Papers with GPT-4-Turbo
TinyAgent: Function Calling at the Edge (2024.emnlp-demo)
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Lutfi Erdogan, Nicholas Lee, Siddharth Jha, Sehoon Kim, Ryan Tabrizi, Suhong Moon, Coleman Hooper, Gopala Anumanchipalli, Kurt Keutzer, Amir Gholami
| Challenge: | Recent large language models (LLMs) have enabled the development of advanced agentic systems that can integrate various tools and APIs to fulfill user queries. |
| Approach: | They propose an end-to-end framework for training and deploying task-specific small language model agents capable of function calling for driving agentic systems at the edge. |
| Outcome: | The proposed model outperforms existing models by reducing the input prompt length and quantizing the inference speed. |
Dissecting Human and LLM Preferences (2024.acl-long)
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| Challenge: | a recent study shows that human and Large Language Model preferences are important for model fine-tuning and evaluation. |
| Approach: | They dissect the preferences of human and 32 different Large Language Models to understand their quantitative composition. |
| Outcome: | The proposed model is compared with 32 different large language models using real-world user-model conversations. |
The Good, The Bad, and The Greedy: Evaluation of LLMs Should Not Ignore Non-Determinism (2025.naacl-long)
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| Challenge: | Current evaluations of large language models (LLMs) focus on a single output per example, which limits our understanding of LLM performance variability in real-world applications. |
| Approach: | They explore the performance differences between greedy decoding and sampling and identify benchmarks’ consistency regarding non-determinism and examine unique model behaviors. |
| Outcome: | The proposed model outperforms sampling methods and greedy decoding outperformed other models. |
Generalists vs. Specialists: Evaluating Large Language Models for Urdu (2024.findings-emnlp)
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| Challenge: | Urdu is underrepresented in natural language processing, yet it is underserved. |
| Approach: | They compare general-purpose models with special-purpose ones that have been fine-tuned on specific tasks. |
| Outcome: | The proposed models outperform general-purpose models on seven classification and seven generation tasks. |
NüshuRescue: Reviving the Endangered Nüshu Language with AI (2025.coling-main)
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| Challenge: | Nüshu is a rare syllabic script used by Yao women in china for self-expression . a lack of data makes the reconstruction labor-intensive and costly . |
| Approach: | They propose an AI-driven framework to train large corpora on endangered languages . Nüshu is a rare syllabic script used by Yao women in china for self-expression . |
| Outcome: | NüshuRescue automates evaluation and expands target corpora to accelerate linguistic revitalization. |
ECoK: Emotional Commonsense Knowledge Graph for Mining Emotional Gold (2024.findings-acl)
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Zhunheng Wang, Xiaoyi Liu, Mengting Hu, Rui Ying, Ming Jiang, Jianfeng Wu, Yalan Xie, Hang Gao, Renhong Cheng
| Challenge: | Existing knowledge graphs focus on the representation and reasoning of general factual knowledge, while there are significant deficiencies in the understanding and reasoning for emotional knowledge. |
| Approach: | They propose a commonsense knowledge graph that can be used to represent emotional knowledge by combining theories from psychology, cognitive science, and linguistics. |
| Outcome: | The proposed model surpasses GPT-4-Turbo in the emotion-related tasks. |
HydraRAG: Structured Cross-Source Enhanced Large Language Model Reasoning (2025.emnlp-main)
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| Challenge: | Current RAG system retrieves evidence from knowledge graphs and text documents but has limitations in multi-hop reasoning, multi-entity questions, and source verification. |
| Approach: | They propose a training-free framework that unifies graph topology, document semantics, and source reliability to support deep, faithful reasoning in large language models. |
| Outcome: | The proposed framework outperforms the current hybrid model-based model-driven system by 20.3% and 30.1% on seven benchmark datasets. |