Papers with iterative fashion

5 papers
One Time of Interaction May Not Be Enough: Go Deep with an Interaction-over-Interaction Network for Response Selection in Dialogues (P19-1)

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Challenge: Currently, retrieval-based dialogues are performed in shallow ways . a recent study investigated the problem of context-response matching in open-domain .
Approach: They propose a model that lets utterance-response interaction go deep by stacking interaction blocks.
Outcome: The proposed model outperforms state-of-the-art methods on three benchmark data sets.
FoodTaxo: Generating Food Taxonomies with Large Language Models (2025.acl-industry)

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Challenge: a recent study shows that LLMs are useful for automating taxonomies from a seed taxonomy to a set of known concepts.
Approach: They propose to use Large Language Models for automated taxonomy generation and completion.
Outcome: The proposed approach is based on an open-source LLM (Llama-3).
Answering Open-Domain Questions of Varying Reasoning Steps from Text (2021.emnlp-main)

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Challenge: a new benchmark is developed to answer open-domain questions from text . the system uses a single multi-task transformer model to perform all the necessary subtasks .
Approach: They develop a unified system to answer directly from open-domain questions . they use a single multi-task transformer model to perform all the necessary subtasks .
Outcome: The proposed system can answer open-domain questions on any text collection without prior knowledge of reasoning complexity.
MILIE: Modular & Iterative Multilingual Open Information Extraction (2022.acl-long)

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Challenge: Current OpenIE systems extract all triple slots independently.
Approach: They propose a neural OpenIE system that extracts triple slots iteratively . they propose to use the system to extract easy slots and difficult ones .
Outcome: The proposed system outperforms SOTA systems on multiple languages ranging from Chinese to Arabic.
Reasons to Reject? Aligning Language Models with Judgments (2024.findings-acl)

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Challenge: a new framework for aligning large language models with judgments is proposed to help with alignment . a framework that allows for fine-grained inappropriate content detection and correction based on judgments . large language model alignment is critical for making artificial intelligence a reliable ally for humanity .
Approach: They propose a framework that allows for fine-grained inappropriate content detection and correction based on judgments.
Outcome: The proposed framework beats the 175B DaVinci003 and improves on AlpacaEval using judgments.

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