress in synthetic data generation from NLP and related fields by grouping and describing major methods, applications, and open problems. |
| Outcome: | This tutorial will describe methods, applications, and open problems that have been developed and are being used to improve the quality and efficiency of synthetic data generation. |
AUGUST: an Automatic Generation Understudy for Synthesizing Conversational Recommendation Datasets (2023.findings-acl)
Copied to clipboard
| Challenge: | Existing work on conversational recommendation systems lacks high-quality data . existing datasets lack large-scale and high-level data based on human annotators . |
| Approach: | They propose an automatic dataset synthesis approach that generates large-scale recommendation dialogues using structured graphs based on user-item information from the real world. |
| Outcome: | The proposed approach can generate large-scale and high-quality recommendation dialogues . it exploits user preferences, knowledge graphs, and conversation ability from existing datasets based on real-world data . |
Unleashing the Power of Large Language Models in Zero-shot Relation Extraction via Self-Prompting (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Existing methods for zero-shot Relation Extraction (RE) lack detailed, context-specific prompts for understanding various sentences and relations. |
| Approach: | They propose a framework that uses a three-stage diversity approach to prompt LLMs by generating multiple synthetic samples that encapsulate specific relations from scratch. |
| Outcome: | The proposed framework outperforms existing LLM-based zero-shot RE methods on benchmark datasets and shows that it produces high-quality synthetic data that enhances performance. |
Plug-and-Play Conversational Models (2020.findings-emnlp)
Copied to clipboard
| Challenge: | Large conversational models that generate coherent and fluent responses often require large dialogue datasets. |
| Approach: | They propose and evaluate plug-and-play methods for controllable response generation . they demonstrate a high degree of control over the generated conversational responses . |
| Outcome: | The proposed method does not require further computation at decoding time and does not need fine-tuning of a large language model. |
PSYDIAL: Personality-based Synthetic Dialogue Generation Using Large Language Models (2024.lrec-main)
Copied to clipboard
| Challenge: | a new pipeline for personality-based synthetic dialogues is being developed in Korea . a dataset curated by large language models is needed to generate human-like dialogues . |
| Approach: | They propose a personality-based synthetic dialogue data pipeline to elicit responses from large language models via prompting. |
| Outcome: | The proposed pipeline generates human-like dialogues considering real-world scenarios when users engage with chatbots. |
Faithful Persona-based Conversational Dataset Generation with Large Language Models (2024.findings-acl)
Copied to clipboard
| Challenge: | Existing datasets for training conversational AI models do not sufficiently model their users. |
| Approach: | They propose a generator-critic architecture framework to expand the initial dataset while improving the quality of its conversations. |
| Outcome: | The proposed framework expands the initial dataset while improving the quality of its conversations. |
A Comparative Analysis of Conversational Large Language Models in Knowledge-Based Text Generation (2024.eacl-short)
Copied to clipboard
| Challenge: | Generating natural language text from graph-structured data is essential for conversational information seeking. |
| Approach: | They conduct an empirical analysis of conversational large language models in generating natural language text from semantic triples using a WebNLG dataset. |
| Outcome: | The proposed models improve their ability to generate natural language text from semantic triples using few-shot prompting, post-processing, and efficient fine-tuning techniques. |
Contextual Dynamic Prompting for Response Generation in Task-oriented Dialog Systems (2023.eacl-main)
Copied to clipboard
| Challenge: | Existing studies show that large pre-trained language models can be adapted to task-oriented dialog systems. |
| Approach: | They propose to use contextual dynamic prompting to generate prompts in dialogs . they propose to distill useful prompting signals from dialog contexts based on contextual dynamic . |
| Outcome: | The proposed approach improves response generation by 3 points and 17 points when dialog states are incorporated. |
Controllable Mixed-Initiative Dialogue Generation through Prompting (2023.acl-short)
Copied to clipboard
| Challenge: | Mixed initiative dialogue systems allow all interacting agents to initiate actions to control the interaction. |
| Approach: | They propose to prompt large language models as a drop-in replacement for fine-tuning on conditional generation. |
| Outcome: | The proposed prompts improve fine-tuning and ground truth responses . the results show that generated responses are high . |