Papers with in-context learning abilities
Generative Pre-trained Speech Language Model with Efficient Hierarchical Transformer (2024.acl-long)
Copied to clipboard
| Challenge: | Experimental results indicate that GPST significantly outperforms the existing speech language models in terms of word error rate, speech quality, and speaker similarity. |
| Approach: | They propose a hierarchical transformer that quantizes audio waveforms into two distinct types of discrete speech representations and integrates them within a transformer architecture. |
| Outcome: | The proposed model outperforms existing speech language models in word error rate, speech quality, and speaker similarity. |
Naive Bayes-based Context Extension for Large Language Models (2024.naacl-long)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) have shown promising in-context learning abilities, but are often impeded by length limitations of transformer architecture. |
| Approach: | They propose a framework to enable existing Large Language Models to perform ICL with an increased number of demonstration examples by significantly expanding their context size. |
| Outcome: | The proposed framework outperforms existing methods in in-context learning with an increased number of demonstration examples while preserving linear efficiency. |
Multilingual Fine-Grained News Headline Hallucination Detection (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Existing models to generate news headlines often suffer from the "hallucination" problem, where the produced headline does not fully align with the source article's content. |
| Approach: | They propose to use a multilingual, fine-grained dataset to detect news headlines in 5 languages using supervised fine-tuning techniques and coarse-to-fine prompting to boost the few-shot detection performance. |
| Outcome: | The proposed methods boost the few-shot hallucination detection performance in terms of the example-F1 metric. |
Retrieval-Augmented Code Generation for Situated Action Generation: A Case Study on Minecraft (2024.findings-emnlp)
Copied to clipboard
| 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. |
MIBench: Evaluating Multimodal Large Language Models over Multiple Images (2024.emnlp-main)
Copied to clipboard
Haowei Liu, Xi Zhang, Haiyang Xu, Yaya Shi, Chaoya Jiang, Ming Yan, Ji Zhang, Fei Huang, Chunfeng Yuan, Bing Li, Weiming Hu
| Challenge: | Existing benchmarks and MLLMs focus on single-image input scenarios, leaving performance of ML models when handling multiple images underexplored. |
| Approach: | They propose a benchmark to evaluate fine-grained abilities of multimodal large language models in multi-image scenarios. |
| Outcome: | The proposed benchmark categorizes the multi-image abilities into three scenarios: MII, MKS and MIC. |