Challenge: In the Minecraft Collaborative Building Task, two players collaborate to build a building using 3D blocks.
Approach: They propose to use large language models to model the Builder's sequence of actions in the Minecraft Collaborative Building Task.
Outcome: The proposed methods significantly improve performance over baseline methods and provide detailed analysis for future work.

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Efficient Integration of External Knowledge to LLM-based World Models via Retrieval-Augmented Generation and Reinforcement Learning (2025.findings-emnlp)

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Challenge: Existing attempts to enhance LLM-based world models through prompting or fine-tuning approaches are either requiring human knowledge or computationally extensive.
Approach: They propose a framework that leverages retrieval-augmented generation to integrate external knowledge to LLM-based world models.
Outcome: The proposed framework outperforms baseline models and exhibits strong generalizability.
Nebula: A discourse aware Minecraft Builder (2024.findings-emnlp)

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Challenge: Recent work has shown that at least some context is needed to understand and carry out conversationally given instructions.
Approach: They propose to incorporate prior discourse and nonlinguistic contexts of a conversation situated in a nonlinguistic environment into an LLM model to improve the "language to action" component of collaborative tasks.
Outcome: The proposed model doubles the baseline on the task of Jayannavar et al. (2020) and can construct shapes and understand location descriptions using a synthetic dataset.
Language Models of Code are Few-Shot Commonsense Learners (2022.emnlp-main)

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Challenge: Existing approaches to generate graphs using pre-trained language models hinder their ability to generate them correctly.
Approach: They propose to frame structured commonsense reasoning tasks as code generation tasks instead of serializing the output graph as a flat list of nodes and edges.
Outcome: The proposed approach outperforms natural-language LMs in three natural language tasks even when the downstream task does not involve source code at all.
Awakening Augmented Generation: Learning to Awaken Internal Knowledge of Large Language Models for Question Answering (2025.coling-main)

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Challenge: Recent studies indicate that Large Language Models model rich knowledge, but it is often not activated and awakened.
Approach: They propose a framework that leverages richer context to enhance question answering . Explicit awakening fine-tunes a context generator to create a synthetic, compressed document that functions as symbolic context.
Outcome: The proposed framework mimics the human ability to answer questions using only thinking and recalling to compensate for knowledge gaps.
In-Context Reinforcement Learning with Retrieval-Augmented Generation for Text-to-SQL (2025.coling-main)

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Challenge: Existing methods of synthetic query generation generate mostly simple queries which might not be sufficiently representative of complex, real world queries.
Approach: They propose to use large language models to fine tune query generation to produce complex queries that practitioners may pose during inference.
Outcome: The proposed framework achieves 15-20% higher recall in database/table retrieval task compared to the existing state-of-the-art models for schema identification and upto 2% higher execution accuracy for SQL generation.
ReACC: A Retrieval-Augmented Code Completion Framework (2022.acl-long)

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Challenge: Recent work has shown that statistical language modeling with transformers can greatly improve the performance in code completion tasks.
Approach: They propose a retrieval-augmented code completion framework that combines a source code retriever and an auto-regressive language model for programming language.
Outcome: The proposed framework achieves state-of-the-art on CodeXGLUE benchmark.
Tagging-Augmented Generation: Assisting Language Models in Finding Intricate Knowledge In Long Contexts (2025.emnlp-industry)

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Challenge: Recent studies into effective context lengths of flagship large language models (LLMs) have revealed major limitations in effective question answering (QA) and reasoning over long and complex contexts for even the largest and most impressive cadre of models.
Approach: They propose a lightweight data augmentation strategy that boosts LLM performance in long-context scenarios without degrading and altering the integrity and composition of retrieved documents.
Outcome: The proposed strategy boosts performance in long-context scenarios without degrading and altering the integrity and composition of retrieved documents.
Enhancing Text-to-SQL Capabilities of Large Language Models: A Study on Prompt Design Strategies (2023.findings-emnlp)

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Challenge: In-context learning (ICL) is a new approach to natural language processing tasks that rely on large language models to make predictions based on context . recent studies have shown that neural symbolic design is the preferred choice for question answering systems because of its limited working memory and unreliable long-term memory.
Approach: They propose to extend in-context learning to question answering tasks that utilize structured knowledge sources and to explore various prompt design strategies for employing LLMs.
Outcome: The proposed approach outperforms the state-of-the-art system by 2.5 points and the best fine-tuned system by 5.1 points on the Spider dataset.
Embedding-Informed Adaptive Retrieval-Augmented Generation of Large Language Models (2025.coling-main)

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Challenge: Retrieval-augmented large language models excel in various NLP tasks but are not always helpful when the knowledge required is absent in the model.
Approach: They propose to determine whether the model is knowledgeable on a query via inspecting the (contextualized) pre-trained token embeddings of LLMs.
Outcome: Experiments show that the proposed approach performs better than previous approaches on various benchmarks.
Evaluating In-Context Learning of Libraries for Code Generation (2024.naacl-long)

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Challenge: Recent work shows that large proprietary LLMs can learn novel library usage in-context from demonstrations.
Approach: They evaluate large proprietary LLMs to understand library usage in-context . they find they are able to generate code based on library specification presented in-constext - a promising area .
Outcome: The proposed models can learn library usage in-context from demonstrations . the results pave the way for more adaptable and dynamic coding environments.

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