Papers with dual approach
LEGENT: Open Platform for Embodied Agents (2024.acl-demos)
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Zhili Cheng, Zhitong Wang, Jinyi Hu, Shengding Hu, An Liu, Yuge Tu, Pengkai Li, Lei Shi, Zhiyuan Liu, Maosong Sun
| Challenge: | Existing integrations of large language models and large multimodal models are limited . Existing platforms for developing embodied agents are limited and limited based on open-source software. |
| Approach: | They propose an open platform for developing embodied agents using LLMs and LMMs. |
| Outcome: | The proposed platform surpasses GPT-4V in embodied tasks with its model training on LEGENT data. |
Advancing Sequential Numerical Prediction in Autoregressive Models (2025.acl-short)
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| Challenge: | Autoregressive models are the de facto choice for sequence generation tasks, but standard approaches treat digits as independent tokens and apply cross-entropy loss, overlooking the coherent structure of numerical sequences. |
| Approach: | They propose a novel approach to entropy loss by extending the Earth Mover’s Distance to preserve ordinal relationships between numerical values and sequence-level to penalize the overall discrepancy between predicted and actual sequences. |
| Outcome: | Extensive experiments show that NTIL improves numerical prediction and integrates effectively with LLMs/MLLMs. |
Explore the Reasoning Capability of LLMs in the Chess Testbed (2025.naacl-short)
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| Challenge: | a recent study shows that large language models struggle with long-term, complex reasoning tasks. |
| Approach: | They propose to integrate annotated strategy and tactic into large language models to improve reasoning capability. |
| Outcome: | The proposed model performs better than GPT, Claude, and Gemini models . it integrates annotated strategy and tactic into the model . |
iQUEST: An Iterative Question-Guided Framework for Knowledge Base Question Answering (2025.acl-long)
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| Challenge: | Large language models suffer from factual inaccuracies in knowledge-intensive domains. |
| Approach: | They propose a question-guided KBQA framework that iteratively decomposes complex queries into simpler sub-questions and integrates a Graph Neural Network (GNN) to look ahead and incorporate 2-hop neighbor information at each reasoning step. |
| Outcome: | The proposed framework improves on four benchmark datasets and four LLMs. |
Raccoon: Prompt Extraction Benchmark of LLM-Integrated Applications (2024.findings-acl)
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| Challenge: | Recent advances in Large Language Models (LLMs) have markedly shifted the landscape of AI, enabling these models to tackle complex, real-world tasks through natural language instructions. |
| Approach: | They propose a benchmark which evaluates a model's susceptibility to prompt extraction attacks by employing a dual approach to evaluate the effectiveness of existing defenses and the resilience of the models. |
| Outcome: | The proposed benchmark assesses models under both defenseless and defended scenarios, employing a dual approach to evaluate the effectiveness of existing defenses and the resilience of the models. |
Where Are We? Evaluating LLM Performance on African Languages (2025.acl-long)
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Ife Adebara, Hawau Olamide Toyin, Nahom Tesfu Ghebremichael, AbdelRahim A. Elmadany, Muhammad Abdul-Mageed
| Challenge: | African languages are underrepresented in NLP due to policies that favor foreign languages and create data inequities. |
| Approach: | They integrate theoretical insights on Africa’s language landscape with an empirical evaluation using Sahara datasets. |
| Outcome: | The proposed model improves on a benchmark curated from large-scale, publicly accessible datasets capturing the continent's linguistic diversity. |