Papers with large-language models
Scaling Intent Understanding: A Framework for Classification with Clarification using Lightweight LLMs (2026.eacl-industry)
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| Challenge: | Proprietary large-language models (LLMs) assign intents to user utterances without addressing ambiguity. |
| Approach: | They propose a domain-agnostic framework that equips open-source LLMs with the ability to perform intent classification and generate clarification questions in case of ambiguity. |
| Outcome: | The proposed framework performs intent classification and generates clarification questions in case of ambiguity. |
Chart-based Reasoning: Transferring Capabilities from LLMs to VLMs (2024.findings-naacl)
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Victor Carbune, Hassan Mansoor, Fangyu Liu, Rahul Aralikatte, Gilles Baechler, Jindong Chen, Abhanshu Sharma
| Challenge: | Visual language models (VLMs) are achieving increasingly strong performance on multimodal tasks. |
| Approach: | They propose to transfer reasoning capabilities from large-language models to VLMs by constructing a 20x larger dataset and a larger dataset to improve general reasoning capabilities. |
| Outcome: | The proposed model outperforms larger models without an upstream OCR system while keeping inference time constant. |
Natural Language-based State Representation in Deep Reinforcement Learning (2024.findings-naacl)
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| Challenge: | a new method for learning policies from images is proposed to reduce image-based observations' complexity and improve interpretability. |
| Approach: | They propose a method that compresses images into a natural language form for state representation. |
| Outcome: | The proposed method allows better interpretability and leverages processing capabilities of large-language models. |
Forecasting Future International Events: A Reliable Dataset for Text-Based Event Modeling (2024.findings-emnlp)
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| Challenge: | Existing approaches for text-based event prediction are limited in quality due to dynamic nature of international relations and conflicting economic dynamics. |
| Approach: | They propose a novel dataset that leverages the advanced reasoning capabilities of large-language models to address these limitations. |
| Outcome: | The proposed dataset features high-quality scoring labels generated through advanced prompt modeling and rigorously validated by domain experts in political science. |
Prompting Scientific Names for Zero-Shot Species Recognition (2023.emnlp-main)
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| Challenge: | We use visionlanguage models (VLMs) to recognize images of common objects in a zero-shot fashion, but it is underexplored how to use CLIP for zero- shot species recognition of highly specialized concepts. |
| Approach: | They propose a method to translate scientific names to common English names and use them in prompts to improve their performance. |
| Outcome: | The proposed method performs poorly for species recognition with prompts that use scientific names, e.g., “a photo of Lepus Timidus” (which is a scientific name in Latin) and additionally use them in the prompts. |
Decomposition for Enhancing Attention: Improving LLM-based Text-to-SQL through Workflow Paradigm (2024.findings-acl)
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Yuanzhen Xie, Xinzhou Jin, Tao Xie, Matrixmxlin Matrixmxlin, Liang Chen, Chenyun Yu, Cheng Lei, Chengxiang Zhuo, Bo Hu, Zang Li
| Challenge: | In-context learning of large-language models has achieved remarkable success in the field of natural language processing . however, the single-step chain-of-thought prompting approach faces challenges such as attention diffusion and inadequate performance in complex tasks like text-to-SQL. |
| Approach: | They propose a workflow paradigm method to enhance the attention and problem-solving scope of large-language models through decomposition. |
| Outcome: | The proposed method outperforms existing methods on three datasets and improves the upper limit of LLM-based approaches. |
Vision-Flan: Scaling Human-Labeled Tasks in Visual Instruction Tuning (2024.findings-acl)
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Zhiyang Xu, Chao Feng, Rulin Shao, Trevor Ashby, Ying Shen, Di Jin, Yu Cheng, Qifan Wang, Lifu Huang
| Challenge: | Recent vision-language models (VLMs) have shown impressive capabilities as general visual assistants, but there are two challenges to their performance: (1) lacking task diversity in pretraining and visual instruction tuning; (2) annotation error and bias in GPT-4 synthesized instruction tuning data. |
| Approach: | They propose a two-stage instruction tuning framework that fine tunes VLMs firstly and further tuned on GPT-4 synthesized data. |
| Outcome: | The proposed framework outperforms the traditional single-stage visual instruction tuning framework and achieves state-of-the-art performance across a wide range of multi-modal evaluation benchmarks. |