Emery Fine


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Selecting Better Samples from Pre-trained LLMs: A Case Study on Question Generation
Xingdi Yuan | Tong Wang | Yen-Hsiang Wang | Emery Fine | Rania Abdelghani | Hélène Sauzéon | Pierre-Yves Oudeyer
Findings of the Association for Computational Linguistics: ACL 2023

Large Language Models (LLMs) have in recent years demonstrated impressive prowess in natural language generation. A common practice to improve generation diversity is to sample multiple outputs from the model. However, partly due to the inaccessibility of LLMs, there lacks a simple and robust way of selecting the best output from these stochastic samples. As a case study framed in the context of question generation, we propose two prompt-based approaches, namely round-trip and prompt-based score, to selecting high-quality questions from a set of LLM-generated candidates. Our method works without the need to modify the underlying model, nor does it rely on human-annotated references — both of which are realistic constraints for real-world deployment of LLMs. With automatic as well as human evaluations, we empirically demonstrate that our approach can effectively select questions of higher qualities than greedy generation.


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Frames: a corpus for adding memory to goal-oriented dialogue systems
Layla El Asri | Hannes Schulz | Shikhar Sharma | Jeremie Zumer | Justin Harris | Emery Fine | Rahul Mehrotra | Kaheer Suleman
Proceedings of the 18th Annual SIGdial Meeting on Discourse and Dialogue

This paper proposes a new dataset, Frames, composed of 1369 human-human dialogues with an average of 15 turns per dialogue. This corpus contains goal-oriented dialogues between users who are given some constraints to book a trip and assistants who search a database to find appropriate trips. The users exhibit complex decision-making behaviour which involve comparing trips, exploring different options, and selecting among the trips that were discussed during the dialogue. To drive research on dialogue systems towards handling such behaviour, we have annotated and released the dataset and we propose in this paper a task called frame tracking. This task consists of keeping track of different semantic frames throughout each dialogue. We propose a rule-based baseline and analyse the frame tracking task through this baseline.